{"689473":{"#nid":"689473","#data":{"type":"event","title":"Optimization Meets Participation: Iterative School Zone Generation with LLMs","body":[{"value":"\u003Ch3\u003ETitle:\u003C\/h3\u003E\u003Cp\u003EOptimization Meets Participation: Iterative School Zone Generation with LLMs\u003C\/p\u003E\u003Ch3\u003EAbstract:\u0026nbsp;\u003C\/h3\u003E\u003Cp\u003EIn U.S. public school systems, geographic boundaries play a central role in shaping students\u2019 assignments and access to opportunity. For example, the San Francisco Unified School District (SFUSD) recently adopted multi-school zones with controlled choice to jointly promote diversity and proximity to assigned schools. Designing such zones is both computationally and socially complex: algorithmic approaches are required to balance competing objectives at scale, yet stakeholders are typically asked to articulate their preferences upfront, before seeing feasible zone maps, limiting their ability to meaningfully influence outcomes. We propose a stakeholder-in-the-loop framework for joint preference elicitation and zone design. Our approach iterates between using optimization to generate zones and collecting participatory feedback as stakeholders react\u0026nbsp;to zones. To enable broad participation, we use large language models (LLMs) to translate between natural language stakeholder input and optimization constraints. To support real-time iteration, we develop faster computational methods for the multi-school zoning problem, using both mathematical programming and sampling-based approaches. Our framework produces zones with substantially improved diversity and proximity metrics relative to existing benchmarks, while also generating individual-level preference representations that can be aggregated using standard social choice methods. Our approach has been used to support preliminary discussions about zone boundaries in SFUSD and are generalizable to other redistricting and participatory planning contexts.\u003C\/p\u003E\u003Ch3\u003EBio:\u0026nbsp;\u003C\/h3\u003E\u003Cp\u003EIrene Lo is an assistant professor in the department of Management Science \u0026amp; Engineering at Stanford University. Her research sits at the intersection of operations research, computer science theory, and economic theory. She designs markets and allocation systems that improve both efficiency and equity, with applications in education, the environment, and the developing world. She leads a Stanford Impact Lab on Equitable Access to Education, co-launched the ACM Conference series on Equity and Access in Algorithms, Mechanisms, and Optimization (EAAMO), and is a William T. Grant Scholar.\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EIn U.S. public school systems, geographic boundaries play a central role in shaping students\u2019 assignments and access to opportunity. For example, the San Francisco Unified School District (SFUSD) recently adopted multi-school zones with controlled choice to jointly promote diversity and proximity to assigned schools. Designing such zones is both computationally and socially complex: algorithmic approaches are required to balance competing objectives at scale, yet stakeholders are typically asked to articulate their preferences upfront, before seeing feasible zone maps, limiting their ability to meaningfully influence outcomes. We propose a stakeholder-in-the-loop framework for joint preference elicitation and zone design. Our approach iterates between using optimization to generate zones and collecting participatory feedback as stakeholders react\u0026nbsp;to zones. To enable broad participation, we use large language models (LLMs) to translate between natural language stakeholder input and optimization constraints. To support real-time iteration, we develop faster computational methods for the multi-school zoning problem, using both mathematical programming and sampling-based approaches. Our framework produces zones with substantially improved diversity and proximity metrics relative to existing benchmarks, while also generating individual-level preference representations that can be aggregated using standard social choice methods. Our approach has been used to support preliminary discussions about zone boundaries in SFUSD and are generalizable to other redistricting and participatory planning contexts.\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"An interactive framework combining optimization and stakeholder feedback to design equitable multi-school zones with improved diversity, proximity, and participatory input."}],"uid":"36458","created_gmt":"2026-04-06 14:26:31","changed_gmt":"2026-04-06 14:41:45","author":"mellis74","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2026-04-10T11:00:00-04:00","event_time_end":"2026-04-10T12:00:00-04:00","event_time_end_last":"2026-04-10T12:00:00-04:00","gmt_time_start":"2026-04-10 15:00:00","gmt_time_end":"2026-04-10 16:00:00","gmt_time_end_last":"2026-04-10 16:00:00","rrule":null,"timezone":"America\/New_York"},"location":"Groseclose 402 Executive Boardroom ","extras":["free_food"],"hg_media":{"679864":{"id":"679864","type":"image","title":"Irene Lo","body":null,"created":"1775486473","gmt_created":"2026-04-06 14:41:13","changed":"1775486473","gmt_changed":"2026-04-06 14:41:13","alt":"Irene Lo","file":{"fid":"264074","name":"irene-lo_profilephoto.jpg","image_path":"\/sites\/default\/files\/2026\/04\/06\/irene-lo_profilephoto.jpg","image_full_path":"http:\/\/hg.gatech.edu\/\/sites\/default\/files\/2026\/04\/06\/irene-lo_profilephoto.jpg","mime":"image\/jpeg","size":11690,"path_740":"http:\/\/hg.gatech.edu\/sites\/default\/files\/styles\/740xx_scale\/public\/2026\/04\/06\/irene-lo_profilephoto.jpg?itok=omfjtvLg"}}},"media_ids":["679864"],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[],"invited_audience":[{"id":"194945","name":"Alumni"},{"id":"78761","name":"Faculty\/Staff"},{"id":"174045","name":"Graduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"689260":{"#nid":"689260","#data":{"type":"event","title":"ISyE Seminar \u2013 Vineet Goyal","body":[{"value":"\u003Ch3\u003E\u003Cstrong\u003ETitle:\u003C\/strong\u003E\u003C\/h3\u003E\u003Cp\u003EDistributionally Robust Newsvendor on a Metric\u003C\/p\u003E\u003Ch3\u003E\u003Cstrong\u003EAbstract:\u003C\/strong\u003E\u003C\/h3\u003E\u003Cp\u003EWe consider a fundamental generalization of the classical newsvendor problem where the seller needs to decide on the inventory of a product jointly for multiple locations on a metric as well as a fulfillment policy to satisfy the uncertain demand that arises sequentially over time after the inventory decisions have been made. To address the distributional-ambiguity, we consider a distributionally robust setting where the decision-maker only knows the mean and variance of the demand, and the goal is to make inventory and fulfillment decisions to minimize the worst-case expected inventory and fulfillment cost (where the expectation is taken over the worst case choice of distribution with given mean and variance).\u0026nbsp;\u003Cbr\u003E\u003Cbr\u003EWe present a significant generalization of the classical result of Scarf (1958) and give a policy with strong theoretical guarantees as well as good practical performance while maintaining the simplicity and interpretability of the solution in Scarf (1958). In particular, our policy first identifies a hierarchical clustering of the locations, and assigns a \u0022virtual-underage cost\u0022 for each cluster. Our inventory solution ensures that for each cluster, the total inventory in the cluster is at least as large as the inventory level suggested by Scarf\u0027s solution for the virtual-underage cost if the cluster was a single point. We present a worst-case performance guarantee for our policy and also demonstrate that the policy performs well in practice. To the best of our knowledge, this is the first algorithm with provable performance guarantees.\u0026nbsp; \u0026nbsp;(This is joint work with Ayoub Foussoul)\u003C\/p\u003E\u003Ch3\u003E\u003Cstrong\u003EBio:\u003C\/strong\u003E\u003C\/h3\u003E\u003Cp\u003EVineet Goyal is a Professor in the Industrial Engineering and Operations Research Department at Columbia University where he joined in 2010. He received his Bachelor\u0027s degree in Computer Science from Indian Institute of Technology, Delhi in 2003 and his Ph.D. in Algorithms, Combinatorics and Optimization (ACO) from Carnegie Mellon University in 2008. Before coming to Columbia, he spent two years as a Postdoctoral Associate at the Operations Research Center at MIT. He is interested in the design of efficient and robust data-driven algorithms for large scale dynamic optimization problems with applications in\u0026nbsp; revenue management and healthcare problems. His research has been continually supported by grants from NSF and industry including NSF CAREER Award in 2014 and faculty research awards from Google, IBM, Adobe and Amazon.\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EWe consider a fundamental generalization of the classical newsvendor problem where the seller needs to decide on the inventory of a product jointly for multiple locations on a metric as well as a fulfillment policy to satisfy the uncertain demand that arises sequentially over time after the inventory decisions have been made. To address the distributional-ambiguity, we consider a distributionally robust setting where the decision-maker only knows the mean and variance of the demand, and the goal is to make inventory and fulfillment decisions to minimize the worst-case expected inventory and fulfillment cost (where the expectation is taken over the worst case choice of distribution with given mean and variance).\u003C\/p\u003E\u003Cp\u003EWe present a significant generalization of the classical result of Scarf (1958) and give a policy with strong theoretical guarantees as well as good practical performance while maintaining the simplicity and interpretability of the solution in Scarf (1958). In particular, our policy first identifies a hierarchical clustering of the locations, and assigns a \u0022virtual-underage cost\u0022 for each cluster. Our inventory solution ensures that for each cluster, the total inventory in the cluster is at least as large as the inventory level suggested by Scarf\u0027s solution for the virtual-underage cost if the cluster was a single point. We present a worst-case performance guarantee for our policy and also demonstrate that the policy performs well in practice. To the best of our knowledge, this is the first algorithm with provable performance guarantees.\u0026nbsp; \u0026nbsp;(This is joint work with Ayoub Foussoul)\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Distributionally Robust Newsvendor on a Metric"}],"uid":"36861","created_gmt":"2026-03-31 15:36:26","changed_gmt":"2026-04-03 14:38:03","author":"adrysdale7","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2026-04-03T11:00:00-04:00","event_time_end":"2026-04-03T12:00:00-04:00","event_time_end_last":"2026-04-03T12:00:00-04:00","gmt_time_start":"2026-04-03 15:00:00","gmt_time_end":"2026-04-03 16:00:00","gmt_time_end_last":"2026-04-03 16:00:00","rrule":null,"timezone":"America\/New_York"},"location":"Groseclose 402","extras":[],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"177814","name":"Postdoc"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"689314":{"#nid":"689314","#data":{"type":"event","title":"ISYE Statistics Seminar - Sankaran Mahadevan","body":[{"value":"\u003Cp\u003EProbabilistic Digital Twins for Diagnosis, Prognosis, and Decision-Making\u003C\/p\u003E\u003Cp\u003EThe digital twin, a virtual representation of a physical system or process, integrates information obtained from sensor data, physics models, as well as operational and inspection\/maintenance\/repair history of the system. As more and more data becomes available, the resulting updated model becomes increasingly accurate in predicting future behavior of the system, and can potentially be used to support several objectives, such as sustainment, mission planning, and operational maneuvers. This presentation will present recent research in digital twin methodologies to support all three objectives, based on several types of computations: current state diagnosis, model updating, future state prognosis, and decision-making. All these computations are affected by uncertainty regarding system properties, operational parameters, usage and environment, as well as uncertainties in data and the prediction models. Therefore the presentation will address decision-making under uncertainty, and the incorporation of modern uncertainty quantification techniques, considering both aleatory and epistemic uncertainty sources. Scaling up the probabilistic digital twin methodology to support real-time decision-making is a challenge, and several strategies that combine recent advances in sensing, computing, data fusion, and machine learning to enable the scale-up will be discussed. Several use cases related to power grid, aircraft, marine vessels, and additive manufacturing will be presented.\u003C\/p\u003E\u003Cp\u003EProfessor Sankaran Mahadevan (Vanderbilt University, Nashville, TN) has more than thirty-five years of research and teaching experience in uncertainty quantification, risk and reliability analysis, machine learning, structural health diagnosis and prognosis, and decision-making under uncertainty. He has applied these methods to a variety of structures, materials and systems in civil, mechanical and aerospace engineering. His research has been extensively funded by NSF, NASA, DOE, DOD, FAA, NIST, as well as GM, Chrysler, GE, Union Pacific, and Mitsubishi, and he has co-authored two textbooks and 350 peer-reviewed journal papers. During the past two decades, he has been at the forefront of academic research on uncertainty quantification and digital twin methodologies.\u003C\/p\u003E\u003Cp\u003EProfessor Mahadevan has served as President of the ASCE Engineering Mechanics Institute, and as chair of several technical committees and prominent conferences in ASCE, ASME, and AIAA. He is currently serving as the Chair of the ASME VVUQ 50 Subcommittee on Advanced Manufacturing. He is a Distinguished Member of ASCE, and Fellow of AIAA, Engineering Mechanics Institute (ASCE), and PHM Society. His awards include ASCE\u2019s Alfredo Ang award for risk analysis and management of civil infrastructure, and the IASSAR Distinguished Research award.\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EThe digital twin, a virtual representation of a physical system or process, integrates information obtained from sensor data, physics models, as well as operational and inspection\/maintenance\/repair history of the system. As more and more data becomes available, the resulting updated model becomes increasingly accurate in predicting future behavior of the system, and can potentially be used to support several objectives, such as sustainment, mission planning, and operational maneuvers. This presentation will present recent research in digital twin methodologies to support all three objectives, based on several types of computations: current state diagnosis, model updating, future state prognosis, and decision-making. All these computations are affected by uncertainty regarding system properties, operational parameters, usage and environment, as well as uncertainties in data and the prediction models. Therefore the presentation will address decision-making under uncertainty, and the incorporation of modern uncertainty quantification techniques, considering both aleatory and epistemic uncertainty sources. Scaling up the probabilistic digital twin methodology to support real-time decision-making is a challenge, and several strategies that combine recent advances in sensing, computing, data fusion, and machine learning to enable the scale-up will be discussed. Several use cases related to power grid, aircraft, marine vessels, and additive manufacturing will be presented.\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Probabilistic Digital Twins for Diagnosis, Prognosis, and Decision-Making"}],"uid":"36868","created_gmt":"2026-04-01 14:29:59","changed_gmt":"2026-04-01 14:33:58","author":"mferrick3","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2026-04-07T11:00:00-04:00","event_time_end":"2026-04-07T12:00:00-04:00","event_time_end_last":"2026-04-07T12:00:00-04:00","gmt_time_start":"2026-04-07 15:00:00","gmt_time_end":"2026-04-07 16:00:00","gmt_time_end_last":"2026-04-07 16:00:00","rrule":null,"timezone":"America\/New_York"},"extras":[],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[],"invited_audience":[{"id":"194945","name":"Alumni"},{"id":"78761","name":"Faculty\/Staff"},{"id":"177814","name":"Postdoc"},{"id":"78771","name":"Public"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"689136":{"#nid":"689136","#data":{"type":"event","title":"CHHS Webinar Series: \u0022Improving Health Cost Transparency in Georgia\u0022","body":[{"value":"\u003Cp\u003EThe \u003Ca href=\u0022https:\/\/apcd.georgia.gov\/\u0022\u003EGeorgia All-Payer Claims Database\u003C\/a\u003E (APCD) was established in 2020 with the mission of improving the access, quality, and cost of healthcare in the state. The APCD Analytics team is based at Georgia Tech and has conducted a series of publicly available analyses on healthcare costs (\u003Ca href=\u0022https:\/\/www.gtri.gatech.edu\/newsroom\/georgia-insurance-claims-database-provides-health-care-cost-comparisons\u0022\u003Erelated news\u003C\/a\u003E). Most recently, the APCD team has released a dashboard comparing costs of care for over 450 procedures at institutions across the state. During the session, we will look at this new tool and discuss plans for further enhancing cost transparency in Georgia.\u003C\/p\u003E\u003Cp\u003EFeaturing \u003Ca href=\u0022https:\/\/research.gatech.edu\/people\/jon-duke\u0022\u003EJon Duke\u003C\/a\u003E, a physician-scientist with a 25-year career spanning clinical practice, academic medicine, and advanced research in health informatics and analytics. Dr. Duke joined Georgia Tech Research Institute (GTRI) in 2016 and has served as director of GTRI\u2019s Health Emerging and Advanced Technologies Division since its inception in 2019. Over the course of his career, Dr. Duke has been awarded over $70M in external funding as a principal investigator and his academic publications have been cited over 6,000 times. Dr. Duke\u0027s work is enabling organizations to better use electronic health data to improve the well-being of patients and populations. His areas of research include decision support, data standards and interoperability, natural language processing, and artificial intelligence with applications spanning medication safety, public health, cost transparency, and clinical research.\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EThe Georgia All-Payer Claims Database (APCD) team recently released a dashboard comparing costs of care for over 450 procedures at institutions across the state. During this session, we will explore their new tool and plans for further enhancing cost transparency in Georgia.\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Georgia residents now have a new way to compare the estimated costs paid for a large variety of health care services in the state."}],"uid":"27233","created_gmt":"2026-03-23 17:59:35","changed_gmt":"2026-03-23 18:46:11","author":"Andy Haleblian","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2026-04-02T12:00:00-04:00","event_time_end":"2026-04-02T13:00:00-04:00","event_time_end_last":"2026-04-02T13:00:00-04:00","gmt_time_start":"2026-04-02 16:00:00","gmt_time_end":"2026-04-02 17:00:00","gmt_time_end_last":"2026-04-02 17:00:00","rrule":null,"timezone":"America\/New_York"},"extras":[],"hg_media":{"679718":{"id":"679718","type":"image","title":"CHHS Webinar Series: \u0022Improving Health Cost Transparency in Georgia\u0022","body":null,"created":"1774291525","gmt_created":"2026-03-23 18:45:25","changed":"1774291525","gmt_changed":"2026-03-23 18:45:25","alt":"CHHS Webinar Series: \u0022Improving Health Cost Transparency in Georgia\u0022","file":{"fid":"263910","name":"20260402_CHHS_LNL.jpg","image_path":"\/sites\/default\/files\/2026\/03\/23\/20260402_CHHS_LNL.jpg","image_full_path":"http:\/\/hg.gatech.edu\/\/sites\/default\/files\/2026\/03\/23\/20260402_CHHS_LNL.jpg","mime":"image\/jpeg","size":161950,"path_740":"http:\/\/hg.gatech.edu\/sites\/default\/files\/styles\/740xx_scale\/public\/2026\/03\/23\/20260402_CHHS_LNL.jpg?itok=FpVWLlwS"}}},"media_ids":["679718"],"related_links":[{"url":"https:\/\/gatech.zoom.us\/webinar\/register\/WN_ZHdh69R2RKStBnQ6FrolAg#\/registration","title":"To attend, please register online via Zoom"}],"groups":[{"id":"1250","name":"Center for Health and Humanitarian Systems (CHHS)"},{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"},{"id":"1243","name":"The Supply Chain and Logistics Institute (SCL)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"194684","name":"Free"},{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"177814","name":"Postdoc"},{"id":"78771","name":"Public"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"688747":{"#nid":"688747","#data":{"type":"event","title":"Generative AI for Global Social Impact: Towards Solving the Deployment Bottleneck","body":[{"value":"\u003Ch3\u003ETitle:\u0026nbsp;\u003Cbr\u003EGenerative AI for Global Social Impact: Towards Solving the Deployment Bottleneck\u003Cbr\u003E\u0026nbsp;\u003C\/h3\u003E\u003Ch3\u003EAbstract:\u0026nbsp;\u003C\/h3\u003E\u003Cp\u003E\u003Cbr\u003EMy team\u2019s work on AI for Social Impact (AI4SI) has spanned two decades, focusing on optimizing limited resources in critical areas like public health, conservation, and public safety. I will present field results from India, where the deployment of restless and collaborative multi-armed bandit (RMAB) algorithms achieved significant improvements in the world\u2019s two largest mobile maternal health programs. I will also highlight ongoing work on network-based HIV prevention in South Africa, modeled as a branching bandit problem. These projects, along with other initiatives across Africa and Asia, expose a critical \u0022deployment bottleneck\u0022 that spans the entire machine learning pipeline. This bottleneck consists of three key hurdles: the observational scarcity gap (data), the policy synthesis gap (learning and modeling), and the human-AI alignment gap (deployment).\u003C\/p\u003E\u003Cdiv\u003E\u0026nbsp;\u003C\/div\u003E\u003Cp\u003EThis talk investigates how Generative AI can address this AI4SI deployment bottleneck through the strategic use of LLM Agents and diffusion models. I will demonstrate how LLM Agents address the alignment gap by integrating expert guidance into algorithmic planning, ensuring resource optimization strategies reflect real-world priorities. Furthermore, I will show how diffusion models mitigate the scarcity and synthesis gaps by generating synthetic social networks and facilitating Transfer RL to utilize data across domains. I will conclude by discussing this path toward a more scalable, human-aligned future for AI for Social Impact.\u003Cbr\u003E\u003Cbr\u003E\u003Cstrong\u003EBio:\u003C\/strong\u003E\u003Cbr\u003EMilind Tambe is the Gordon McKay Professor of Computer Science at Harvard University; concurrently, he is Principal Scientist and Director for \u201cAI for Social Good\u201d at Google Research. Prof. Tambe and his team have developed innovative AI and multi-agent reasoning systems that have been successfully deployed to deliver real-world impact in public health (e.g., maternal and child health), public safety, and wildlife conservation. He is the recipient of the AAAI Award for Artificial Intelligence for the Benefit of Humanity, the AAAI Feigenbaum Prize, the IJCAI John McCarthy Award, the AAAI Robert S. Engelmore Memorial Lecture Award, and the AAMAS ACM\/SIGAI Autonomous Agents Research Award. He is a fellow of AAAI and ACM. His contributions in Operations Research and public safety have also been recognized with the INFORMS Wagner Prize for excellence in Operations Research practice, Military Operations Research Society Rist Prize, the Columbus Fellowship Foundation Homeland security award, and commendations and certificates of appreciation from the US Coast Guard, the Federal Air Marshals Service, and airport police at the city of Los Angeles.\u003Cbr\u003E\u003Cbr\u003E\u0026nbsp;\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EMy team\u2019s work on AI for Social Impact (AI4SI) has spanned two decades, focusing on optimizing limited resources in critical areas like public health, conservation, and public safety. I will present field results from India, where the deployment of restless and collaborative multi-armed bandit (RMAB) algorithms achieved significant improvements in the world\u2019s two largest mobile maternal health programs. I will also highlight ongoing work on network-based HIV prevention in South Africa, modeled as a branching bandit problem. These projects, along with other initiatives across Africa and Asia, expose a critical \u0022deployment bottleneck\u0022 that spans the entire machine learning pipeline. This bottleneck consists of three key hurdles: the observational scarcity gap (data), the policy synthesis gap (learning and modeling), and the human-AI alignment gap (deployment).\u003C\/p\u003E\u003Cp\u003EThis talk investigates how Generative AI can address this AI4SI deployment bottleneck through the strategic use of LLM Agents and diffusion models. I will demonstrate how LLM Agents address the alignment gap by integrating expert guidance into algorithmic planning, ensuring resource optimization strategies reflect real-world priorities. Furthermore, I will show how diffusion models mitigate the scarcity and synthesis gaps by generating synthetic social networks and facilitating Transfer RL to utilize data across domains. I will conclude by discussing this path toward a more scalable, human-aligned future for AI for Social Impact.\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"This talk presents how Generative AI methods, including LLM agents and diffusion models, can address data, modeling, and deployment challenges in AI for Social Impact, enabling scalable and human-aligned solutions in public health and global programs."}],"uid":"36458","created_gmt":"2026-03-05 19:16:53","changed_gmt":"2026-03-11 16:11:59","author":"mellis74","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2026-03-13T11:00:00-04:00","event_time_end":"2026-03-13T12:30:00-04:00","event_time_end_last":"2026-03-13T12:30:00-04:00","gmt_time_start":"2026-03-13 15:00:00","gmt_time_end":"2026-03-13 16:30:00","gmt_time_end_last":"2026-03-13 16:30:00","rrule":null,"timezone":"America\/New_York"},"location":"Groseclose Executive Boardroom- GC 402","extras":["free_food"],"hg_media":{"679532":{"id":"679532","type":"image","title":"Milind Tambe","body":null,"created":"1772738765","gmt_created":"2026-03-05 19:26:05","changed":"1772738765","gmt_changed":"2026-03-05 19:26:05","alt":"Milind Tambe","file":{"fid":"263705","name":"Tambe_Summer2019-23.jpg","image_path":"\/sites\/default\/files\/2026\/03\/05\/Tambe_Summer2019-23.jpg","image_full_path":"http:\/\/hg.gatech.edu\/\/sites\/default\/files\/2026\/03\/05\/Tambe_Summer2019-23.jpg","mime":"image\/jpeg","size":153829,"path_740":"http:\/\/hg.gatech.edu\/sites\/default\/files\/styles\/740xx_scale\/public\/2026\/03\/05\/Tambe_Summer2019-23.jpg?itok=V3A5xODf"}}},"media_ids":["679532"],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1789","name":"Conference\/Symposium"}],"invited_audience":[{"id":"194945","name":"Alumni"},{"id":"78761","name":"Faculty\/Staff"},{"id":"177814","name":"Postdoc"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"688854":{"#nid":"688854","#data":{"type":"event","title":"ISyE Seminar_ Uncertainty Quantification in Engineering: What, Why, and How","body":[{"value":"\u003Ch3\u003E\u003Cstrong\u003ETitle:\u003C\/strong\u003E\u003Cbr\u003E\u003Cstrong\u003EUncertainty Quantification in Engineering: What, Why, and How\u003C\/strong\u003E\u003Cbr\u003EPeter Chien\u003Cbr\u003EProfessor of Statistics\u003Cbr\u003EUniversity of Wisconsin\u2013Madison\u003Cbr\u003E\u003Ca href=\u0022mailto:peter.chien@wisc.edu\u0022\u003Epeter.chien@wisc.edu\u003C\/a\u003E\u003C\/h3\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Ch3\u003EAbstract:\u003C\/h3\u003E\u003Cp\u003EMany manufacturing companies have experienced costly recalls and product failures because uncertainties in design, testing, and manufacturing processes were not adequately quantified. These failures have led to fatal accidents, billions of dollars in lost revenue, and even the collapse of major manufacturing firms. In response, industries such as aerospace, automotive, semiconductor, and medical devices have increasingly adopted \u003Cstrong\u003EUncertainty Quantification (UQ)\u003C\/strong\u003E\u2014a multidisciplinary framework drawing from statistics, applied mathematics, and engineering\u2014to better design, test, and manufacture products under uncertainty.\u003C\/p\u003E\u003Cp\u003EThis talk provides an overview of Uncertainty Quantification, explains why it has become indispensable in modern engineering, and introduces key design of experiment and predictive model methods for rigorously quantifying uncertainty in complex systems.\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Ch3\u003EBio\u0026nbsp;\u003C\/h3\u003E\u003Cp\u003EPeter Chien is a Professor of Statistics and Industrial \u0026amp; Systems Engineering at the University of Wisconsin\u2013Madison and a Fellow of the American Statistical Association. He is the recipient of a National Science Foundation CAREER Award and an IBM Faculty Award. His research has been widely adopted by Fortune 500 companies across industries including aerospace, automotive, semiconductors, electronics, chemical, battery and life sciences.\u003Cbr\u003E\u0026nbsp;\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EMany manufacturing companies have experienced costly recalls and product failures because uncertainties in design, testing, and manufacturing processes were not adequately quantified. These failures have led to fatal accidents, billions of dollars in lost revenue, and even the collapse of major manufacturing firms. In response, industries such as aerospace, automotive, semiconductor, and medical devices have increasingly adopted \u003Cstrong\u003EUncertainty Quantification (UQ)\u003C\/strong\u003E\u2014a multidisciplinary framework drawing from statistics, applied mathematics, and engineering\u2014to better design, test, and manufacture products under uncertainty.\u003C\/p\u003E\u003Cp\u003EThis talk provides an overview of Uncertainty Quantification, explains why it has become indispensable in modern engineering, and introduces key design of experiment and predictive model methods for rigorously quantifying uncertainty in complex systems.\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"This talk introduces Uncertainty Quantification (UQ) as a multidisciplinary framework that helps engineers rigorously measure and manage uncertainty in design, testing, and manufacturing to improve reliability and prevent costly product failures."}],"uid":"36458","created_gmt":"2026-03-11 15:41:32","changed_gmt":"2026-03-11 15:47:28","author":"mellis74","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2026-03-20T11:00:00-04:00","event_time_end":"2026-03-20T12:00:00-04:00","event_time_end_last":"2026-03-20T12:00:00-04:00","gmt_time_start":"2026-03-20 15:00:00","gmt_time_end":"2026-03-20 16:00:00","gmt_time_end_last":"2026-03-20 16:00:00","rrule":null,"timezone":"America\/New_York"},"location":"Groseclose Executive Boardroom GC402","extras":[],"hg_media":{"679578":{"id":"679578","type":"image","title":"Peter Chien","body":null,"created":"1773244006","gmt_created":"2026-03-11 15:46:46","changed":"1773244006","gmt_changed":"2026-03-11 15:46:46","alt":"Peter Chien","file":{"fid":"263754","name":"PIC.jpeg","image_path":"\/sites\/default\/files\/2026\/03\/11\/PIC.jpeg","image_full_path":"http:\/\/hg.gatech.edu\/\/sites\/default\/files\/2026\/03\/11\/PIC.jpeg","mime":"image\/jpeg","size":41100,"path_740":"http:\/\/hg.gatech.edu\/sites\/default\/files\/styles\/740xx_scale\/public\/2026\/03\/11\/PIC.jpeg?itok=j5JbSLcK"}}},"media_ids":["679578"],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"194945","name":"Alumni"},{"id":"78761","name":"Faculty\/Staff"},{"id":"177814","name":"Postdoc"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"688541":{"#nid":"688541","#data":{"type":"event","title":"XR Bytes: Srikanth\u00a0Tindivanam\u00a0Varadharajan (ADC XR Makerspace) ","body":[{"value":"\u003Cp\u003E\u003Cstrong\u003EXR Bytes\u003C\/strong\u003E\u0026nbsp;is a graduate-student-led initiative dedicated to\u0026nbsp;showcasing\u0026nbsp;the trajectory of Extended Reality (XR) research across the Institute. Our goal is to highlight the versatile ways XR technologies are applied across various disciplines and majors, fostering\u0026nbsp;an\u0026nbsp;interdisciplinary community of innovation at Georgia Tech.\u0026nbsp;\u003Cbr\u003E\u0026nbsp;\u003C\/p\u003E\u003Ch2\u003E\u003Cstrong\u003EPresenter: \u003C\/strong\u003ESrikanth\u0026nbsp;Tindivanam\u0026nbsp;Varadharajan (Aerospace Engineering)\u003C\/h2\u003E\u003Cp\u003E\u003Cbr\u003E\u003Ca href=\u0022https:\/\/lightroom.adobe.com\/shares\/c0fab204c0354d2c85328ed299305590\u0022 target=\u0022_blank\u0022\u003EView Event Photos\u003C\/a\u003E\u003Cbr\u003E\u0026nbsp;\u003C\/p\u003E\u003Ch2\u003E\u003Cstrong\u003ESeminar Title:\u0026nbsp;\u003C\/strong\u003E\u003C\/h2\u003E\u003Cp\u003EXR\u0026nbsp;for the Sky: Enhancing UAV and UAM Operations\u0026nbsp;\u003C\/p\u003E\u003Ch2\u003E\u003Cstrong\u003EAffiliation\u003C\/strong\u003E\u003C\/h2\u003E\u003Cp\u003EGeorgia Tech, College of\u0026nbsp;Engineering :\u0026nbsp;DR-CSE-AE, Labs: ASDL\u0026nbsp;and\u0026nbsp; CCG\u0026nbsp;(Aerospace Systems Design Laboratory and Contextual Computing Group)\u0026nbsp;\u003C\/p\u003E\u003Ch2\u003E\u003Cstrong\u003EParticipation \u0026amp;\u0026nbsp;Visibility\u003C\/strong\u003E\u0026nbsp;\u003C\/h2\u003E\u003Cp\u003EWe actively encourage cross-departmental participation and welcome engagement from both internal academic units and external industry partners. Our goal is to foster a robust community of practice around XR at Georgia Tech.\u0026nbsp;\u003C\/p\u003E\u003Ch2\u003E\u003Cstrong\u003ESubscribe \u0026amp; Sign Up\u003C\/strong\u003E\u0026nbsp;\u003C\/h2\u003E\u003Cp\u003ETo join our mailing list and RSVP your attendance for these seminar series, please sign up here: \u003Ca href=\u0022https:\/\/eforms.isye.gatech.edu\/form\/xr-rsvp?event=7047\u0022 id=\u0022OWA22b64c58-63b7-d310-12aa-d03756bd4375\u0022 rel=\u0022noreferrer noopener\u0022 target=\u0022_blank\u0022 title=\u0022https:\/\/eforms.isye.gatech.edu\/form\/xr-rsvp\u0022\u003E\u003Cstrong\u003ERSVP HERE\u003C\/strong\u003E\u003C\/a\u003E\u003Cstrong\u003E. \u003C\/strong\u003E\u003Cem\u003ELunch will be served for those who RSVP.\u003C\/em\u003E\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003E\u003Cstrong\u003EXR Bytes\u003C\/strong\u003E\u0026nbsp;is a graduate-student-led initiative dedicated to\u0026nbsp;showcasing\u0026nbsp;the trajectory of Extended Reality (XR) research across the Institute. Our goal is to highlight the versatile ways XR technologies are applied across various disciplines and majors, fostering\u0026nbsp;an\u0026nbsp;interdisciplinary community of innovation at Georgia Tech.\u0026nbsp;\u003Cbr\u003E\u003Cbr\u003E\u0026nbsp;\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Weekly research seminar series hosted by ADC XR Makerspace at ISyE"}],"uid":"36736","created_gmt":"2026-02-26 15:25:08","changed_gmt":"2026-03-09 17:19:48","author":"ebrown386","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2026-03-06T12:30:00-05:00","event_time_end":"2026-03-06T13:30:00-05:00","event_time_end_last":"2026-03-06T13:30:00-05:00","gmt_time_start":"2026-03-06 17:30:00","gmt_time_end":"2026-03-06 18:30:00","gmt_time_end_last":"2026-03-06 18:30:00","rrule":null,"timezone":"America\/New_York"},"location":"ADC XR Makerspace (ISyE Main 115)","extras":[],"hg_media":{"679446":{"id":"679446","type":"image","title":"XR Bytes - Srikanth Tindivanam Varadharajan","body":null,"created":"1772119517","gmt_created":"2026-02-26 15:25:17","changed":"1772119517","gmt_changed":"2026-02-26 15:25:17","alt":"XR Bytes - Srikanth Tindivanam Varadharajan","file":{"fid":"263610","name":"Srikanth-XR-Headshot.jpg","image_path":"\/sites\/default\/files\/2026\/02\/26\/Srikanth-XR-Headshot.jpg","image_full_path":"http:\/\/hg.gatech.edu\/\/sites\/default\/files\/2026\/02\/26\/Srikanth-XR-Headshot.jpg","mime":"image\/jpeg","size":143716,"path_740":"http:\/\/hg.gatech.edu\/sites\/default\/files\/styles\/740xx_scale\/public\/2026\/02\/26\/Srikanth-XR-Headshot.jpg?itok=xLkYbj6-"}}},"media_ids":["679446"],"related_links":[{"url":"https:\/\/eforms.isye.gatech.edu\/form\/xr-rsvp?event=7047","title":"RSVP"},{"url":"https:\/\/lightroom.adobe.com\/shares\/c0fab204c0354d2c85328ed299305590","title":"View Event Photos"}],"groups":[{"id":"660404","name":"ISyE Extended Reality Makerspace (ISYE XR)"},{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"194684","name":"Free"},{"id":"194683","name":"Talk"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"177814","name":"Postdoc"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"688751":{"#nid":"688751","#data":{"type":"event","title":"LeeAnn and Walter Muller Distinguished Scholarship Lecture Series - Dr. Bin Yu","body":[{"value":"\u003Ch2\u003E2026 LeeAnn and Walter Muller Distinguished Scholarship Lecture Series, Dr. Bin Yu\u003C\/h2\u003E\u003Cp\u003E\u003Cstrong\u003EVeridical Data Science for Healthcare in the Age of AI\u003C\/strong\u003E\u003Cbr\u003E\u003Cbr\u003EGeorgia Tech Exhibition Hall\u003Cbr\u003EKirkwood Room\u003Cbr\u003EMonday, March 30, 2026\u003Cbr\u003E3:30-4:30PM\u0026nbsp;\u003Cbr\u003E\u003Cem\u003EReception to follow at \u003C\/em\u003E\u003Ca href=\u0022https:\/\/www.isye.gatech.edu\/about\/school\/facilities\u0022\u003E\u003Cem\u003EISyE Main Atrium\u003C\/em\u003E\u003C\/a\u003E\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Ch2\u003EAbstract: Dr. Bin Yu, Keynote Speaker\u003Cbr\u003E\u0026nbsp;\u003C\/h2\u003E\u003Cp\u003EData science underpins modern AI and many advances in healthcare, yet human judgment permeates every stage of the data science life cycle. These judgment calls introduce hidden uncertainties that go well beyond sampling variability and drive many of the risks associated with AI.\u003C\/p\u003E\u003Cp\u003EWe introduce veridical data science, grounded in three fundamental principles\u2014Predictability, Computability, and Stability (PCS)\u2014to make such uncertainties explicit and assessable and to aggregate reality-checked algorithms for better results. The PCS framework unifies and extends best practices in statistics and machine learning and is illustrated through healthcare applications, including identifying genetic drivers of heart disease, reducing cost of prostate cancer detection, improving uncertainty quantification beyond standard conformal prediction, and proposing, Green Shielding, a new user-centric framework for safeguarding users of AI.\u003C\/p\u003E\u003Ch2\u003E\u0026nbsp;\u003C\/h2\u003E\u003Ch2\u003EAbout: Dr. Yu\u003Cbr\u003E\u0026nbsp;\u003C\/h2\u003E\u003Cp\u003EDr. Bin Yu is CDSS Chancellor\u0027s Distinguished Professor in Statistics, EECS, Center for Computational Biology, and Senior Advisor at the Simons Institute for the Theory of Computing, all at UC Berkeley. Her research focuses on the practice and theory of statistical machine learning, veridical data science, responsible and safe AI, and solving interdisciplinary data problems in neuroscience, genomics, and precision medicine. She and her team have developed algorithms such as iterative random forests (iRF), stability-driven NMF, adaptive wavelet distillation (AWD), Contextual Decomposition for Transformers (CD-T), SPEX and ProxySPEX for interpreting deep learning models, especially for compositional interpretability.\u003C\/p\u003E\u003Cp\u003EShe is a member of the National Academy of Sciences and of the American Academy of Arts and Sciences. She was a Guggenheim Fellow, President of Institute of Mathematical Statistics (IMS), and delivered the Tukey Lecture of the Bernoulli Society, the Breiman Lecture at NeurIPS, the IMS Rietz Lecture, and the Wald Memorial Lectures (the highest honor of IMS), and Distinguished Achievement Award and Lecture (formerly Fisher Lecture) of COPSS (Committee of Presidents of Statistical Societies). She holds an Honorary Doctorate from The University of Lausanne. She is on the Editorial Board of Proceedings of National Academy of Science (PNAS) and a co-editor of the Harvard Data Science Review (HDSR).\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EISyE welcomes Dr. Bin Yu (CDSS Chancellor\u0027s Distinguished Professor in Statistics, EECS, Center for Computational Biology, University of California, Berkeley) as the keynote speaker for its 2026 LeeAnn and Walter Muller Distinguished Scholarship Lecture Series.\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"2026 ISyE LeeAnn and Walter Muller Distinguished Scholarship Lecture Series with Dr. Bin Yu"}],"uid":"36736","created_gmt":"2026-03-05 19:55:18","changed_gmt":"2026-03-05 20:18:31","author":"ebrown386","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2026-03-30T15:30:00-04:00","event_time_end":"2026-03-30T16:30:00-04:00","event_time_end_last":"2026-03-30T16:30:00-04:00","gmt_time_start":"2026-03-30 19:30:00","gmt_time_end":"2026-03-30 20:30:00","gmt_time_end_last":"2026-03-30 20:30:00","rrule":null,"timezone":"America\/New_York"},"location":"Exhibition Hall - Kirkwood Room","extras":[],"hg_media":{"679535":{"id":"679535","type":"image","title":"LeeAnn and Walter Muller Distinguished Scholarship Lecture Series - Dr. Bin Yu","body":"\u003Cp\u003EISyE LeeAnn and Walter Muller Distinguished Scholarship Lecture Series\u0026nbsp;\u003C\/p\u003E","created":"1772740844","gmt_created":"2026-03-05 20:00:44","changed":"1772740844","gmt_changed":"2026-03-05 20:00:44","alt":"LeeAnn and Walter Muller Distinguished Scholarship Lecture Series ","file":{"fid":"263708","name":"DLS-226---Vertical-Monitor--1080-x-1158-px---2-.png","image_path":"\/sites\/default\/files\/2026\/03\/05\/DLS-226---Vertical-Monitor--1080-x-1158-px---2-.png","image_full_path":"http:\/\/hg.gatech.edu\/\/sites\/default\/files\/2026\/03\/05\/DLS-226---Vertical-Monitor--1080-x-1158-px---2-.png","mime":"image\/png","size":713710,"path_740":"http:\/\/hg.gatech.edu\/sites\/default\/files\/styles\/740xx_scale\/public\/2026\/03\/05\/DLS-226---Vertical-Monitor--1080-x-1158-px---2-.png?itok=upmesjHY"}}},"media_ids":["679535"],"related_links":[{"url":"https:\/\/eforms.isye.gatech.edu\/distinguished-scholarship-lecture-series","title":"RSVP"}],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"194683","name":"Talk"}],"invited_audience":[{"id":"194945","name":"Alumni"},{"id":"78761","name":"Faculty\/Staff"},{"id":"177814","name":"Postdoc"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"688547":{"#nid":"688547","#data":{"type":"event","title":"XR Bytes: Seok Joon Kim (ADC XR Makerspace)","body":[{"value":"\u003Cp\u003E\u003Cstrong\u003EXR Bytes\u003C\/strong\u003E\u0026nbsp;is a graduate-student-led initiative dedicated to\u0026nbsp;showcasing\u0026nbsp;the trajectory of Extended Reality (XR) research across the Institute. Our goal is to highlight the versatile ways XR technologies are applied across various disciplines and majors, fostering\u0026nbsp;an\u0026nbsp;interdisciplinary community of innovation at Georgia Tech.\u0026nbsp;\u003C\/p\u003E\u003Ch2\u003E\u003Cstrong\u003EPresenter: Seok Joon Kim\u003C\/strong\u003E\u0026nbsp;\u003Cbr\u003E\u003Cbr\u003E\u003Cstrong\u003ESeminar Title\u003C\/strong\u003E\u003C\/h2\u003E\u003Cp\u003EAHEAD of Time: Toward Robots That Behave Like Human Companions\u0026nbsp;\u003C\/p\u003E\u003Ch2\u003E\u003Cstrong\u003EAffiliation\u0026nbsp;\u003C\/strong\u003E\u003C\/h2\u003E\u003Cp\u003EGeorge W. Woodruff School of Mechanical Engineering (Georgia Tech College of Engineering), Symbiotic and Augmented Intelligence Laboratory (SAIL)\u003C\/p\u003E\u003Ch2\u003E\u003Cstrong\u003EParticipation \u0026amp;\u0026nbsp;Visibility\u0026nbsp;\u003C\/strong\u003E\u003C\/h2\u003E\u003Cp\u003EWe actively encourage cross-departmental participation and welcome engagement from both internal academic units and external industry partners. Our goal is to foster a robust community of practice around XR at Georgia Tech.\u0026nbsp;\u003C\/p\u003E\u003Ch2\u003E\u003Cstrong\u003ESubscribe \u0026amp; Sign Up\u0026nbsp;\u003C\/strong\u003E\u003C\/h2\u003E\u003Cp\u003ETo join our mailing list and RSVP your attendance for these seminar series, please sign up here: \u003Ca href=\u0022https:\/\/eforms.isye.gatech.edu\/form\/xr-rsvp?event=7051\u0022\u003E\u003Cstrong\u003ERSVP HERE\u003C\/strong\u003E\u003C\/a\u003E\u003Cstrong\u003E. \u003C\/strong\u003E\u003Cem\u003ELunch will be served for those who RSVP.\u003C\/em\u003E\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003E\u003Cstrong\u003EXR Bytes\u003C\/strong\u003E\u0026nbsp;is a graduate-student-led initiative dedicated to\u0026nbsp;showcasing\u0026nbsp;the trajectory of Extended Reality (XR) research across the Institute. Our goal is to highlight the versatile ways XR technologies are applied across various disciplines and majors, fostering\u0026nbsp;an\u0026nbsp;interdisciplinary community of innovation at Georgia Tech.\u0026nbsp;\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Weekly research seminar series hosted by ADC XR Makerspace at ISyE"}],"uid":"36736","created_gmt":"2026-02-26 17:01:09","changed_gmt":"2026-03-04 20:26:47","author":"ebrown386","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2026-04-10T12:30:00-04:00","event_time_end":"2026-04-10T13:30:00-04:00","event_time_end_last":"2026-04-10T13:30:00-04:00","gmt_time_start":"2026-04-10 16:30:00","gmt_time_end":"2026-04-10 17:30:00","gmt_time_end_last":"2026-04-10 17:30:00","rrule":null,"timezone":"America\/New_York"},"location":"ADC XR Makerspace (ISyE Main 115)","extras":[],"hg_media":{"679451":{"id":"679451","type":"image","title":"XR Bytes - Seok Joon Kim","body":null,"created":"1772125619","gmt_created":"2026-02-26 17:06:59","changed":"1772125619","gmt_changed":"2026-02-26 17:06:59","alt":"XR Bytes - Seok Joon Kim","file":{"fid":"263615","name":"Unknown-10.png","image_path":"\/sites\/default\/files\/2026\/02\/26\/Unknown-10.png","image_full_path":"http:\/\/hg.gatech.edu\/\/sites\/default\/files\/2026\/02\/26\/Unknown-10.png","mime":"image\/png","size":323668,"path_740":"http:\/\/hg.gatech.edu\/sites\/default\/files\/styles\/740xx_scale\/public\/2026\/02\/26\/Unknown-10.png?itok=bJb7XTSu"}}},"media_ids":["679451"],"related_links":[{"url":"https:\/\/eforms.isye.gatech.edu\/form\/xr-rsvp?event=7051","title":"RSVP"}],"groups":[{"id":"660404","name":"ISyE Extended Reality Makerspace (ISYE XR)"},{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"194684","name":"Free"},{"id":"194683","name":"Talk"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"177814","name":"Postdoc"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"688546":{"#nid":"688546","#data":{"type":"event","title":"XR Bytes: Prithiv Premkumar (ADC XR Makerspace)","body":[{"value":"\u003Cp\u003E\u003Cstrong\u003EXR Bytes\u003C\/strong\u003E\u0026nbsp;is a graduate-student-led initiative dedicated to\u0026nbsp;showcasing\u0026nbsp;the trajectory of Extended Reality (XR) research across the Institute. Our goal is to highlight the versatile ways XR technologies are applied across various disciplines and majors, fostering\u0026nbsp;an\u0026nbsp;interdisciplinary community of innovation at Georgia Tech.\u0026nbsp;\u003C\/p\u003E\u003Ch2\u003E\u003Cbr\u003E\u003Cstrong\u003EPresenter: Prithiv Premkumar\u003C\/strong\u003E\u0026nbsp;\u003Cbr\u003E\u0026nbsp;\u003C\/h2\u003E\u003Ch2\u003E\u003Cstrong\u003ESeminar Title:\u0026nbsp;\u003C\/strong\u003E\u003C\/h2\u003E\u003Cp\u003EMotion, Heart Rate, and Haptics: Using XR devices for Human Monitoring and Regulation\u0026nbsp;\u003C\/p\u003E\u003Ch2\u003E\u003Cstrong\u003EAffiliation\u0026nbsp;\u003C\/strong\u003E\u003C\/h2\u003E\u003Cp\u003ESchool of Interactive Computing (Georgia Tech College of Computing), Georgia Tech Sonification Lab\u003C\/p\u003E\u003Ch2\u003E\u003Cstrong\u003EParticipation \u0026amp;\u0026nbsp;Visibility\u0026nbsp;\u003C\/strong\u003E\u003C\/h2\u003E\u003Cp\u003EWe actively encourage cross-departmental participation and welcome engagement from both internal academic units and external industry partners. Our goal is to foster a robust community of practice around XR at Georgia Tech.\u0026nbsp;\u003C\/p\u003E\u003Ch2\u003E\u003Cstrong\u003ESubscribe \u0026amp; Sign Up\u0026nbsp;\u003C\/strong\u003E\u003C\/h2\u003E\u003Cp\u003ETo join our mailing list and RSVP your attendance for these seminar series, please sign up here: \u003Ca href=\u0022https:\/\/eforms.isye.gatech.edu\/form\/xr-rsvp?event=7050\u0022\u003E\u003Cstrong\u003ERSVP HERE\u003C\/strong\u003E\u003C\/a\u003E\u003Cstrong\u003E. \u003C\/strong\u003E\u003Cem\u003ELunch will be served for those who RSVP.\u003C\/em\u003E\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003E\u003Cstrong\u003EXR Bytes\u003C\/strong\u003E\u0026nbsp;is a graduate-student-led initiative dedicated to\u0026nbsp;showcasing\u0026nbsp;the trajectory of Extended Reality (XR) research across the Institute. Our goal is to highlight the versatile ways XR technologies are applied across various disciplines and majors, fostering\u0026nbsp;an\u0026nbsp;interdisciplinary community of innovation at Georgia Tech.\u0026nbsp;\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Weekly research seminar series hosted by ADC XR Makerspace at ISyE"}],"uid":"36736","created_gmt":"2026-02-26 16:54:45","changed_gmt":"2026-03-04 20:26:26","author":"ebrown386","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2026-04-03T12:30:00-04:00","event_time_end":"2026-04-03T13:30:00-04:00","event_time_end_last":"2026-04-03T13:30:00-04:00","gmt_time_start":"2026-04-03 16:30:00","gmt_time_end":"2026-04-03 17:30:00","gmt_time_end_last":"2026-04-03 17:30:00","rrule":null,"timezone":"America\/New_York"},"location":"ADC XR Makerspace (ISyE Main 115)","extras":[],"hg_media":{"679450":{"id":"679450","type":"image","title":"XR Bytes - Prithiv Premkumar","body":null,"created":"1772125133","gmt_created":"2026-02-26 16:58:53","changed":"1772125133","gmt_changed":"2026-02-26 16:58:53","alt":"XR Bytes - Prithiv Premkumar","file":{"fid":"263614","name":"Unknown-9.png","image_path":"\/sites\/default\/files\/2026\/02\/26\/Unknown-9.png","image_full_path":"http:\/\/hg.gatech.edu\/\/sites\/default\/files\/2026\/02\/26\/Unknown-9.png","mime":"image\/png","size":216840,"path_740":"http:\/\/hg.gatech.edu\/sites\/default\/files\/styles\/740xx_scale\/public\/2026\/02\/26\/Unknown-9.png?itok=ePAAxXwn"}}},"media_ids":["679450"],"related_links":[{"url":"https:\/\/eforms.isye.gatech.edu\/form\/xr-rsvp?event=7050","title":"RSVP"}],"groups":[{"id":"660404","name":"ISyE Extended Reality Makerspace (ISYE XR)"},{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"194684","name":"Free"},{"id":"194683","name":"Talk"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"177814","name":"Postdoc"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"688545":{"#nid":"688545","#data":{"type":"event","title":"XR Bytes: Hanna Neroj (ADC XR Makerspace)","body":[{"value":"\u003Cp\u003E\u003Cstrong\u003EXR Bytes\u003C\/strong\u003E\u0026nbsp;is a graduate-student-led initiative dedicated to\u0026nbsp;showcasing\u0026nbsp;the trajectory of Extended Reality (XR) research across the Institute. Our goal is to highlight the versatile ways XR technologies are applied across various disciplines and majors, fostering\u0026nbsp;an\u0026nbsp;interdisciplinary community of innovation at Georgia Tech.\u0026nbsp;\u003C\/p\u003E\u003Ch2\u003E\u003Cstrong\u003EPresenter: Hanna Neroj\u003C\/strong\u003E\u003C\/h2\u003E\u003Ch2\u003E\u003Cbr\u003E\u003Cstrong\u003ESeminar Title:\u0026nbsp;\u003C\/strong\u003E\u003C\/h2\u003E\u003Cp\u003ESimulating the Future: Experience Prototyping for Emerging Technologies via Multimodal XR\u0026nbsp;\u003C\/p\u003E\u003Ch2\u003E\u003Cstrong\u003EAffiliation\u003C\/strong\u003E\u003C\/h2\u003E\u003Cp\u003ESchool of Psychology (Georgia Tech College of Sciences), Georgia Tech Sonification Lab\u003C\/p\u003E\u003Ch2\u003E\u003Cstrong\u003EParticipation \u0026amp;\u0026nbsp;Visibility\u0026nbsp;\u003C\/strong\u003E\u003C\/h2\u003E\u003Cp\u003EWe actively encourage cross-departmental participation and welcome engagement from both internal academic units and external industry partners. Our goal is to foster a robust community of practice around XR at Georgia Tech.\u0026nbsp;\u003C\/p\u003E\u003Ch2\u003E\u003Cstrong\u003ESubscribe \u0026amp; Sign Up\u0026nbsp;\u003C\/strong\u003E\u003C\/h2\u003E\u003Cp\u003ETo join our mailing list and RSVP your attendance for these seminar series, please sign up here: \u003Ca href=\u0022https:\/\/eforms.isye.gatech.edu\/form\/xr-rsvp?event=7049\u0022\u003E\u003Cstrong\u003ERSVP HERE\u003C\/strong\u003E\u003C\/a\u003E\u003Cstrong\u003E. \u003C\/strong\u003E\u003Cem\u003ELunch will be served for those who RSVP.\u003C\/em\u003E\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003E\u003Cstrong\u003EXR Bytes\u003C\/strong\u003E\u0026nbsp;is a graduate-student-led initiative dedicated to\u0026nbsp;showcasing\u0026nbsp;the trajectory of Extended Reality (XR) research across the Institute. Our goal is to highlight the versatile ways XR technologies are applied across various disciplines and majors, fostering\u0026nbsp;an\u0026nbsp;interdisciplinary community of innovation at Georgia Tech.\u0026nbsp;\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Weekly research seminar series hosted by ADC XR Makerspace at ISyE"}],"uid":"36736","created_gmt":"2026-02-26 16:48:37","changed_gmt":"2026-03-04 20:26:00","author":"ebrown386","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2026-03-20T12:30:00-04:00","event_time_end":"2026-03-20T13:30:00-04:00","event_time_end_last":"2026-03-20T13:30:00-04:00","gmt_time_start":"2026-03-20 16:30:00","gmt_time_end":"2026-03-20 17:30:00","gmt_time_end_last":"2026-03-20 17:30:00","rrule":null,"timezone":"America\/New_York"},"location":"ADC XR Makerspace (ISyE Main 115)","extras":[],"hg_media":{"679449":{"id":"679449","type":"image","title":"XR Bytes - Hanna Neroj ","body":null,"created":"1772124681","gmt_created":"2026-02-26 16:51:21","changed":"1772124681","gmt_changed":"2026-02-26 16:51:21","alt":"XR Bytes - Hanna Neroj ","file":{"fid":"263613","name":"Unknown-8.png","image_path":"\/sites\/default\/files\/2026\/02\/26\/Unknown-8.png","image_full_path":"http:\/\/hg.gatech.edu\/\/sites\/default\/files\/2026\/02\/26\/Unknown-8.png","mime":"image\/png","size":177571,"path_740":"http:\/\/hg.gatech.edu\/sites\/default\/files\/styles\/740xx_scale\/public\/2026\/02\/26\/Unknown-8.png?itok=wd45r9Ov"}}},"media_ids":["679449"],"related_links":[{"url":"https:\/\/eforms.isye.gatech.edu\/form\/xr-rsvp?event=7049","title":"RSVP"}],"groups":[{"id":"660404","name":"ISyE Extended Reality Makerspace (ISYE XR)"},{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"194684","name":"Free"},{"id":"194683","name":"Talk"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"177814","name":"Postdoc"},{"id":"78771","name":"Public"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"688544":{"#nid":"688544","#data":{"type":"event","title":"XR Bytes: Jorge Garcia (ADC XR Makerspace)","body":[{"value":"\u003Cp\u003E\u003Cstrong\u003EXR Bytes\u003C\/strong\u003E\u0026nbsp;is a graduate-student-led initiative dedicated to\u0026nbsp;showcasing\u0026nbsp;the trajectory of Extended Reality (XR) research across the Institute. Our goal is to highlight the versatile ways XR technologies are applied across various disciplines and majors, fostering\u0026nbsp;an\u0026nbsp;interdisciplinary community of innovation at Georgia Tech.\u0026nbsp;\u003C\/p\u003E\u003Ch2\u003E\u003Cstrong\u003EPresenter: Jorge Garcia\u003C\/strong\u003E\u003C\/h2\u003E\u003Ch2\u003E\u003Cbr\u003E\u003Cstrong\u003ESeminar Title:\u0026nbsp;\u003C\/strong\u003E\u003C\/h2\u003E\u003Cp\u003EHuman-in-the-Loop and XR for Context-Rich Industrial Decision-Making\u0026nbsp;\u003C\/p\u003E\u003Ch2\u003E\u003Cstrong\u003EAffiliation\u003C\/strong\u003E\u003C\/h2\u003E\u003Cdiv\u003E\u003Cdiv\u003EH. Milton Stewart School of Industrial and Systems Engineering \u0026nbsp;(ISyE), Physical Internet Center, Supply Chain \u0026amp; Logistics Institute\u0026nbsp;\u003Cbr\u003E\u0026nbsp;\u003C\/div\u003E\u003Ch2\u003E\u003Cstrong\u003EParticipation \u0026amp;\u0026nbsp;Visibility\u0026nbsp;\u003C\/strong\u003E\u003C\/h2\u003E\u003Cp\u003EWe actively encourage cross-departmental participation and welcome engagement from both internal academic units and external industry partners. Our goal is to foster a robust community of practice around XR at Georgia Tech.\u0026nbsp;\u003C\/p\u003E\u003Ch2\u003E\u003Cstrong\u003ESubscribe \u0026amp; Sign Up\u0026nbsp;\u003C\/strong\u003E\u003C\/h2\u003E\u003Cp\u003ETo join our mailing list and RSVP your attendance for these seminar series, please sign up here: \u003Ca href=\u0022https:\/\/eforms.isye.gatech.edu\/form\/xr-rsvp?event=7048\u0022 id=\u0022OWA22b64c58-63b7-d310-12aa-d03756bd4375\u0022 rel=\u0022noreferrer noopener\u0022 target=\u0022_blank\u0022 title=\u0022https:\/\/eforms.isye.gatech.edu\/form\/xr-rsvp\u0022\u003E\u003Cstrong\u003ERSVP HERE\u003C\/strong\u003E\u003C\/a\u003E\u003Cstrong\u003E. \u003C\/strong\u003E\u003Cem\u003ELunch will be served for those who RSVP.\u003C\/em\u003E\u003C\/p\u003E\u003C\/div\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003E\u003Cstrong\u003EXR Bytes\u003C\/strong\u003E\u0026nbsp;is a graduate-student-led initiative dedicated to\u0026nbsp;showcasing\u0026nbsp;the trajectory of Extended Reality (XR) research across the Institute. Our goal is to highlight the versatile ways XR technologies are applied across various disciplines and majors, fostering\u0026nbsp;an\u0026nbsp;interdisciplinary community of innovation at Georgia Tech.\u0026nbsp;\u003Cbr\u003E\u0026nbsp;\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Weekly research seminar series hosted by ADC XR Makerspace at ISyE"}],"uid":"36736","created_gmt":"2026-02-26 16:43:34","changed_gmt":"2026-03-04 20:25:46","author":"ebrown386","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2026-03-13T12:30:00-04:00","event_time_end":"2026-03-13T13:30:00-04:00","event_time_end_last":"2026-03-13T13:30:00-04:00","gmt_time_start":"2026-03-13 16:30:00","gmt_time_end":"2026-03-13 17:30:00","gmt_time_end_last":"2026-03-13 17:30:00","rrule":null,"timezone":"America\/New_York"},"location":"ADC XR Makerspace (ISyE Main 115)","extras":[],"hg_media":{"679448":{"id":"679448","type":"image","title":"XR Bytes - Jorge Garcia","body":null,"created":"1772124318","gmt_created":"2026-02-26 16:45:18","changed":"1772124318","gmt_changed":"2026-02-26 16:45:18","alt":"XR Bytes - Jorge Garcia","file":{"fid":"263612","name":"Unknown-7.png","image_path":"\/sites\/default\/files\/2026\/02\/26\/Unknown-7.png","image_full_path":"http:\/\/hg.gatech.edu\/\/sites\/default\/files\/2026\/02\/26\/Unknown-7.png","mime":"image\/png","size":363445,"path_740":"http:\/\/hg.gatech.edu\/sites\/default\/files\/styles\/740xx_scale\/public\/2026\/02\/26\/Unknown-7.png?itok=b_FgtnTX"}}},"media_ids":["679448"],"related_links":[{"url":"https:\/\/eforms.isye.gatech.edu\/form\/xr-rsvp?event=7048","title":"RSVP"}],"groups":[{"id":"660404","name":"ISyE Extended Reality Makerspace (ISYE XR)"},{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"194683","name":"Talk"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"177814","name":"Postdoc"},{"id":"174045","name":"Graduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"688650":{"#nid":"688650","#data":{"type":"event","title":"ISyE Statistic Seminar - Ying Nian Wu","body":[{"value":"\u003Ch3\u003ETitle:\u003C\/h3\u003E\u003Cp\u003ESolving the Mysteries of Place Cells and Grid Cells by Representation Learning\u003C\/p\u003E\u003Ch3\u003EAbstract:\u003C\/h3\u003E\u003Cp\u003EThe 2014 Nobel Prize in Physiology or Medicine recognized the discovery of place cells and grid cells in the mammalian brain. Each place cell fires at a single specific location, whereas each grid cell fires at multiple locations forming a hexagonal grid pattern. Yet the computational principles underlying these phenomena have remained mysterious. We show both emerge from representation learning through geometric optimization. Grid cells learn embeddings that preserve local distances through conformal isometry, forming a coordinate system. We prove hexagonal patterns are optimal: hexagonal flat tori uniquely minimize deviation from local distance preservation by distributing curvature isotropically through six-fold symmetry. Building upon this coordinate system, place cells learn embeddings that preserve spatial adjacency relations defined by transition kernels of heat diffusion with reflecting boundary conditions, thereby forming a cognitive map. Specifically, inner products between embeddings reconstruct transition probabilities, causing localized firing patterns to emerge automatically from non-negative matrix factorization constraints. This reveals how the brain solves navigation by transforming spatial reasoning into optimization on learned geometric representations.\u003C\/p\u003E\u003Ch3\u003EBio:\u003C\/h3\u003E\u003Cp\u003EYing Nian Wu is a professor in the Department of Statistics and Data Science at UCLA. He earned his A.M. and Ph.D. in statistics from Harvard University in 1994 and 1996, respectively. From 1997 to 1999, he served as an assistant professor in the Department of Statistics at the University of Michigan before joining UCLA in 1999. He became a full professor in 2006, and he was an Amazon Scholar 2020-2025. Wu\u2019s research spans generative AI, computer vision, computational neuroscience, and bioinformatics.\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EThe 2014 Nobel Prize in Physiology or Medicine recognized the discovery of place cells and grid cells in the mammalian brain. Each place cell fires at a single specific location, whereas each grid cell fires at multiple locations forming a hexagonal grid pattern. Yet the computational principles underlying these phenomena have remained mysterious. We show both emerge from representation learning through geometric optimization. Grid cells learn embeddings that preserve local distances through conformal isometry, forming a coordinate system. We prove hexagonal patterns are optimal: hexagonal flat tori uniquely minimize deviation from local distance preservation by distributing curvature isotropically through six-fold symmetry. Building upon this coordinate system, place cells learn embeddings that preserve spatial adjacency relations defined by transition kernels of heat diffusion with reflecting boundary conditions, thereby forming a cognitive map. Specifically, inner products between embeddings reconstruct transition probabilities, causing localized firing patterns to emerge automatically from non-negative matrix factorization constraints. This reveals how the brain solves navigation by transforming spatial reasoning into optimization on learned geometric representations.\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Solving the Mysteries of Place Cells and Grid Cells by Representation Learning"}],"uid":"36861","created_gmt":"2026-03-03 15:36:02","changed_gmt":"2026-03-03 15:39:20","author":"adrysdale7","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2026-03-17T12:30:00-04:00","event_time_end":"2026-03-17T13:30:00-04:00","event_time_end_last":"2026-03-17T13:30:00-04:00","gmt_time_start":"2026-03-17 16:30:00","gmt_time_end":"2026-03-17 17:30:00","gmt_time_end_last":"2026-03-17 17:30:00","rrule":null,"timezone":"America\/New_York"},"location":"Groseclose 402","extras":[],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"194945","name":"Alumni"},{"id":"78761","name":"Faculty\/Staff"},{"id":"177814","name":"Postdoc"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"688040":{"#nid":"688040","#data":{"type":"event","title":"ISyE Seminar - Soroush Saghafian","body":[{"value":"\u003Ch3\u003E\u003Cstrong\u003ETitle:\u0026nbsp;\u003C\/strong\u003E\u003C\/h3\u003E\u003Cp\u003EMaking\u0026nbsp;AI\u0026nbsp;Impactful\u0026nbsp;in Healthcare\u003C\/p\u003E\u003Ch3\u003E\u003Cstrong\u003EAbstract:\u003C\/strong\u003E\u003C\/h3\u003E\u003Cp\u003EThere\u0026nbsp;is\u0026nbsp;increasing\u0026nbsp;evidence\u0026nbsp;that\u0026nbsp;Machine\u0026nbsp;Learning\u0026nbsp;and\u0026nbsp;Artificial\u0026nbsp;intelligence\u0026nbsp;algorithms\u0026nbsp;can\u0026nbsp;be used to enhance clinical care. In this talk, I address two critical aspects that can significantly improve the impact\u0026nbsp;of\u0026nbsp;such\u0026nbsp;algorithms\u0026nbsp;in\u0026nbsp;healthcare\u0026nbsp;practices:\u0026nbsp;(1)\u0026nbsp;moving\u0026nbsp;beyond\u0026nbsp;associations\u0026nbsp;and\u0026nbsp;creating\u0026nbsp;algorithms capable of causal reasoning under ambiguity, and (2) a human-algorithm \u201ccentaur\u201d model of care and decision-making, in which the power of human intuition is combined with the outstanding capabilities of algorithms. I describe our latest research on these subjects at the Public Impact Analytics Science Lab (PIAS-Lab)\u0026nbsp;at Harvard, and discuss findings based on our various collaborations with the Mayo Clinic, Mass General Hospital, and some other public and private organizations.\u003C\/p\u003E\u003Ch3\u003E\u003Cstrong\u003EBio:\u003C\/strong\u003E\u003C\/h3\u003E\u003Cp\u003E\u003Ca href=\u0022http:\/\/scholar.harvard.edu\/saghafian\u0022\u003ESoroush\u0026nbsp;Saghafian\u003C\/a\u003E\u0026nbsp;(\u003Ca href=\u0022https:\/\/en.wikipedia.org\/wiki\/Soroush_Saghafian\u0022\u003EWikipedia\u003C\/a\u003E)\u0026nbsp;is\u0026nbsp;an\u0026nbsp;Associate\u0026nbsp;Professor\u0026nbsp;at\u0026nbsp;Harvard\u0026nbsp;University\u0026nbsp;and\u0026nbsp;is\u0026nbsp;the\u0026nbsp;founder\u0026nbsp;and director\u0026nbsp;of\u0026nbsp;Harvard\u2019s\u0026nbsp;\u003Ca href=\u0022https:\/\/scholar.harvard.edu\/saghafian\/public-impact-analytics-science-lab-pias-lab-harvard\u0022\u003EPublic\u0026nbsp;Impact\u0026nbsp;Analytics\u0026nbsp;Science\u0026nbsp;Lab\u0026nbsp;(PIAS-Lab).\u003C\/a\u003E\u0026nbsp;He\u0026nbsp;also\u0026nbsp;serves\u0026nbsp;as\u0026nbsp;a\u0026nbsp;core\u0026nbsp;faculty\u0026nbsp;or a faculty affiliate for (a) Harvard Data Science Initiative, (b) Harvard Mossavar-Rahmani Center for Business\u0026nbsp;and\u0026nbsp;Government,\u0026nbsp;(c)\u0026nbsp;Harvard\u0026nbsp;Center\u0026nbsp;for\u0026nbsp;Health\u0026nbsp;Decision\u0026nbsp;Science,\u0026nbsp;(d)\u0026nbsp;Harvard\u0026nbsp;Ph.D.\u0026nbsp;Program\u0026nbsp;in Health\u0026nbsp;Policy,\u0026nbsp;(e)\u0026nbsp;Harvard\u0026nbsp;Belfer\u0026nbsp;Center\u0026nbsp;for\u0026nbsp;Science\u0026nbsp;and\u0026nbsp;International\u0026nbsp;Affairs,\u0026nbsp;(f)\u0026nbsp;Harvard\u0026nbsp;Center\u0026nbsp;for\u0026nbsp;Public Leadership, and (g) Harvard Ariadne Labs (a pioneer lab in health systems innovation), and holds appointments\u0026nbsp;at\u0026nbsp;Massachusetts\u0026nbsp;General\u0026nbsp;Hospital\u0026nbsp;(MGH),\u0026nbsp;Beth\u0026nbsp;Israel\u0026nbsp;Deaconess\u0026nbsp;Medical\u0026nbsp;Center\u0026nbsp;(BIDMC), and Mayo Clinic.\u0026nbsp;He is an expert in healthcare AI, analytics, and operations management, and has collaborated\u0026nbsp;with\u0026nbsp;a\u0026nbsp;variety\u0026nbsp;of\u0026nbsp;hospitals.\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EDr.\u0026nbsp;Saghafian\u0027s\u0026nbsp;research\u0026nbsp;has\u0026nbsp;appeared\u0026nbsp;numerous\u0026nbsp;times\u0026nbsp;\u003Ca href=\u0022https:\/\/scholar.harvard.edu\/saghafian\/news-1\u0022\u003Ein\u0026nbsp;the\u0026nbsp;news\u003C\/a\u003E\u0026nbsp;including\u0026nbsp;in\u0026nbsp;top\u0026nbsp;national\u0026nbsp;and\u0026nbsp;international\u0026nbsp;media\u0026nbsp;outlets,\u0026nbsp;and\u0026nbsp;has\u0026nbsp;been\u0026nbsp;recognized\u0026nbsp;through\u0026nbsp;\u003Ca href=\u0022https:\/\/scholar.harvard.edu\/saghafian\/honors-awards\u0022\u003Evarious\u0026nbsp;awards,\u003C\/a\u003E including the\u0026nbsp;I\u003Cstrong\u003ENFORMS MSOM Young Scholar Prize\u0026nbsp;\u003C\/strong\u003Efor \u201coutstanding contributions to scholarship in operations management,\u201d \u003Cstrong\u003EINFORMS MSOM Responsible Research Award\u0026nbsp;\u003C\/strong\u003E(second place) for \u201ccontributing knowledge that may have implications for making the world a better place,\u201d the Inaugural \u003Cstrong\u003EINFORMS\u0026nbsp;Mehrotra\u0026nbsp;Research\u0026nbsp;Excellence\u0026nbsp;Award\u0026nbsp;\u003C\/strong\u003E\u201cfor\u0026nbsp;significant\u0026nbsp;contributions\u0026nbsp;to\u0026nbsp;the\u0026nbsp;practice\u0026nbsp;of\u0026nbsp;health applications through operations research and management science modeling and methodologies,\u201d \u003Cstrong\u003EINFORMS\u0026nbsp;Computing\u0026nbsp;Society\u0026nbsp;Harvey\u0026nbsp;Greenberg\u0026nbsp;Award\u0026nbsp;\u003C\/strong\u003E(honorable\u0026nbsp;mention)\u0026nbsp;\u201cfor\u0026nbsp;research\u0026nbsp;excellence in the field of computation and operations research applications, especially those in emerging application fields,\u201d\u0026nbsp;\u003Cstrong\u003EINFORMS Pierskalla Award\u0026nbsp;\u003C\/strong\u003E\u201cfor the best research paper in healthcare,\u201d \u003Cstrong\u003EINFORMS Franz Edelman Award \u003C\/strong\u003E(semi-finalist) \u201cfor achievement in advanced analytics, operations research, and management\u0026nbsp;science,\u201d\u0026nbsp;and\u0026nbsp;\u003Cstrong\u003EPOMS\u0026nbsp;College\u0026nbsp;of\u0026nbsp;Healthcare\u0026nbsp;Best\u0026nbsp;Paper\u0026nbsp;Award\u003C\/strong\u003E.\u0026nbsp;His\u0026nbsp;forthcoming\u0026nbsp;book\u0026nbsp;with Cambridge University\u0026nbsp;Press,\u0026nbsp;\u201cInsight-Driven\u0026nbsp;Problem\u0026nbsp;Solving:\u0026nbsp;Analytics\u0026nbsp;Science\u0026nbsp;to\u0026nbsp;Improve the\u0026nbsp;World,\u201d has been endorsed\u0026nbsp;by top academic and industry figures [Full CV \u003Ca href=\u0022https:\/\/apps.hks.harvard.edu\/faculty\/cv\/SoroushSaghafian.pdf\u0022\u003Ehere\u003C\/a\u003E].\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Ch3\u003E\u003Cstrong\u003EAbstract:\u003C\/strong\u003E\u003C\/h3\u003E\u003Cp\u003EThere\u0026nbsp;is\u0026nbsp;increasing\u0026nbsp;evidence\u0026nbsp;that\u0026nbsp;Machine\u0026nbsp;Learning\u0026nbsp;and\u0026nbsp;Artificial\u0026nbsp;intelligence\u0026nbsp;algorithms\u0026nbsp;can\u0026nbsp;be used to enhance clinical care. In this talk, I address two critical aspects that can significantly improve the impact\u0026nbsp;of\u0026nbsp;such\u0026nbsp;algorithms\u0026nbsp;in\u0026nbsp;healthcare\u0026nbsp;practices:\u0026nbsp;(1)\u0026nbsp;moving\u0026nbsp;beyond\u0026nbsp;associations\u0026nbsp;and\u0026nbsp;creating\u0026nbsp;algorithms capable of causal reasoning under ambiguity, and (2) a human-algorithm \u201ccentaur\u201d model of care and decision-making, in which the power of human intuition is combined with the outstanding capabilities of algorithms. I describe our latest research on these subjects at the Public Impact Analytics Science Lab (PIAS-Lab)\u0026nbsp;at Harvard, and discuss findings based on our various collaborations with the Mayo Clinic, Mass General Hospital, and some other public and private organizations.\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Making AI Impactful in Healthcare"}],"uid":"34977","created_gmt":"2026-02-05 15:16:26","changed_gmt":"2026-03-02 12:51:10","author":"Julie Smith","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2026-03-03T11:00:00-05:00","event_time_end":"2026-03-03T12:00:00-05:00","event_time_end_last":"2026-03-03T12:00:00-05:00","gmt_time_start":"2026-03-03 16:00:00","gmt_time_end":"2026-03-03 17:00:00","gmt_time_end_last":"2026-03-03 17:00:00","rrule":null,"timezone":"America\/New_York"},"location":"ISyE Main 228","extras":[],"groups":[{"id":"1250","name":"Center for Health and Humanitarian Systems (CHHS)"},{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"177814","name":"Postdoc"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"687593":{"#nid":"687593","#data":{"type":"event","title":"SCL Lunch and Learn: \u0022Ahead of the Curve: Building the Electrified Supply Chain\u0022","body":[{"value":"\u003Cp\u003E\u003Cem\u003E\u003Cstrong\u003EJoin SCL affiliated faculty member Constance Crozier as she shares insights from her research and explains the forces that will define the future of freight electrification.\u003C\/strong\u003E\u003C\/em\u003E\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EThursday, March 5, 2026 | 12-1pm ET\u003C\/strong\u003E\u003C\/p\u003E\u003Cdiv\u003E\u003Cdiv\u003E\u003Cdiv\u003E\u003Cdiv\u003E\u003Cdiv\u003E\u003Cp\u003EElectric vehicles are reshaping freight networks, charging demand is growing faster than infrastructure can keep up, and companies are trying to understand how electrification will change the cost and design of their supply chains. Georgia Tech Assistant Professor, Dr. Constance Crozier\u2019s research tackles these questions with models that capture grid constraints, charging behavior, transportation patterns, and the economic tradeoffs behind electrified logistics. In this session, she will walk through the forces that will define the future of freight electrification.\u003C\/p\u003E\u003C\/div\u003E\u003C\/div\u003E\u003C\/div\u003E\u003C\/div\u003E\u003C\/div\u003E\u003Cp\u003E\u003Ca href=\u0022https:\/\/gatech.zoom.us\/webinar\/register\/9317690984289\/WN_3tkM1P6rQl2Hiu5dWiaLgQ\u0022\u003E\u003Cstrong\u003ERegister Online to Attend\u003C\/strong\u003E\u003C\/a\u003E\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EElectric vehicles are reshaping freight networks, charging demand is growing faster than infrastructure can keep up, and companies are trying to understand how electrification will change the cost and design of their supply chains.\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Join SCL affiliated faculty member Constance Crozier as she shares insights from her research and explains the forces that will define the future of freight electrification."}],"uid":"27233","created_gmt":"2026-01-22 16:30:00","changed_gmt":"2026-02-27 14:06:45","author":"Andy Haleblian","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2026-03-05T12:00:00-05:00","event_time_end":"2026-03-05T13:00:00-05:00","event_time_end_last":"2026-03-05T13:00:00-05:00","gmt_time_start":"2026-03-05 17:00:00","gmt_time_end":"2026-03-05 18:00:00","gmt_time_end_last":"2026-03-05 18:00:00","rrule":null,"timezone":"America\/New_York"},"location":"Online via Zoom","extras":[],"hg_media":{"679060":{"id":"679060","type":"image","title":"Reminder---Lunch---Learns.png","body":null,"created":"1769099657","gmt_created":"2026-01-22 16:34:17","changed":"1772200921","gmt_changed":"2026-02-27 14:02:01","alt":"SCL Lunch and Learn: \u0022Ahead of the Curve: Building the Electrified Supply Chain\u0022","file":{"fid":"263636","name":"Reminder---Lunch---Learns.png","image_path":"\/sites\/default\/files\/2026\/02\/27\/Reminder---Lunch---Learns.png","image_full_path":"http:\/\/hg.gatech.edu\/\/sites\/default\/files\/2026\/02\/27\/Reminder---Lunch---Learns.png","mime":"image\/png","size":107892,"path_740":"http:\/\/hg.gatech.edu\/sites\/default\/files\/styles\/740xx_scale\/public\/2026\/02\/27\/Reminder---Lunch---Learns.png?itok=EPnfo1IH"}}},"media_ids":["679060"],"related_links":[{"url":"https:\/\/gatech.zoom.us\/webinar\/register\/9317690984289\/WN_3tkM1P6rQl2Hiu5dWiaLgQ","title":"Register Online to Attend the Webinar"},{"url":"https:\/\/www.scl.gatech.edu\/education\/professional-education","title":"Professional Education at the Georgia Tech Supply Chain and Logistics Institute"}],"groups":[{"id":"1250","name":"Center for Health and Humanitarian Systems (CHHS)"},{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"},{"id":"1243","name":"The Supply Chain and Logistics Institute (SCL)"}],"categories":[],"keywords":[{"id":"3615","name":"information session"},{"id":"186857","name":"go-gtmi"},{"id":"122741","name":"physical internet"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"10377","name":"Career\/Professional development"}],"invited_audience":[{"id":"78771","name":"Public"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[{"value":"\u003Cp\u003Ecourse@scl.gatech.edu\u003C\/p\u003E","format":"limited_html"}],"email":[],"slides":[],"orientation":[],"userdata":""}},"688389":{"#nid":"688389","#data":{"type":"event","title":"SCL Lunch and Learn: \u0022Ahead of the Curve: The Hidden Data Supply Chain Propelling AI\u0022","body":[{"value":"\u003Cp\u003E\u003Cem\u003E\u003Cstrong\u003EJoin SCL affiliated faculty member Rosemarie Santa Gonz\u00e1lez as she shares insights from her research and explores why AI initiatives often fail at the data supply chain.\u003C\/strong\u003E\u003C\/em\u003E\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EThursday, April 2, 2026 | 12-1pm ET\u003C\/strong\u003E\u003C\/p\u003E\u003Cdiv\u003E\u003Cdiv\u003E\u003Cdiv\u003E\u003Cdiv\u003E\u003Cdiv\u003E\u003Cp\u003EAI initiatives often struggle not because of model sophistication, but because the underlying data supply chain has not been mapped, synchronized, or aligned with operational decision-making. Just as a physical supply chain cannot function without visibility, coordination, and flow from source to destination, AI systems cannot create value when data sources are siloed, inconsistently governed, or disconnected from the \u201clast mile\u201d of decision execution. Even well designed models stall when the upstream data inputs are unreliable or the downstream decision processes are unclear.\u003C\/p\u003E\u003Cp\u003EThis talk reframes AI implementation through the lens of the hidden data supply chain that propels it - from data sourcing and acquisition, to transformation, integration, governance, and delivery into decision environments. We will explore the continuous loop between data engineering and AI development, showing how model requirements should shape data architecture from the outset, and why data pipelines must be engineered as critical infrastructure rather than reactive fixes. Participants will leave with a practical framework for mapping their data supply chain, identifying bottlenecks and failure points in the data-to-decision flow, and building resilient data architectures that support reliable, explainable, and production ready AI systems.\u003C\/p\u003E\u003Cp\u003EFeaturing Rosemarie Santa Gonz\u00e1lez, Ph.D., Research Scientist in the Institute for Robotics and Intelligent Machines (IRIM) and the NSF AI\u2011CARING Institute at Georgia Tech, and an instructor in the SCL professional education program.\u003C\/p\u003E\u003C\/div\u003E\u003C\/div\u003E\u003C\/div\u003E\u003C\/div\u003E\u003C\/div\u003E\u003Cp\u003E\u003Ca href=\u0022https:\/\/gatech.zoom.us\/webinar\/register\/7117715216849\/WN_mjKMn5ZAR7CX4Z_0Ieh3cQ\u0022\u003E\u003Cstrong\u003ERegister Online to Attend\u003C\/strong\u003E\u003C\/a\u003E\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EAI initiatives often struggle not because the model is weak, but because the data supply chain is. In this Lunch and Learn, we will map the hidden system that powers AI, from data sourcing and acquisition through transformation, integration, governance, and delivery into real decision environments. You will leave with a practical framework to identify bottlenecks in the data-to-decision flow and build resilient, production-ready pipelines that make AI reliable, explainable, and usable.\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Join SCL affiliated faculty member Rosemarie Santa Gonz\u00e1lez as she shares insights from her research and explores why AI initiatives often fail at the data supply chain."}],"uid":"27233","created_gmt":"2026-02-19 18:16:24","changed_gmt":"2026-02-27 13:37:26","author":"Andy Haleblian","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2026-04-02T12:00:00-04:00","event_time_end":"2026-04-02T13:00:00-04:00","event_time_end_last":"2026-04-02T13:00:00-04:00","gmt_time_start":"2026-04-02 16:00:00","gmt_time_end":"2026-04-02 17:00:00","gmt_time_end_last":"2026-04-02 17:00:00","rrule":null,"timezone":"America\/New_York"},"location":"Online via Zoom","extras":[],"hg_media":{"679369":{"id":"679369","type":"image","title":"SCL Lunch and Learn: \u0022Ahead of the Curve: The Hidden Data Supply Chain Propelling AI\u0022","body":null,"created":"1771526105","gmt_created":"2026-02-19 18:35:05","changed":"1771526132","gmt_changed":"2026-02-19 18:35:32","alt":"SCL Lunch and Learn: \u0022Ahead of the Curve: The Hidden Data Supply Chain Propelling AI\u0022","file":{"fid":"263519","name":"hg_LNL_HiddenSCData_20260402.png","image_path":"\/sites\/default\/files\/2026\/02\/19\/hg_LNL_HiddenSCData_20260402.png","image_full_path":"http:\/\/hg.gatech.edu\/\/sites\/default\/files\/2026\/02\/19\/hg_LNL_HiddenSCData_20260402.png","mime":"image\/png","size":112330,"path_740":"http:\/\/hg.gatech.edu\/sites\/default\/files\/styles\/740xx_scale\/public\/2026\/02\/19\/hg_LNL_HiddenSCData_20260402.png?itok=wkhi1fMl"}}},"media_ids":["679369"],"related_links":[{"url":"https:\/\/gatech.zoom.us\/webinar\/register\/7117715216849\/WN_mjKMn5ZAR7CX4Z_0Ieh3cQ","title":"Register Online to Attend the Webinar"},{"url":"https:\/\/www.scl.gatech.edu\/education\/professional-education","title":"Professional Education at the Georgia Tech Supply Chain and Logistics Institute"}],"groups":[{"id":"1250","name":"Center for Health and Humanitarian Systems (CHHS)"},{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"},{"id":"1243","name":"The Supply Chain and Logistics Institute (SCL)"}],"categories":[],"keywords":[{"id":"3615","name":"information session"},{"id":"186857","name":"go-gtmi"},{"id":"122741","name":"physical internet"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"10377","name":"Career\/Professional development"}],"invited_audience":[{"id":"78771","name":"Public"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[{"value":"\u003Cp\u003Ecourse@scl.gatech.edu\u003C\/p\u003E","format":"limited_html"}],"email":[],"slides":[],"orientation":[],"userdata":""}},"687141":{"#nid":"687141","#data":{"type":"event","title":"Allen\u2013Davidson\u2013Coleman XR Makerspace Grand Opening","body":[{"value":"\u003Cp\u003ECelebrate the opening of the Allen\u2013Davidson\u2013Coleman XR Makerspace with an interdisciplinary XR Symposium bringing together faculty, students, and staff interested in immersive technologies for research, education, and human-centered engineering. Plus, guests will enjoy a speaker symposium, networking, and a hands-on open house with live XR demonstrations.\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EJanuary 23, 2026\u003C\/strong\u003E\u003Cbr\u003E10:30 AM \u2013 4:00 PM\u0026nbsp;\u003Cbr\u003EISyE Main Building\u003Cstrong\u003E\u0026nbsp;\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EAgenda:\u0026nbsp;\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003E10:30 AM \u2013 Noon (ISyE Main Atrium):\u003C\/strong\u003E\u003Cbr\u003EXR Symposium: Invited Talks - Short talks and discussion on XR research and applications across campus\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003ENoon \u2013 1:00 PM (ISyE Main Atrium):\u0026nbsp;\u003C\/strong\u003E\u003Cbr\u003ELunch \u0026amp; Networking: Informal networking in the ISyE Main Atrium\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003E1:00 \u2013 4:00 PM (ISyE Main 115)\u003C\/strong\u003E\u003Cbr\u003EXR Makerspace Open House \u0026amp; Demos: Hands-on demonstrations, guided tours, and conversations with XR Makerspace staff and researchers\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EInvited Speakers:\u003C\/strong\u003E\u003Cbr\u003E\u003Cbr\u003E\u003Cstrong\u003EBruce Walker\u003C\/strong\u003E, School of Psychology \u0026amp; School of Interactive Computing, Georgia Tech\u003Cbr\u003E\u003Cstrong\u003ETalk Title:\u003C\/strong\u003E\u0026nbsp;Research With, About, and In Virtual Reality: A Sample of Sonification Lab XR R\u0026amp;D Projects\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EBio:\u003C\/strong\u003E\u0026nbsp;Bruce N. Walker is Professor of Psychology and Interactive Computing at Georgia Tech. His Sonification Lab studies multimodal interfaces, sonification and auditory displays, and human-technology interaction in complex tasks, futuristic technologies, and VR\/AR\/XR. Dr. Walker is founding Director of VRlandia, a GT shared collaboratory for VR\/AR\/XR research and development. He is also Director of the Center for Human-AI-Robot Interaction (CHART). He teaches HCI, Sensation \u0026amp; Perception, and Assistive Technology; and has consulted for NASA, state and federal governments, the military, and private companies.\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EFrederick Benaben\u003C\/strong\u003E, H. Milton Stewart School of Industrial \u0026amp; Systems Engineering, Georgia Tech\u003Cbr\u003E\u003Cstrong\u003ETalk Title:\u003C\/strong\u003E\u0026nbsp;A Framework for Immersive Technologies in Engineering: An Illustrated Perspective\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EBio:\u003C\/strong\u003E\u0026nbsp;Frederick Benaben is Tenured Full Professor at Institut Mines Telecom in France (Industrial Engineering Center) and Visiting Professor at the Georgia Institute of Technology. His research focuses on the use of Artificial Intelligence and Immersive Technologies for decision support and the management of complex situations in uncertain environments. He is the head of the \u201cDigital Systems for Crisis Management and Security\u201d research team at IMT, directs the IOMEGA VR Lab, leads the POD (Physics of Decision) and HITeC (Hybrid Immersive Teaching Campus) initiatives, and co-directs the international SIReN Lab with Georgia Tech.\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EShreyas Kousik\u003C\/strong\u003E, George W. Woodruff School of Mechanical Engineering, Georgia Tech\u003Cbr\u003E\u003Cstrong\u003ETalk Title:\u003C\/strong\u003E\u0026nbsp;Do Dangerous Things Safely in XR Humanoid Robot Teleoperation\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EBio:\u003C\/strong\u003E\u0026nbsp;Shreyas Kousik is an assistant professor in the Mechanical Engineering at Georgia Tech. He previously held postdoctoral appointments at Stanford University and in the NAV Lab with Prof. Grace Gao. He earned his Ph.D. in Mechanical Engineering from the University of Michigan, advised by Prof. Ram Vasudevan in the ROAHM Lab. His research focuses on guaranteeing safety in autonomous robotic systems through collision avoidance, with an emphasis on modeling uncertainty in perception and estimation for practical planning and control on real robots.\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EWayne Li\u003C\/strong\u003E, Colleges of Design and Engineering, Georgia Tech\u003Cbr\u003E\u003Cstrong\u003ETalk Title:\u003C\/strong\u003E\u0026nbsp;Using XR for Automotive Interior Development (Hyundai Project)\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EBio:\u003C\/strong\u003E\u0026nbsp;Wayne K. Li is the James L. Oliver Professor, holding a joint position between the Colleges of Design and Engineering at Georgia Institute of Technology. He leads joint teaching initiatives and advances interdisciplinary collaboration between mechanical engineering and industrial design through classes and the Innovation and Design Collaborative (IDC) and Design Bloc. Li\u0027s research areas include ethnographic research, multidisciplinary online education, and human-machine interaction in transportation design. His career spans industry and academia. Li has led innovation and market expansion for Pottery Barn seasonal home products, taught in Stanford University\u0027s design program, led interface development at Volkswagen of America\u0027s Electronics Research Laboratory, and developed corporate brand and vehicle differentiation strategies at Ford Motor Company. He has also worked as a product designer and mechanical engineer at IDEO Product Development.\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EMeryem Yilmaz Soylu\u003C\/strong\u003E, Center for 21st Century Universities, Georgia Tech\u003Cbr\u003E\u003Cstrong\u003ETalk Title: \u003C\/strong\u003EDesigning Human-Centered XR for Learning, Reflection, and Skill Development\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EBio:\u003C\/strong\u003E\u0026nbsp;Meryem Yilmaz Soylu is a Research Scientist at Georgia Tech\u2019s Center for 21st Century Universities (C21U) within the College of Lifetime Learning, where her work focuses on human-centered learning design, immersive technologies, and AI-supported educational systems. Her research examines how XR and AI can support learning experiences and engagement, leadership, and durable skills development across online and hybrid learning environments. Her work combines mixed-methods research and learner experience (UX) design to inform the design and evaluation of real-world educational systems.\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EJoin us for the opening of the Allen\u2013Davidson\u2013Coleman XR Makerspace with an interdisciplinary XR Symposium that convenes faculty, students, and staff to engage with immersive technologies shaping research, education, and human-centered engineering.\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Celebrate the opening of the Allen\u2013Davidson\u2013Coleman XR Makerspace "}],"uid":"36736","created_gmt":"2026-01-09 19:42:58","changed_gmt":"2026-02-25 20:14:43","author":"ebrown386","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2026-01-23T10:30:00-05:00","event_time_end":"2026-01-23T16:00:00-05:00","event_time_end_last":"2026-01-23T16:00:00-05:00","gmt_time_start":"2026-01-23 15:30:00","gmt_time_end":"2026-01-23 21:00:00","gmt_time_end_last":"2026-01-23 21:00:00","rrule":null,"timezone":"America\/New_York"},"location":"ISyE Main Building","extras":[],"related_links":[{"url":"https:\/\/eforms.isye.gatech.edu\/grand-opening-xr-makerspace","title":"RSVP"}],"groups":[{"id":"660404","name":"ISyE Extended Reality Makerspace (ISYE XR)"},{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"194683","name":"Talk"},{"id":"1789","name":"Conference\/Symposium"},{"id":"194613","name":"Industry"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"177814","name":"Postdoc"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"687992":{"#nid":"687992","#data":{"type":"event","title":"2026 ISyE LeeAnn and Walter Muller Distinguished Lecture Series: Eddie Capel","body":[{"value":"\u003Ch2\u003E2026 LeeAnn and Walter Muller Distinguished Lecture Series, Eddie Capel\u003C\/h2\u003E\u003Cp\u003E\u003Cstrong\u003ELeading with Purpose and Innovation: Manhattan Associates\u0027 Journey\u003C\/strong\u003E\u003Cbr\u003EISyE Main\u003Cbr\u003EThursday, February 26, 2026\u003Cbr\u003E3:30-4:30PM\u0026nbsp;\u003Cbr\u003EReception to follow.\u0026nbsp;\u003C\/p\u003E\u003Ch5\u003E\u003Cstrong\u003ERSVP here: https:\/\/eforms.isye.gatech.edu\/2026-distinguished-lecture-series\u003C\/strong\u003E\u003C\/h5\u003E\u003Ch2\u003E\u003Cbr\u003EAbstract: Eddie Capel, Keynote Speaker\u003Cbr\u003E\u0026nbsp;\u003C\/h2\u003E\u003Cp\u003EOver the past 25 years, Manhattan Associates has played a pivotal role in transforming global supply chains, enabling many of the world\u2019s largest retailers, brands, pharmaceutical distributors, and logistics providers to operate faster, smarter, and more reliably.\u003Cbr\u003E\u003Cbr\u003EIn this lecture, Eddie Capel, chairman of the board at Manhattan Associates, will reflect on Manhattan\u2019s journey and evolution during a period of continuous technological disruption, from the early days of the internet, through the shift to cloud-native platforms, and into today\u2019s AI-driven era. He will discuss how being an early adopter of emerging technologies, combined with disciplined engineering thinking and foundation, allowed Manhattan to build and continuously reinvent its solutions, and sustain industry leadership.\u003C\/p\u003E\u003Cp\u003E\u003Cbr\u003EThe session highlights how strategic technology choices, long-term R\u0026amp;D investment, and a strong culture enabled Manhattan to deliver significant gains in supply chain efficiency and productivity. Eddie will also share leadership lessons for the next generation, emphasizing curiosity, adaptability, and the importance of embracing new technologies early to shape, rather than react to, the future of the overall industry.\u003C\/p\u003E\u003Ch2\u003E\u0026nbsp;\u003C\/h2\u003E\u003Ch2\u003EAbout: Eddie Capel, Keynote Speaker\u003Cbr\u003E\u0026nbsp;\u003C\/h2\u003E\u003Cp\u003EEddie Capel serves as the executive chairman of the board at Manhattan Associates. He previously served as executive vice-chairman for three months from February 2025 until end of 2025. Before that, beginning in 2013, Mr. Capel led the company as its president and CEO, driving innovation and growth. Prior to that, he served as executive vice president and chief operating officer. With more than 30 years of experience in supply chain strategy and operations, Mr. Capel has played a pivotal role in shaping industry-leading solutions.\u003C\/p\u003E\u003Cp\u003EBefore joining Manhattan Associates in June 2000, Mr. Capel held key leadership positions at Real Time Solutions (RTS), where he served as chief operations officer and vice president of operations. In these roles, he led teams that supported the supply chain strategies of major companies, including Walmart, Amazon.com, and J.C. Penney. He also served as director of operations at Unarco Automation, an Industrial Automation\/Robotics systems integrator. Earlier in his career, he worked as a project manager and system designer for ABB Robotics in the United Kingdom.\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003E\u003Cstrong\u003E2026 ISyE LeeAnn and Walter Muller Distinguished Scholarship Lecture Series\u003C\/strong\u003E\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"2026 ISyE LeeAnn and Walter Muller Distinguished Scholarship Lecture Series"}],"uid":"36736","created_gmt":"2026-02-03 14:38:35","changed_gmt":"2026-02-19 20:02:54","author":"ebrown386","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2026-02-26T15:30:00-05:00","event_time_end":"2026-02-26T16:30:00-05:00","event_time_end_last":"2026-02-26T16:30:00-05:00","gmt_time_start":"2026-02-26 20:30:00","gmt_time_end":"2026-02-26 21:30:00","gmt_time_end_last":"2026-02-26 21:30:00","rrule":null,"timezone":"America\/New_York"},"location":"ISyE Main Atrium","extras":[],"hg_media":{"679174":{"id":"679174","type":"image","title":"2026 ISyE LeeAnn and Walter Muller Distinguished Scholarship Lecture Series: Eddie Capel","body":null,"created":"1770129596","gmt_created":"2026-02-03 14:39:56","changed":"1770129596","gmt_changed":"2026-02-03 14:39:56","alt":"2026 ISyE LeeAnn and Walter Muller Distinguished Scholarship Lecture Series: Eddie Capel","file":{"fid":"263302","name":"Monitor-Template_2.png","image_path":"\/sites\/default\/files\/2026\/02\/03\/Monitor-Template_2.png","image_full_path":"http:\/\/hg.gatech.edu\/\/sites\/default\/files\/2026\/02\/03\/Monitor-Template_2.png","mime":"image\/png","size":2835066,"path_740":"http:\/\/hg.gatech.edu\/sites\/default\/files\/styles\/740xx_scale\/public\/2026\/02\/03\/Monitor-Template_2.png?itok=UAoEImPh"}}},"media_ids":["679174"],"related_links":[{"url":"https:\/\/eforms.isye.gatech.edu\/2026-distinguished-lecture-series","title":"RSVP"}],"groups":[{"id":"1250","name":"Center for Health and Humanitarian Systems (CHHS)"},{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[{"id":"168400","name":"ISyE Distinguished Lecture"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"194683","name":"Talk"},{"id":"194613","name":"Industry"},{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"177814","name":"Postdoc"},{"id":"78771","name":"Public"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"688279":{"#nid":"688279","#data":{"type":"event","title":"CHHS Webinar Series: \u0022Wildfire Smoke and Health Impacts \u2014 Preliminary Results and Research Opportunities\u0022","body":[{"value":"\u003Cp\u003EWildfire smoke poses an imminent public health threat by releasing large amounts of toxic pollutants into the atmosphere. Of particular concern is fine particulate matter \u003Ccode\u003E\u003Cem\u003EPM2.5\u003C\/em\u003E\u003C\/code\u003E, which contributes to 90% of the total particle mass emitted during these events. Because these particles are small enough to enter the bloodstream, the ability to \u0022see,\u0022 \u0022track,\u0022 and \u0022predict\u0022 the spread of smoke has become an urgent task with significant societal implications.\u003C\/p\u003E\u003Cp\u003EThis threat is especially acute for vulnerable populations, including children, pregnant women, older adults, and outdoor workers, as well as those with preexisting cardiovascular disease or from low socioeconomic status groups. Developing a robust tracking capability is critical for generating accurate, real-time air quality predictions during active wildfire episodes, allowing these individuals to take timely and effective mitigation actions.\u003C\/p\u003E\u003Cp\u003EBeyond immediate safety, advanced modeling provides a vital dataset for scientific investigations into the long-term health impacts of wildfires. By leveraging the generative nature of modern AI, we can efficiently simulate multiple smoke spread scenarios under various conditions. This facilitates long-term planning for essential services, such as managing hospital capacity during fire events, directing traffic, and informing insurance policy.\u003C\/p\u003E\u003Cp\u003EThis talk will present preliminary results on the modeling and prediction of wildfire smoke using a combination of remote-sensing and computer simulation data. We will also explore future research opportunities in this rapidly evolving field of environmental health and data science.\u003C\/p\u003E\u003Cp\u003EFeaturing \u003Ca href=\u0022https:\/\/www.isye.gatech.edu\/users\/xiao-liu\u0022\u003EXiao Liu, PhD\u003C\/a\u003E, David M. McKenney Family Associate Professor, \u003Ca href=\u0022https:\/\/www.isye.gatech.edu\/\u0022\u003EH. Milton Stewart School of Industrial and Systems Engineering\u003C\/a\u003E, Georgia Tech \u003Ca href=\u0022https:\/\/chhs.gatech.edu\/\u0022\u003ECenter for Health and Humanitarian Systems\u003C\/a\u003E. Dr. Liu\u0027s research focuses on developing data-driven methods for scientific and engineering applications. His work has been published in leading Industrial Engineering and Statistics journals, including JASA, and AOAS. He has served as the president of the Data Analytics \u0026amp; Information Systems division of IISE and as Program Chair for the 2025 IISE Annual Conference \u0026amp; Expo. Before returning to academia, he worked as a research staff member at the IBM Thomas J. Watson Research Center.\u003C\/p\u003E\u003Cp\u003EOffered online via Zoom. \u003Ca href=\u0022https:\/\/gatech.zoom.us\/webinar\/register\/WN_hIUmG_xERjC-vCS9nMikYA#\/registration\u0022\u003EPlease register to attend\u003C\/a\u003E.\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EThis talk will present preliminary results on the modeling and prediction of wildfire smoke using a combination of remote-sensing and computer simulation data. We will also explore future research opportunities in this rapidly evolving field of environmental health and data science.\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"How modern AI and remote-sensing data can be used to track and predict toxic wildfire smoke to protect vulnerable populations and improve long-term public health planning."}],"uid":"27233","created_gmt":"2026-02-16 17:58:13","changed_gmt":"2026-02-16 18:26:58","author":"Andy Haleblian","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2026-02-27T10:00:00-05:00","event_time_end":"2026-02-27T11:00:00-05:00","event_time_end_last":"2026-02-27T11:00:00-05:00","gmt_time_start":"2026-02-27 15:00:00","gmt_time_end":"2026-02-27 16:00:00","gmt_time_end_last":"2026-02-27 16:00:00","rrule":null,"timezone":"America\/New_York"},"extras":[],"hg_media":{"679296":{"id":"679296","type":"image","title":"CHHS Webinar Series: \u0022Wildfire Smoke and Health Impacts \u2014 Preliminary Results and Research Opportunities\u0022","body":null,"created":"1771264597","gmt_created":"2026-02-16 17:56:37","changed":"1771264657","gmt_changed":"2026-02-16 17:57:37","alt":"CHHS Webinar Series: \u0022Wildfire Smoke and Health Impacts \u2014 Preliminary Results and Research Opportunities\u0022","file":{"fid":"263438","name":"CHHS-webinar_20260227.jpg","image_path":"\/sites\/default\/files\/2026\/02\/16\/CHHS-webinar_20260227.jpg","image_full_path":"http:\/\/hg.gatech.edu\/\/sites\/default\/files\/2026\/02\/16\/CHHS-webinar_20260227.jpg","mime":"image\/jpeg","size":153233,"path_740":"http:\/\/hg.gatech.edu\/sites\/default\/files\/styles\/740xx_scale\/public\/2026\/02\/16\/CHHS-webinar_20260227.jpg?itok=L5dXMhjC"}}},"media_ids":["679296"],"related_links":[{"url":"https:\/\/gatech.zoom.us\/webinar\/register\/WN_hIUmG_xERjC-vCS9nMikYA#\/registration","title":"To attend, please register online via Zoom"}],"groups":[{"id":"1250","name":"Center for Health and Humanitarian Systems (CHHS)"},{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"},{"id":"1243","name":"The Supply Chain and Logistics Institute (SCL)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"194684","name":"Free"},{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"177814","name":"Postdoc"},{"id":"78771","name":"Public"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"687997":{"#nid":"687997","#data":{"type":"event","title":"ISyE Seminar - Sheng Liu","body":[{"value":"\u003Ch3\u003ETitle:\u0026nbsp;\u003C\/h3\u003E\u003Cdiv\u003EZoning in Emerging Logistics Systems: New Theory and Practice\u003C\/div\u003E\u003Cdiv\u003E\u0026nbsp;\u003C\/div\u003E\u003Ch3\u003EAbstract:\u003C\/h3\u003E\u003Cdiv\u003EZoning shapes how complicated logistics and societal systems work in practice. In particular, the growth of e-commerce and on-demand delivery services poses new challenges for operationalizing zoning to achieve efficiency goals. In this talk, I will discuss new zoning methods and their applications to manage emerging logistics systems in retail and last-mile delivery. Specifically, I will introduce a provably effective zoning policy for handling time-sensitive delivery requests under uncertainty and describe a field implementation of a flexible zoning policy for a large logistics company. I will also present a new approach to using zoning to manage a dual-delivery system comprising both scheduled and on-demand delivery jobs, shedding light on the value of co-modality for future transportation and logistics systems.\u0026nbsp;\u003C\/div\u003E\u003Cdiv\u003E\u0026nbsp;\u003C\/div\u003E\u003Ch3\u003EBio:\u003C\/h3\u003E\u003Cdiv\u003ESheng Liu is an Assistant Professor of Operations Management and Statistics at the Rotman School of Management, University of Toronto. He earned a PhD in Operations Research from UC Berkeley in 2019. Sheng\u0027s research focuses on solving operations problems in supply chains, transportation, and logistics systems through optimization and data analytics. His industry experience includes consulting or working for organizations such as JD.com, Sport Chek, Ninja Van, Hungerhub, Amazon, and Lyft. \u0026nbsp;He also strives to improve decision outcomes for vulnerable populations, motivated by collaboration with nonprofit organizations. His work has been recognized with several awards and paper competitions, including the INFORMS Public Sector Operations Research Best Paper Award, the INFORMS TSL Outstanding Paper Award (Freight Transportation and Logistics), and the M\u0026amp;SOM Data-Driven Research Competition. He currently serves as an associate editor of Transportation Science, a senior editor of Production and Operations Management, and an editorial review board member of Service Science.\u003C\/div\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Ch3\u003EAbstract:\u003C\/h3\u003E\u003Cdiv\u003EZoning shapes how complicated logistics and societal systems work in practice. In particular, the growth of e-commerce and on-demand delivery services poses new challenges for operationalizing zoning to achieve efficiency goals. In this talk, I will discuss new zoning methods and their applications to manage emerging logistics systems in retail and last-mile delivery. Specifically, I will introduce a provably effective zoning policy for handling time-sensitive delivery requests under uncertainty and describe a field implementation of a flexible zoning policy for a large logistics company. I will also present a new approach to using zoning to manage a dual-delivery system comprising both scheduled and on-demand delivery jobs, shedding light on the value of co-modality for future transportation and logistics systems.\u0026nbsp;\u003C\/div\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Zoning in Emerging Logistics Systems: New Theory and Practice"}],"uid":"34977","created_gmt":"2026-02-03 17:59:06","changed_gmt":"2026-02-11 10:37:12","author":"Julie Smith","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2026-02-19T11:00:00-05:00","event_time_end":"2026-02-19T12:00:00-05:00","event_time_end_last":"2026-02-19T12:00:00-05:00","gmt_time_start":"2026-02-19 16:00:00","gmt_time_end":"2026-02-19 17:00:00","gmt_time_end_last":"2026-02-19 17:00:00","rrule":null,"timezone":"America\/New_York"},"location":"ISyE Main 228","extras":[],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"177814","name":"Postdoc"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"687468":{"#nid":"687468","#data":{"type":"event","title":"ISyE Seminar - Alexandria Schmid","body":[{"value":"\u003Ch3\u003E\u003Cstrong\u003ETitle:\u0026nbsp;\u003C\/strong\u003E\u003C\/h3\u003E\u003Cdiv dir=\u0022ltr\u0022\u003EA double decomposition algorithm for network planning and operations in deviated fixed-route microtransit\u003C\/div\u003E\u003Cdiv dir=\u0022ltr\u0022\u003E\u0026nbsp;\u003C\/div\u003E\u003Ch3\u003E\u003Cstrong\u003EAbstract\u003C\/strong\u003E:\u0026nbsp;\u003C\/h3\u003E\u003Cdiv\u003EMicrotransit offers opportunities to enhance urban mobility by combining the reliability of public transit and the flexibility of ride-sharing. This paper optimizes the design and operations of a deviated fixed-route microtransit system that relies on reference lines but can deviate on demand in response to passenger requests. We formulate a Microtransit Network Design (MiND) model via two-stage stochastic integer optimization, with a first-stage network design and service scheduling structure and a second-stage vehicle routing structure.\u0026nbsp;We derive a tight second-stage relaxation using a subpath-based representation of microtransit operations in a load-expanded network. We develop a double-decomposition algorithm combining Benders decomposition and subpath-based column generation. We prove that the algorithm maintains a valid optimality gap and converges to an optimal solution in a finite number of iterations. Results obtained with real-world data from Manhattan show that the methodology scales to large and otherwise-intractable instances, with up to 10-100 candidate lines and hundreds of stops. Comparisons with transit and ride-sharing suggest that microtransit can provide win-win outcomes toward efficient mobility (high demand coverage, low costs, high level of service), equitable mobility (broad geographic reach) and sustainable mobility (limited environmental footprint).\u0026nbsp;\u003C\/div\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Ch3\u003E\u003Cstrong\u003EAbstract\u003C\/strong\u003E:\u0026nbsp;\u003C\/h3\u003E\u003Cp\u003EMicrotransit offers opportunities to enhance urban mobility by combining the reliability of public transit and the flexibility of ride-sharing. This paper optimizes the design and operations of a deviated fixed-route microtransit system that relies on reference lines but can deviate on demand in response to passenger requests. We formulate a Microtransit Network Design (MiND) model via two-stage stochastic integer optimization, with a first-stage network design and service scheduling structure and a second-stage vehicle routing structure.\u0026nbsp;We derive a tight second-stage relaxation using a subpath-based representation of microtransit operations in a load-expanded network. We develop a double-decomposition algorithm combining Benders decomposition and subpath-based column generation. We prove that the algorithm maintains a valid optimality gap and converges to an optimal solution in a finite number of iterations. Results obtained with real-world data from Manhattan show that the methodology scales to large and otherwise-intractable instances, with up to 10-100 candidate lines and hundreds of stops. Comparisons with transit and ride-sharing suggest that microtransit can provide win-win outcomes toward efficient mobility (high demand coverage, low costs, high level of service), equitable mobility (broad geographic reach) and sustainable mobility (limited environmental footprint).\u0026nbsp;\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"A double decomposition algorithm for network planning and operations in deviated fixed-route microtransit"}],"uid":"34977","created_gmt":"2026-01-20 18:01:39","changed_gmt":"2026-02-03 14:07:46","author":"Julie Smith","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2026-02-03T11:00:00-05:00","event_time_end":"2026-02-03T12:00:00-05:00","event_time_end_last":"2026-02-03T12:00:00-05:00","gmt_time_start":"2026-02-03 16:00:00","gmt_time_end":"2026-02-03 17:00:00","gmt_time_end_last":"2026-02-03 17:00:00","rrule":null,"timezone":"America\/New_York"},"location":"ISyE Main 228","extras":[],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"177814","name":"Postdoc"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"687811":{"#nid":"687811","#data":{"type":"event","title":"ISyE Seminar - Brian Liu","body":[{"value":"\u003Ch3\u003E\u003Cstrong\u003ETitle:\u003C\/strong\u003E\u003C\/h3\u003E\u003Cp\u003EFrontiers and Applications at the Interface of Discrete Optimization and Interpretable Machine Learning\u003C\/p\u003E\u003Ch3\u003E\u003Cstrong\u003EAbstract:\u003C\/strong\u003E\u003C\/h3\u003E\u003Cp\u003EModern machine learning models achieve remarkable predictive accuracy and can capture complex interactions, but they are often difficult to interpret and may fail to reveal useful relationships in the data. This lack of interpretability also limits their use in high-stakes applications such as healthcare, where predictions must be auditable for trust and safety. Using tree ensembles (e.g., gradient boosting or random forests) as a motivating example, we propose a novel optimization-based framework for extracting interpretable rule-based models at the post-training stage. We formulate rule extraction as a large-scale discrete optimization problem that balances predictive accuracy with considerations such as model compactness, stability, and transparency. To address these problems, we develop specialized algorithms that scale beyond the capabilities of off-the-shelf optimization software. Using mental telehealth treatment data from our industry collaborators at SilverCloud Health, we demonstrate how these methods enable practitioners to extract meaningful insights from complex datasets and predictive models.\u003C\/p\u003E\u003Ch3\u003E\u003Cstrong\u003EBio:\u003C\/strong\u003E\u003C\/h3\u003E\u003Cp\u003EBrian Liu is a fifth-year Ph.D. candidate in Operations Research at MIT, advised by Professor Rahul Mazumder. His research lies at the intersection of discrete optimization, statistics, and computer science, with a focus on developing efficient and interpretable machine learning algorithms. His work is motivated by real-world applications in domains such as healthcare and medicine and has received multiple Best Student Paper Awards from INFORMS (Data Mining; Quality, Statistics, and Reliability) and the American Statistical Association (Statistical Computing; Nonparametric Statistics).\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Ch3\u003E\u003Cstrong\u003EAbstract:\u003C\/strong\u003E\u0026nbsp;\u003C\/h3\u003E\u003Cp\u003EModern machine learning models achieve remarkable predictive accuracy and can capture complex interactions, but they are often difficult to interpret and may fail to reveal useful relationships in the data. This lack of interpretability also limits their use in high-stakes applications such as healthcare, where predictions must be auditable for trust and safety. Using tree ensembles (e.g., gradient boosting or random forests) as a motivating example, we propose a novel optimization-based framework for extracting interpretable rule-based models at the post-training stage. We formulate rule extraction as a large-scale discrete optimization problem that balances predictive accuracy with considerations such as model compactness, stability, and transparency. To address these problems, we develop specialized algorithms that scale beyond the capabilities of off-the-shelf optimization software. Using mental telehealth treatment data from our industry collaborators at SilverCloud Health, we demonstrate how these methods enable practitioners to extract meaningful insights from complex datasets and predictive models.\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Frontiers and Applications at the Interface of Discrete Optimization and Interpretable Machine Learning"}],"uid":"34977","created_gmt":"2026-01-29 13:21:35","changed_gmt":"2026-02-03 14:06:53","author":"Julie Smith","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2026-02-12T11:00:00-05:00","event_time_end":"2026-02-12T12:00:00-05:00","event_time_end_last":"2026-02-12T12:00:00-05:00","gmt_time_start":"2026-02-12 16:00:00","gmt_time_end":"2026-02-12 17:00:00","gmt_time_end_last":"2026-02-12 17:00:00","rrule":null,"timezone":"America\/New_York"},"location":"ISyE Main 228","extras":[],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"177814","name":"Postdoc"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"687100":{"#nid":"687100","#data":{"type":"event","title":"BERD Forum - Artificial Intelligence in Medical and Healthcare Systems","body":[{"value":"\u003Cp\u003EJoin us for Artificial Intelligence in Medical and Health Care Systems on Friday, January 30, 2026, from 8:00 AM to 4:00 PM EST at the ISyE Main Building Atrium, Georgia Tech. This full-day event will feature two keynote speakers, seven technical presentations, and poster sessions, including a voluntary Best Student Poster Competition. Registration for the conference and poster session will open at the beginning of January, with a fee of $30 per person. The registration will cover breakfast, coffee during the breaks, lunch, and parking validation (please indicate the need for parking validation when registering).\u003C\/p\u003E\u003Cp\u003EDon\u2019t miss this opportunity to explore cutting-edge AI applications in Medical and Health Care Systems.\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u003Ca href=\u0022https:\/\/sites.gatech.edu\/ai-mhcs\/\u0022\u003Ehttps:\/\/sites.gatech.edu\/ai-mhcs\/\u003C\/a\u003E\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EJoin us for Artificial Intelligence in Medical and Health Care Systems on Friday, January 30, 2026, from 8:00 AM to 4:00 PM EST at the ISyE Main Building Atrium, Georgia Tech. This full-day event will feature two keynote speakers, seven technical presentations, and poster sessions, including a voluntary Best Student Poster Competition. Registration for the conference and poster session will open at the beginning of January, with a fee of $30 per person. The registration will cover breakfast, coffee during the breaks, lunch, and parking validation (please indicate the need for parking validation when registering).\u003C\/p\u003E\u003Cp\u003EDon\u2019t miss this opportunity to explore cutting-edge AI applications in Medical and Health Care Systems.\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"A biostatistics, epidemiology, and research design forum exploring the role of AI in medical and healthcare systems."}],"uid":"36374","created_gmt":"2026-01-07 14:08:19","changed_gmt":"2026-01-26 20:46:18","author":"mwelch39","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2026-01-30T08:00:00-05:00","event_time_end":"2026-01-30T16:00:00-05:00","event_time_end_last":"2026-01-30T16:00:00-05:00","gmt_time_start":"2026-01-30 13:00:00","gmt_time_end":"2026-01-30 21:00:00","gmt_time_end_last":"2026-01-30 21:00:00","rrule":null,"timezone":"America\/New_York"},"location":" ISYE Main Atrium","extras":[],"hg_media":{"678910":{"id":"678910","type":"image","title":"Biostatistics--epidemiology----Research-Design--5-.png","body":null,"created":"1767623902","gmt_created":"2026-01-05 14:38:22","changed":"1767623902","gmt_changed":"2026-01-05 14:38:22","alt":"BERD Forum Flyer ","file":{"fid":"263006","name":"Biostatistics--epidemiology----Research-Design--5-.png","image_path":"\/sites\/default\/files\/2026\/01\/05\/Biostatistics--epidemiology----Research-Design--5-.png","image_full_path":"http:\/\/hg.gatech.edu\/\/sites\/default\/files\/2026\/01\/05\/Biostatistics--epidemiology----Research-Design--5-.png","mime":"image\/png","size":638993,"path_740":"http:\/\/hg.gatech.edu\/sites\/default\/files\/styles\/740xx_scale\/public\/2026\/01\/05\/Biostatistics--epidemiology----Research-Design--5-.png?itok=dMeXeyxq"}}},"media_ids":["678910"],"related_links":[{"url":"https:\/\/sites.gatech.edu\/ai-mhcs\/","title":"Please register here"}],"groups":[{"id":"1250","name":"Center for Health and Humanitarian Systems (CHHS)"},{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[{"id":"187423","name":"go-bio"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"194683","name":"Talk"},{"id":"194682","name":"Workshop"},{"id":"10377","name":"Career\/Professional development"},{"id":"194613","name":"Industry"},{"id":"26411","name":"Training\/Workshop"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"177814","name":"Postdoc"},{"id":"78771","name":"Public"},{"id":"174045","name":"Graduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"687651":{"#nid":"687651","#data":{"type":"event","title":"CHHS Webinar Series: \u0022Reimagining Maternal Healthcare Delivery in the U.S.\u0022","body":[{"value":"\u003Cp\u003EMaternal mortality and morbidity in the United States remain among the highest of all high-income countries, revealing deep structural gaps in how maternal care is organized and delivered. Addressing these challenges requires systems-level improvements, as issues such as limited geographic access, safety concerns in low-volume hospitals, facility closures, and mismatches between patient risk and facility capability all interact to shape maternal outcomes. This talk highlights how mathematical modeling can help us understand these complex, interrelated system-level issues and support evidence-based strategies to strengthen maternal healthcare delivery.\u003C\/p\u003E\u003Cp\u003EFeaturing \u003Ca href=\u0022https:\/\/www.isye.gatech.edu\/users\/lauren-steimle\u0022\u003ELauren N. Steimle, PhD\u003C\/a\u003E, Assistant Professor, \u003Ca href=\u0022https:\/\/www.isye.gatech.edu\/\u0022\u003EH. Milton Stewart School of Industrial and Systems Engineering\u003C\/a\u003E, Georgia Tech \u003Ca href=\u0022https:\/\/chhs.gatech.edu\/\u0022\u003ECenter for Health and Humanitarian Systems\u003C\/a\u003E. Dr. Steimle\u0027s research involves the creation of industrial engineering and operations research (IE\/OR) methodologies to answer decision-making problems arising in public health and medicine. Her areas of research include medical decision-making, regionalized systems of healthcare delivery, and infectious disease prevention and control. Her work is motivated by addressing fundamental problems arising from challenges in maternal health, poliovirus, and chronic disease management, and COVID-19.\u003C\/p\u003E\u003Cp\u003EOffered online via Zoom. \u003Ca href=\u0022https:\/\/gatech.zoom.us\/webinar\/register\/WN_YFoBA_QpR0evbuINn3eO1Q\u0022\u003EPlease register to attend\u003C\/a\u003E.\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EThe talk will explore how mathematical modeling can address the structural failures and systemic gaps in U.S. maternal healthcare to develop evidence-based strategies for improving access, safety, and patient outcomes.\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Exploring how mathematical modeling can address the structural failures and systemic gaps in U.S. maternal healthcare."}],"uid":"27233","created_gmt":"2026-01-23 21:05:59","changed_gmt":"2026-01-23 22:07:43","author":"Andy Haleblian","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2026-01-29T10:00:00-05:00","event_time_end":"2026-01-29T11:00:00-05:00","event_time_end_last":"2026-01-29T11:00:00-05:00","gmt_time_start":"2026-01-29 15:00:00","gmt_time_end":"2026-01-29 16:00:00","gmt_time_end_last":"2026-01-29 16:00:00","rrule":null,"timezone":"America\/New_York"},"extras":[],"hg_media":{"679080":{"id":"679080","type":"image","title":"CHHS Webinar Series: \u0022Reimagining Maternal Healthcare Delivery in the U.S.\u0022","body":null,"created":"1769206017","gmt_created":"2026-01-23 22:06:57","changed":"1769206017","gmt_changed":"2026-01-23 22:06:57","alt":"CHHS Webinar Series: \u0022Reimagining Maternal Healthcare Delivery in the U.S.\u0022","file":{"fid":"263197","name":"20260129_CHHS-webinar_LSteimle.png","image_path":"\/sites\/default\/files\/2026\/01\/23\/20260129_CHHS-webinar_LSteimle.png","image_full_path":"http:\/\/hg.gatech.edu\/\/sites\/default\/files\/2026\/01\/23\/20260129_CHHS-webinar_LSteimle.png","mime":"image\/png","size":208123,"path_740":"http:\/\/hg.gatech.edu\/sites\/default\/files\/styles\/740xx_scale\/public\/2026\/01\/23\/20260129_CHHS-webinar_LSteimle.png?itok=QmqCHQm3"}}},"media_ids":["679080"],"related_links":[{"url":"https:\/\/gatech.zoom.us\/webinar\/register\/WN_YFoBA_QpR0evbuINn3eO1Q","title":"To attend, please register online via Zoom"}],"groups":[{"id":"1250","name":"Center for Health and Humanitarian Systems (CHHS)"},{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"},{"id":"1243","name":"The Supply Chain and Logistics Institute (SCL)"}],"categories":[],"keywords":[{"id":"184331","name":"access to healthcare"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"194684","name":"Free"},{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"177814","name":"Postdoc"},{"id":"78771","name":"Public"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"687233":{"#nid":"687233","#data":{"type":"event","title":"ISyE Seminar - Connor Lawless","body":[{"value":"\u003Ch3\u003ETitle:\u0026nbsp;\u003C\/h3\u003E\u003Cp\u003EBridging Machine Learning and Optimization for Human-Centered AI\u003C\/p\u003E\u003Ch3\u003EAbstract:\u0026nbsp;\u003C\/h3\u003E\u003Cp\u003EFrom healthcare delivery to resilient power grid management, predictive and prescriptive analytics tools have the potential to improve decision-making for some of today\u2019s most pressing problems, yet their impact is often limited by the technical barriers required to access these tools and to interpret and trust their results. This talk will explore how the synthesis of machine learning and optimization can lower these barriers to advance human-centered artificial intelligence (AI). The first part of the talk will demonstrate how generative AI can broaden access to optimization tools through an interactive decision-support framework, developed in collaboration with Microsoft Outlook, that leverages large language models to translate natural-language user requests into underlying constraint programming models. The second part of the talk will focus on trust, showing how optimization can identify regions where machine learning models make fixed predictions that preclude individuals from changing their outcomes, such as a loan applicant who can never be approved regardless of their actions. We will conclude by outlining broader opportunities for integrating AI and optimization, moving toward a future in which advanced analytics tools are as accessible and trustworthy for managers at a local food bank as they are for applied scientists at Amazon.\u003C\/p\u003E\u003Ch3\u003EBio:\u0026nbsp;\u003C\/h3\u003E\u003Cp\u003EConnor Lawless is a Postdoctoral Fellow at the Stanford Institute for Human-Centered Artificial Intelligence advised by Ellen Vitercik and Madeleine Udell. His research blends tools from optimization, machine learning, and human-computer interaction to make advanced analytics tools more accessible and trustworthy. He received his PhD in Operations Research from Cornell University where he was advised by Oktay Gunluk, and previously spent time at Microsoft Research, IBM Research, and the Royal Bank of Canada.\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Ch3\u003EAbstract:\u0026nbsp;\u003C\/h3\u003E\u003Cp\u003EFrom healthcare delivery to resilient power grid management, predictive and prescriptive analytics tools have the potential to improve decision-making for some of today\u2019s most pressing problems, yet their impact is often limited by the technical barriers required to access these tools and to interpret and trust their results. This talk will explore how the synthesis of machine learning and optimization can lower these barriers to advance human-centered artificial intelligence (AI). The first part of the talk will demonstrate how generative AI can broaden access to optimization tools through an interactive decision-support framework, developed in collaboration with Microsoft Outlook, that leverages large language models to translate natural-language user requests into underlying constraint programming models. The second part of the talk will focus on trust, showing how optimization can identify regions where machine learning models make fixed predictions that preclude individuals from changing their outcomes, such as a loan applicant who can never be approved regardless of their actions. We will conclude by outlining broader opportunities for integrating AI and optimization, moving toward a future in which advanced analytics tools are as accessible and trustworthy for managers at a local food bank as they are for applied scientists at Amazon.\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Bridging Machine Learning and Optimization for Human-Centered AI"}],"uid":"34977","created_gmt":"2026-01-13 14:06:30","changed_gmt":"2026-01-13 14:08:19","author":"Julie Smith","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2026-01-29T11:00:00-05:00","event_time_end":"2026-01-29T12:00:00-05:00","event_time_end_last":"2026-01-29T12:00:00-05:00","gmt_time_start":"2026-01-29 16:00:00","gmt_time_end":"2026-01-29 17:00:00","gmt_time_end_last":"2026-01-29 17:00:00","rrule":null,"timezone":"America\/New_York"},"location":"ISyE Groseclose 402","extras":[],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"177814","name":"Postdoc"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"687109":{"#nid":"687109","#data":{"type":"event","title":"SCL Lunch and Learn: \u0022Ahead of the Curve: Emergency Logistics\u0022","body":[{"value":"\u003Cp\u003E\u003Cem\u003E\u003Cstrong\u003EJoin SCL affiliated faculty member Mathieu Dahan as he shares insights from his research on how service systems and workforce models perform under stress, and how flexible capacity and smarter allocation policies can improve response outcomes.\u003C\/strong\u003E\u003C\/em\u003E\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EThursday, February 5, 2026 | 12-1pm ET\u003C\/strong\u003E\u003C\/p\u003E\u003Cdiv\u003E\u003Cdiv\u003E\u003Cdiv\u003E\u003Cdiv\u003E\u003Cdiv\u003E\u003Cp\u003EEmergencies push supply chains into conditions they were never designed for. Demand surges, resources become scarce, and leaders must make fast decisions with limited information. Dr. Mathieu Dahan will share insights from his research on how service systems and workforce models perform under stress, and how flexible capacity and smarter allocation policies can improve response outcomes. The session is designed for leaders responsible for continuity planning, public-private coordination, and operational risk, offering a clear view of what more resilient response systems can look like.\u003C\/p\u003E\u003C\/div\u003E\u003C\/div\u003E\u003C\/div\u003E\u003C\/div\u003E\u003C\/div\u003E\u003Cp\u003E\u003Ca href=\u0022https:\/\/gatech.zoom.us\/webinar\/register\/4617678864963\/WN_2zyxTb6aQT2tzqZJtgFkQg\u0022\u003E\u003Cstrong\u003ERegister Online to Attend\u003C\/strong\u003E\u003C\/a\u003E\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EEmergencies push supply chains into conditions they were never designed for. Demand surges, resources become scarce, and leaders must make fast decisions with limited information.\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Join SCL affiliated faculty member Mathieu Dahan as he shares insights from his research on how service systems and workforce models perform under stress, and how flexible capacity and smarter allocation policies can improve response outcomes. "}],"uid":"27233","created_gmt":"2026-01-08 17:29:54","changed_gmt":"2026-01-08 17:34:43","author":"Andy Haleblian","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2026-02-05T12:00:00-05:00","event_time_end":"2026-02-05T13:00:00-05:00","event_time_end_last":"2026-02-05T13:00:00-05:00","gmt_time_start":"2026-02-05 17:00:00","gmt_time_end":"2026-02-05 18:00:00","gmt_time_end_last":"2026-02-05 18:00:00","rrule":null,"timezone":"America\/New_York"},"location":"Online via Zoom","extras":[],"hg_media":{"678935":{"id":"678935","type":"image","title":"SCL Lunch and Learn: \u0022Ahead of the Curve: Emergency Logistics\u0022","body":null,"created":"1767893279","gmt_created":"2026-01-08 17:27:59","changed":"1767893332","gmt_changed":"2026-01-08 17:28:52","alt":"SCL Lunch and Learn: \u0022Ahead of the Curve: Emergency Logistics\u0022","file":{"fid":"263033","name":"hg_LNL_2026EmergeLog_20260205.png","image_path":"\/sites\/default\/files\/2026\/01\/08\/hg_LNL_2026EmergeLog_20260205.png","image_full_path":"http:\/\/hg.gatech.edu\/\/sites\/default\/files\/2026\/01\/08\/hg_LNL_2026EmergeLog_20260205.png","mime":"image\/png","size":167187,"path_740":"http:\/\/hg.gatech.edu\/sites\/default\/files\/styles\/740xx_scale\/public\/2026\/01\/08\/hg_LNL_2026EmergeLog_20260205.png?itok=w8YJCjA7"}}},"media_ids":["678935"],"related_links":[{"url":"https:\/\/gatech.zoom.us\/webinar\/register\/4617678864963\/WN_2zyxTb6aQT2tzqZJtgFkQg","title":"Register Online to Attend the Webinar"},{"url":"https:\/\/www.scl.gatech.edu\/education\/professional-education","title":"Professional Education at the Georgia Tech Supply Chain and Logistics Institute"}],"groups":[{"id":"1250","name":"Center for Health and Humanitarian Systems (CHHS)"},{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"},{"id":"1243","name":"The Supply Chain and Logistics Institute (SCL)"}],"categories":[],"keywords":[{"id":"3615","name":"information session"},{"id":"186857","name":"go-gtmi"},{"id":"122741","name":"physical internet"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"10377","name":"Career\/Professional development"}],"invited_audience":[{"id":"78771","name":"Public"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[{"value":"\u003Cp\u003Ecourse@scl.gatech.edu\u003C\/p\u003E","format":"limited_html"}],"email":[],"slides":[],"orientation":[],"userdata":""}},"687095":{"#nid":"687095","#data":{"type":"event","title":"Spring 2026 IISE Career Fair","body":[{"value":"\u003Ch3 dir=\u0022ltr\u0022\u003E\u003Cstrong\u003EAbout\u003C\/strong\u003E\u003C\/h3\u003E\u003Cp\u003EEvery fall and spring semester, during the IISE Career Fair, companies across the nation come to Georgia Tech to recruit some of the nation\u2019s top talent from our Bachelor\u2019s and Master\u2019s programs. Our students are recruited for a variety of roles and perform well past expectations in all positions. We hope that you will join us this semester and meet some of the country\u2019s brightest students. This event is open to all majors, and recruiting for roles in Data Analytics, Supply Chain, Consulting, Operations, Finance, Business, Tech \u0026amp; more.\u003C\/p\u003E\u003Ch3 dir=\u0022ltr\u0022\u003E\u003Cstrong\u003EEmployers\u003C\/strong\u003E\u003C\/h3\u003E\u003Cp dir=\u0022ltr\u0022\u003ERecruit from the\u0026nbsp;\u003Cstrong\u003E#1-Ranked Industrial Engineering Program\u0026nbsp;\u003C\/strong\u003Ein the nation at the IISE Career Fair.\u0026nbsp;\u003C\/p\u003E\u003Cul\u003E\u003Cli dir=\u0022ltr\u0022\u003E\u003Cstrong\u003ERegistration Deadline:\u003C\/strong\u003E\u0026nbsp; January 29, 2026\u003C\/li\u003E\u003Cli dir=\u0022ltr\u0022\u003E\u003Cstrong\u003EEmployer Registration Fee\u003C\/strong\u003E: $1000.00.\u003C\/li\u003E\u003Cli dir=\u0022ltr\u0022\u003E\u003Ca href=\u0022https:\/\/www.gtiise.org\/career-fair\u0022\u003E[Click Here to Register Today]\u003C\/a\u003E\u003C\/li\u003E\u003C\/ul\u003E\u003Ch3 dir=\u0022ltr\u0022\u003E\u003Cstrong\u003EStudents\u003C\/strong\u003E\u003C\/h3\u003E\u003Cp dir=\u0022ltr\u0022\u003EExplore\u0026nbsp;\u003Cstrong\u003Einternships\u003C\/strong\u003E and\u0026nbsp;\u003Cstrong\u003Efull-time opportunities\u003C\/strong\u003E with companies recruiting across Industrial \u0026amp; Systems Engineering and related fields. For more info \u0026amp; resume submission, visit \u003Ca href=\u0022https:\/\/www.gtiise.org\/career-fair-info\u0022\u003Ehttps:\/\/www.gtiise.org\/career-fair-info\u003C\/a\u003E\u003C\/p\u003E\u003Cp dir=\u0022ltr\u0022\u003ESecure Your Spot Early for the Spring 2026 Career Fair! For questions, email\u0026nbsp;\u003Ca href=\u0022mailto:iise@gatech.edu\u0022\u003Eiise@gatech.edu\u003C\/a\u003E.\u0026nbsp;\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EJoin us at one of the largest Industrial \u0026amp; Systems Engineering career fairs in the nation. Registration closes\u0026nbsp;Thursday, January 29, so secure your table today!\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Spring 2026 IISE Career Fair will take place Wednesday, February 4, at GT Exhibition Hall (9am - 2pm)."}],"uid":"36736","created_gmt":"2026-01-06 19:03:13","changed_gmt":"2026-01-06 19:16:05","author":"ebrown386","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2026-02-04T09:00:00-05:00","event_time_end":"2026-02-04T14:00:00-05:00","event_time_end_last":"2026-02-04T14:00:00-05:00","gmt_time_start":"2026-02-04 14:00:00","gmt_time_end":"2026-02-04 19:00:00","gmt_time_end_last":"2026-02-04 19:00:00","rrule":null,"timezone":"America\/New_York"},"location":"Georgia Tech Exhibition Hall (On-Campus)","extras":[],"hg_media":{"678931":{"id":"678931","type":"image","title":"IISE Spring 2026 Career Fair","body":null,"created":"1767726664","gmt_created":"2026-01-06 19:11:04","changed":"1767726664","gmt_changed":"2026-01-06 19:11:04","alt":"IISE Spring 2026 Career Fair","file":{"fid":"263028","name":"IISE-Career-Fair-SP26.jpg","image_path":"\/sites\/default\/files\/2026\/01\/06\/IISE-Career-Fair-SP26.jpg","image_full_path":"http:\/\/hg.gatech.edu\/\/sites\/default\/files\/2026\/01\/06\/IISE-Career-Fair-SP26.jpg","mime":"image\/jpeg","size":64056,"path_740":"http:\/\/hg.gatech.edu\/sites\/default\/files\/styles\/740xx_scale\/public\/2026\/01\/06\/IISE-Career-Fair-SP26.jpg?itok=TrR6rFno"}}},"media_ids":["678931"],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"10377","name":"Career\/Professional development"},{"id":"1791","name":"Student sponsored"}],"invited_audience":[{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[{"value":"\u003Cp\u003EFor questions, email\u0026nbsp;\u003Ca href=\u0022mailto:iise@gatech.edu\u0022\u003Eiise@gatech.edu\u003C\/a\u003E\u003C\/p\u003E","format":"limited_html"}],"email":[],"slides":[],"orientation":[],"userdata":""}},"687085":{"#nid":"687085","#data":{"type":"event","title":"ISyE Seminar - Isaac Gibbs","body":[{"value":"\u003Ch3\u003ETitle:\u0026nbsp;\u003C\/h3\u003E\u003Cp\u003EUncertainty quantification for black-box models with conditional guarantees\u003C\/p\u003E\u003Ch3\u003EAbstract:\u0026nbsp;\u003C\/h3\u003E\u003Cp\u003EA central problem in uncertainty quantification is designing methods that are both distribution-free and individualized to the test sample at hand. Prior work has shown that it is impossible to achieve finite-sample conditional validity without modelling assumptions. Thus, canonical methods in the conformal inference literature typically only issue marginal guarantees over a random draw of the test covariates. In this talk, I will outline a framework that bridges this gap by recasting the conditional objective as a set of robustness criteria under covariate shifts. By modifying the target class of covariate shifts, I will define a spectrum of problems that range between marginal and exact instance-wise validity and give methods that provide precise guarantees in between these extremes. This framework has broad applications and I will show how it can be used to construct prediction sets around the outputs of black-box regression models and filter out false information from the responses of large language models. This talk is based on joint work with John Cherian and Emmanuel Cand\u00e8s.\u003C\/p\u003E\u003Ch3\u003EBio:\u0026nbsp;\u003C\/h3\u003E\u003Cp\u003EIsaac Gibbs is a postdoctoral researcher at the University of California, Berkeley, where he is advised by Ryan Tibshirani. He received his Ph.D. in Statistics from Stanford University, advised by Emmanuel Cand\u00e8s, and his B.Sc. in Math and Computer Science from McGill University. His research focuses on topics related to predictive inference, distribution-free uncertainty quantification, online learning, and high-dimensional statistics.\u0026nbsp;\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Ch3\u003EAbstract:\u0026nbsp;\u003C\/h3\u003E\u003Cp\u003EA central problem in uncertainty quantification is designing methods that are both distribution-free and individualized to the test sample at hand. Prior work has shown that it is impossible to achieve finite-sample conditional validity without modelling assumptions. Thus, canonical methods in the conformal inference literature typically only issue marginal guarantees over a random draw of the test covariates. In this talk, I will outline a framework that bridges this gap by recasting the conditional objective as a set of robustness criteria under covariate shifts. By modifying the target class of covariate shifts, I will define a spectrum of problems that range between marginal and exact instance-wise validity and give methods that provide precise guarantees in between these extremes. This framework has broad applications and I will show how it can be used to construct prediction sets around the outputs of black-box regression models and filter out false information from the responses of large language models. This talk is based on joint work with John Cherian and Emmanuel Cand\u00e8s.\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Uncertainty quantification for black-box models with conditional guarantees"}],"uid":"34977","created_gmt":"2026-01-06 14:21:35","changed_gmt":"2026-01-06 14:23:23","author":"Julie Smith","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2026-01-15T11:00:00-05:00","event_time_end":"2026-01-15T12:00:00-05:00","event_time_end_last":"2026-01-15T12:00:00-05:00","gmt_time_start":"2026-01-15 16:00:00","gmt_time_end":"2026-01-15 17:00:00","gmt_time_end_last":"2026-01-15 17:00:00","rrule":null,"timezone":"America\/New_York"},"location":"ISyE Groseclose 402","extras":[],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"177814","name":"Postdoc"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"687084":{"#nid":"687084","#data":{"type":"event","title":"ISyE Seminar - Jinwen Yang","body":[{"value":"\u003Ch3\u003E\u003Cstrong\u003ETitle:\u003C\/strong\u003E\u0026nbsp;\u003C\/h3\u003E\u003Cp dir=\u0022ltr\u0022\u003EGPU-Accelerated Linear Programming and Beyond\u003C\/p\u003E\u003Ch3\u003E\u003Cstrong\u003EAbstract:\u003C\/strong\u003E\u0026nbsp;\u003C\/h3\u003E\u003Cp dir=\u0022ltr\u0022\u003EThe rapid progress in GPU computing has revolutionized many fields, yet its potential in mathematical programming, such as linear programming (LP), has only recently begun to be realized. This talk aims to provide an overview of recent advancements in GPU-based first-order methods for LP, with a particular focus on the design and development of cuPDLPx. The extensions to GPU-based optimization beyond LP, including convex quadratic programming and semidefinite programming, will also be discussed.\u003C\/p\u003E\u003Ch3\u003E\u003Cstrong\u003EBio:\u003C\/strong\u003E\u003C\/h3\u003E\u003Cp dir=\u0022ltr\u0022\u003E\u0026nbsp;Jinwen Yang is a final-year Ph.D. student at the University of Chicago, advised by Professor Haihao Lu. His research interests are in optimization, with a particular focus on optimization algorithms tailored to modern hardware (like GPUs) and intended for practical applications. He obtained B.S. in Mathematics and Applied Mathematics from Fudan University.\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Ch3\u003E\u003Cstrong\u003EAbstract:\u003C\/strong\u003E\u0026nbsp;\u003C\/h3\u003E\u003Cp dir=\u0022ltr\u0022\u003EThe rapid progress in GPU computing has revolutionized many fields, yet its potential in mathematical programming, such as linear programming (LP), has only recently begun to be realized. This talk aims to provide an overview of recent advancements in GPU-based first-order methods for LP, with a particular focus on the design and development of cuPDLPx. The extensions to GPU-based optimization beyond LP, including convex quadratic programming and semidefinite programming, will also be discussed.\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"GPU-Accelerated Linear Programming and Beyond"}],"uid":"34977","created_gmt":"2026-01-06 14:16:18","changed_gmt":"2026-01-06 14:19:00","author":"Julie Smith","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2026-01-27T11:00:00-05:00","event_time_end":"2026-01-27T12:00:00-05:00","event_time_end_last":"2026-01-27T12:00:00-05:00","gmt_time_start":"2026-01-27 16:00:00","gmt_time_end":"2026-01-27 17:00:00","gmt_time_end_last":"2026-01-27 17:00:00","rrule":null,"timezone":"America\/New_York"},"location":"ISyE Groseclose 402","extras":[],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"177814","name":"Postdoc"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"687083":{"#nid":"687083","#data":{"type":"event","title":"ISyE Seminar - Anthony Cheng","body":[{"value":"\u003Ch3\u003E\u003Cstrong\u003ETitle:\u003C\/strong\u003E\u0026nbsp;\u003C\/h3\u003E\u003Cdiv\u003ESystemic Consequences of Technology Choice in Clean Energy Supply Chains:\u0026nbsp;Vulnerability and Competitiveness in Battery Critical Minerals\u003Cbr\u003E\u0026nbsp;\u003C\/div\u003E\u003Ch3\u003E\u003Cstrong\u003EAbstract:\u0026nbsp;\u003C\/strong\u003E\u003C\/h3\u003E\u003Cdiv\u003EAchieving large-scale energy transitions requires rapid deployment of clean energy technologies, which in turn depend on interconnected manufacturing and supply chain processes. While prior work has examined resource extraction and downstream product manufacturing in detail, less attention has been paid to the intermediate stages of materials refining and component manufacturing,\u0026nbsp;where technology choices propagate across the supply chain and shape the range of possible system trajectories.\u0026nbsp;\u003Cbr\u003EIn this talk, I present an integrated modeling framework that\u0026nbsp;explores these intermediate supply chains in the context of electric vehicle batteries and their associated critical minerals, linking technology-level decisions to system-level outcomes, such as supply chain vulnerability, economic competitiveness, and emissions. More broadly, this work provides a pathway for incorporating technology and materials details into systems-level energy and climate analyses, supporting more robust decision-making in energy transitions.\u003C\/div\u003E\u003Cdiv\u003E\u0026nbsp;\u003C\/div\u003E\u003Ch3\u003E\u003Cstrong\u003EBio:\u003C\/strong\u003E\u003C\/h3\u003E\u003Cdiv\u003EAnthony Cheng is a PhD Candidate (ABD) in the Department of Engineering and Public Policy at Carnegie Mellon University. His work examines how technology, supply chain, and policy choices shape the competitiveness and resilience of clean energy and critical mineral\u0026nbsp;systems, with\u0026nbsp;a current focus on electric vehicle batteries. Methodologically, his research develops and applies integrated technoeconomic, environmental, and supply chain modeling frameworks to connect process-level technology characteristics to macro-energy systems and policy decision-making.\u0026nbsp;\u003C\/div\u003E\u003Cdiv\u003EHe is a National Science Foundation Graduate Research Fellow and holds an S.B. in Materials Science and Engineering from MIT, with additional training in computer science, energy systems, and entrepreneurship. Prior to his doctoral work, he engaged in both research and industry work spanning industrial decarbonization, data science, and clean technology commercialization.\u003C\/div\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Ch3\u003E\u003Cstrong\u003EAbstract:\u0026nbsp;\u003C\/strong\u003E\u003C\/h3\u003E\u003Cdiv\u003EAchieving large-scale energy transitions requires rapid deployment of clean energy technologies, which in turn depend on interconnected manufacturing and supply chain processes. While prior work has examined resource extraction and downstream product manufacturing in detail, less attention has been paid to the intermediate stages of materials refining and component manufacturing,\u0026nbsp;where technology choices propagate across the supply chain and shape the range of possible system trajectories.\u0026nbsp;\u003Cbr\u003EIn this talk, I present an integrated modeling framework that\u0026nbsp;explores these intermediate supply chains in the context of electric vehicle batteries and their associated critical minerals, linking technology-level decisions to system-level outcomes, such as supply chain vulnerability, economic competitiveness, and emissions. More broadly, this work provides a pathway for incorporating technology and materials details into systems-level energy and climate analyses, supporting more robust decision-making in energy transitions.\u003C\/div\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Systemic Consequences of Technology Choice in Clean Energy Supply Chains:\u00a0Vulnerability and Competitiveness in Battery Critical Minerals"}],"uid":"34977","created_gmt":"2026-01-06 14:05:54","changed_gmt":"2026-01-06 14:09:09","author":"Julie Smith","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2026-01-22T11:00:00-05:00","event_time_end":"2026-01-22T12:00:00-05:00","event_time_end_last":"2026-01-22T12:00:00-05:00","gmt_time_start":"2026-01-22 16:00:00","gmt_time_end":"2026-01-22 17:00:00","gmt_time_end_last":"2026-01-22 17:00:00","rrule":null,"timezone":"America\/New_York"},"location":"ISyE Groseclose 402","extras":[],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"177814","name":"Postdoc"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"687052":{"#nid":"687052","#data":{"type":"event","title":"BERD Forum - Artificial Intelligence in Medical and Healthcare Systems","body":[{"value":"\u003Cp\u003EJoin us for Artificial Intelligence in Medical and Health Care Systems on Friday, January 30, 2026, from 8:00 AM to 4:00 PM EST at the ISyE Main Building Atrium, Georgia Tech. This full-day event will feature two keynote speakers, seven technical presentations, and poster sessions, including a voluntary Best Student Poster Competition. Registration for the conference and poster session will open at the beginning of January, with a fee of $30 per person. The registration will cover breakfast, coffee during the breaks, lunch, and parking validation (please indicate the need for parking validation when registering).\u003C\/p\u003E\u003Cp\u003EDon\u2019t miss this opportunity to explore cutting-edge AI applications in Medical and Health Care Systems.\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EJoin us for Artificial Intelligence in Medical and Health Care Systems on Friday, January 30, 2026, from 8:00 AM to 4:00 PM EST at the ISyE Main Building Atrium, Georgia Tech. This full-day event will feature two keynote speakers, seven technical presentations, and poster sessions, including a voluntary Best Student Poster Competition. Registration for the conference and poster session will open at the beginning of January, with a fee of $30 per person. The registration will cover breakfast, coffee during the breaks, lunch, and parking validation (please indicate the need for parking validation when registering).\u003C\/p\u003E\u003Cp\u003EDon\u2019t miss this opportunity to explore cutting-edge AI applications in Medical and Health Care Systems.\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"A biostatistics, epidemiology, and research design forum exploring the role of AI in medical and healthcare systems."}],"uid":"36374","created_gmt":"2026-01-05 14:35:46","changed_gmt":"2026-01-05 14:41:44","author":"mwelch39","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2026-01-30T08:00:00-05:00","event_time_end":"2026-01-30T16:00:00-05:00","event_time_end_last":"2026-01-30T16:00:00-05:00","gmt_time_start":"2026-01-30 13:00:00","gmt_time_end":"2026-01-30 21:00:00","gmt_time_end_last":"2026-01-30 21:00:00","rrule":null,"timezone":"America\/New_York"},"location":" ISYE Main Atrium","extras":[],"hg_media":{"678910":{"id":"678910","type":"image","title":"Biostatistics--epidemiology----Research-Design--5-.png","body":null,"created":"1767623902","gmt_created":"2026-01-05 14:38:22","changed":"1767623902","gmt_changed":"2026-01-05 14:38:22","alt":"BERD Forum Flyer ","file":{"fid":"263006","name":"Biostatistics--epidemiology----Research-Design--5-.png","image_path":"\/sites\/default\/files\/2026\/01\/05\/Biostatistics--epidemiology----Research-Design--5-.png","image_full_path":"http:\/\/hg.gatech.edu\/\/sites\/default\/files\/2026\/01\/05\/Biostatistics--epidemiology----Research-Design--5-.png","mime":"image\/png","size":638993,"path_740":"http:\/\/hg.gatech.edu\/sites\/default\/files\/styles\/740xx_scale\/public\/2026\/01\/05\/Biostatistics--epidemiology----Research-Design--5-.png?itok=dMeXeyxq"}}},"media_ids":["678910"],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"194683","name":"Talk"},{"id":"194682","name":"Workshop"},{"id":"10377","name":"Career\/Professional development"},{"id":"194613","name":"Industry"},{"id":"26411","name":"Training\/Workshop"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"177814","name":"Postdoc"},{"id":"78771","name":"Public"},{"id":"174045","name":"Graduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"686981":{"#nid":"686981","#data":{"type":"event","title":"Health Systems Day","body":[{"value":"\u003Cp\u003ERegister at: \u003Ca href=\u0022https:\/\/forms.office.com\/r\/EL5qxHYmMQ\u0022 rel=\u0022noopener noreferrer\u0022 target=\u0022_blank\u0022 title=\u0022https:\/\/forms.office.com\/r\/EL5qxHYmMQ\u0022\u003EEvent Interest Form\u003C\/a\u003E\u0026nbsp;to receive more event details\u003C\/p\u003E\u003Cp\u003ESubscribe to our newsletter: \u003Ca href=\u0022https:\/\/app.e2ma.net\/app2\/audience\/signup\/2019066\/1986058.558972426\/\u0022 rel=\u0022noopener noreferrer\u0022 target=\u0022_blank\u0022 title=\u0022https:\/\/app.e2ma.net\/app2\/audience\/signup\/2019066\/1986058.558972426\/\u0022\u003ECHHS Newsletter\u003C\/a\u003E\u003C\/p\u003E\u003Cp\u003ELearn more about CHHS: \u003Ca href=\u0022https:\/\/chhs.gatech.edu\/\u0022 rel=\u0022noopener noreferrer\u0022 target=\u0022_blank\u0022 title=\u0022https:\/\/chhs.gatech.edu\/\u0022\u003ECenter for Health and Humanitarian Systems\u003C\/a\u003E\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EThe Georgia Tech Center for Health and Humanitarian Systems (CHHS), an Interdisciplinary Research Center (IRC), is dedicated to transforming health and humanitarian systems on a global scale through education, outreach efforts, and the development of innovative solutions.\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Health Systems: The Next Generation"}],"uid":"34977","created_gmt":"2025-12-18 12:46:12","changed_gmt":"2025-12-23 15:15:02","author":"Julie Smith","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2026-02-18T10:00:00-05:00","event_time_end":"2026-02-18T16:15:00-05:00","event_time_end_last":"2026-02-18T16:15:00-05:00","gmt_time_start":"2026-02-18 15:00:00","gmt_time_end":"2026-02-18 21:15:00","gmt_time_end_last":"2026-02-18 21:15:00","rrule":null,"timezone":"America\/New_York"},"location":"ISyE Main Atrium","extras":[],"groups":[{"id":"1250","name":"Center for Health and Humanitarian Systems (CHHS)"},{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"},{"id":"1243","name":"The Supply Chain and Logistics Institute (SCL)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"194682","name":"Workshop"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"177814","name":"Postdoc"},{"id":"78771","name":"Public"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"683182":{"#nid":"683182","#data":{"type":"event","title":"SCL Course: Generative AI Application for Supply Chain Professionals (Virtual\/Instructor-led)","body":[{"value":"\u003Ch3\u003E\u003Cstrong\u003ECourse Description\u003C\/strong\u003E\u003C\/h3\u003E\u003Cp\u003EThis course provides a deep dive into the ways in which artificial intelligence (AI) optimizes supply chain efficiency. Participants will explore generative AI fundamentals, prompt engineering, and practical applications such as automated inventory systems, predictive maintenance, and route optimization. The course also covers ethical AI use, good and bad use of generative AI (GenAI), and rapidly emerging use cases. By the end, professionals will be skilled in applying AI to enhance supply chain processes and drive success in their organizations.\u003C\/p\u003E\u003Ch3\u003E\u003Cstrong\u003EWho Should Attend\u003C\/strong\u003E\u003C\/h3\u003E\u003Cp\u003EThis course targets supply chain managers, data analysts, logistics professionals, procurement specialists, and business leaders aiming to harness GenAI for enhanced supply chain operations. It is ideal for those interested in GenAI-driven efficiency, strategic insights, and navigation of GenAI\u0027s role in transforming supply chain processes.\u003C\/p\u003E\u003Ch3\u003E\u003Cstrong\u003EHow You Will Benefit\u003C\/strong\u003E\u003C\/h3\u003E\u003Cul\u003E\u003Cli\u003EEnhance decision-making capabilities through GenAI-driven insights to optimize processes and boost efficiency.\u003C\/li\u003E\u003Cli\u003EAcquire practical skills in prompt engineering and the use of generative AI models.\u003C\/li\u003E\u003Cli\u003EExplore practical use cases that can be reapplied.\u003C\/li\u003E\u003Cli\u003ELearn about good and bad use of GenAI for individuals, teams, and organizations.\u003C\/li\u003E\u003Cli\u003EBecome better equipped to effectively harness GenAI capabilities in supply chain activities and planning.\u003C\/li\u003E\u003C\/ul\u003E\u003Ch3\u003E\u003Cstrong\u003EWhat Is Covered\u003C\/strong\u003E\u003C\/h3\u003E\u003Cul\u003E\u003Cli\u003EFoundational understanding of using GenAI in supply chain management\u003C\/li\u003E\u003Cli\u003EBasics of GenAI\u003C\/li\u003E\u003Cli\u003ECrafting effective AI prompts and their applications in optimizing warehouse layouts\u003C\/li\u003E\u003Cli\u003EPredictive maintenance and supplier selection\u003C\/li\u003E\u003Cli\u003EElimination of redundant tasks through AI\u003C\/li\u003E\u003Cli\u003EEthical considerations, risk assessments, and strategy for AI adoption\u003C\/li\u003E\u003Cli\u003EPractical strategies and real-world examples for implementing AI solutions effectively and making informed decisions\u003C\/li\u003E\u003C\/ul\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EParticipants will explore generative AI fundamentals, prompt engineering, and practical applications such as automated inventory systems, predictive maintenance, and route optimization.\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Participants will explore generative AI fundamentals, prompt engineering, and practical applications such as automated inventory systems, predictive maintenance, and route optimization."}],"uid":"27233","created_gmt":"2025-07-18 18:41:48","changed_gmt":"2025-12-10 23:29:14","author":"Andy Haleblian","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2026-03-23T20:00:00-04:00","event_time_end":"2026-03-25T16:00:00-04:00","event_time_end_last":"2026-03-25T16:00:00-04:00","gmt_time_start":"2026-03-24 00:00:00","gmt_time_end":"2026-03-25 20:00:00","gmt_time_end_last":"2026-03-25 20:00:00","rrule":null,"timezone":"America\/New_York"},"location":"Virtual\/Instructor-led","extras":[],"related_links":[{"url":"https:\/\/www.scl.gatech.edu\/education\/professional-education\/course\/gaiascp","title":"Course webpage within the SCL website"}],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"},{"id":"1243","name":"The Supply Chain and Logistics Institute (SCL)"}],"categories":[],"keywords":[{"id":"192390","name":"generative AI"},{"id":"170001","name":"Supply Chain Engineering"},{"id":"167074","name":"Supply Chain"},{"id":"122741","name":"physical internet"},{"id":"186857","name":"go-gtmi"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"10377","name":"Career\/Professional development"}],"invited_audience":[{"id":"78771","name":"Public"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[{"value":"\u003Cp\u003E\u003Ca href=\u0022mailto:info@scl.gatech.edu\u0022\u003Einfo@scl.gatech.edu\u003C\/a\u003E\u003C\/p\u003E","format":"limited_html"}],"email":[],"slides":[],"orientation":[],"userdata":""}},"685777":{"#nid":"685777","#data":{"type":"event","title":"Professional Education Course: Inventory Management and Resource Allocation in Supply Chains (Virtual\/Instructor-led)","body":[{"value":"\u003Cp\u003EClasses\u0026nbsp;will be taught by LIVE video instruction similar to the experience you would receive in person with the same interactive components.\u0026nbsp;Each course will run for one week Monday through Thursday from 10am to 1:30pm ET each day with an optional extra day on Friday.\u003C\/p\u003E\u003Ch3\u003E\u003Cstrong\u003ECourse Description\u003C\/strong\u003E\u003C\/h3\u003E\u003Cp\u003EMany Supply Chain decisions are concerned with the timely and efficient procurement, allocation, and distribution of resources (e.g. funds, supplies, volunteers, money, employees) through a supply chain network. This course will explore methodologies for \u201cmedium term\u201d decision making including procurement and inventory policies, strategies for distribution and allocation of limited resources, and supply chain design.\u003C\/p\u003E\u003Ch3\u003E\u003Cstrong\u003EWho Should Attend\u003C\/strong\u003E\u003C\/h3\u003E\u003Cp\u003EThis course is designed for representatives from governmental or non-governmental organizations, private corporations, military, and foundations, including but not limited to senior executives overseeing administrative and operational functions of an organization, logistics and supply chain managers, program managers, directors of field operations, directors of emergency\/disaster preparedness and response, and public health professionals.\u003C\/p\u003E\u003Ch3\u003E\u003Cstrong\u003EHow You Will Benefit\u003C\/strong\u003E\u003C\/h3\u003E\u003Cul\u003E\u003Cli\u003EProvide immediate impact to your organization through applied and real-world case studies.\u003C\/li\u003E\u003Cli\u003ELearn to bring NGOs, businesses, or government entities together to enhance collaboration, cooperation, and communication.\u003C\/li\u003E\u003Cli\u003EDiscover current trends and procedures to help your organization and team members get and stay ahead of the curve.\u003C\/li\u003E\u003C\/ul\u003E\u003Ch3\u003E\u003Cstrong\u003EWhat Is Covered\u003C\/strong\u003E\u003C\/h3\u003E\u003Cul\u003E\u003Cli\u003EProcurement decisions\u003C\/li\u003E\u003Cli\u003EInventory management techniques for a single event versus ongoing operations under uncertainty\u003C\/li\u003E\u003Cli\u003EStrategies for resource allocation geographically and over time\u003C\/li\u003E\u003C\/ul\u003E\u003Ch3\u003E\u003Cstrong\u003EAbout the Course and the\u0026nbsp;HHSCM Course Series\u003C\/strong\u003E\u003C\/h3\u003E\u003Cp\u003EThis course is the second in a 3-part virtually synchronous professional education program. Register and pay for all three required\u0026nbsp;\u003Ca href=\u0022https:\/\/pe.gatech.edu\/node\/54\u0022\u003EHealth and Humanitarian Supply Chain Management Certificate courses\u003C\/a\u003E\u0026nbsp;and receive a discount of $400 off per course. Enter coupon code\u0026nbsp;\u003Cstrong\u003ESCL-HHS\u003C\/strong\u003E\u0026nbsp;at checkout with the Georgia Tech Professional Education website..\u0026nbsp;\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EAdditionally, there are scholarships available for the certificate program. Apply at \u003Ca href=\u0022https:\/\/chhs.gatech.edu\/course-scholarships\u0022\u003Ehttps:\/\/chhs.gatech.edu\/course-scholarships\u003C\/a\u003E by the noted deadline.\u0026nbsp;\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EQuestions? Reach out to\u0026nbsp;\u003Ca href=\u0022mailto:chhs@gatech.edu\u0022\u003Echhs@gatech.edu\u003C\/a\u003E!\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EThis course explores methodologies for tactical decision making including procurement and inventory policies, strategies for distribution and allocation of limited resources, and transportation decisions.\u003C\/p\u003E\r\n","format":"limited_html"}],"field_summary_sentence":[{"value":"Inventory availability is the most important aspect of customer service, and the cost of inventory is one of the most important entries on a company\u0027s balance sheet."}],"uid":"27233","created_gmt":"2025-10-17 14:51:42","changed_gmt":"2025-12-09 12:13:15","author":"Andy Haleblian","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2026-03-23T10:00:00-04:00","event_time_end":"2026-03-27T13:30:00-04:00","event_time_end_last":"2026-03-27T13:30:00-04:00","gmt_time_start":"2026-03-23 14:00:00","gmt_time_end":"2026-03-27 17:30:00","gmt_time_end_last":"2026-03-27 17:30:00","rrule":null,"timezone":"America\/New_York"},"location":"Virtual\/Instructor-led","extras":[],"related_links":[{"url":"https:\/\/pe.gatech.edu\/courses\/inventory-management-and-resource-allocation-supply-chains","title":"Registration link via Georgia Tech Professional Education"},{"url":"https:\/\/chhs.gatech.edu\/education\/professional-education\/course\/invmgmt","title":"Course Details via Center for Health and Humanitarian Systems website"},{"url":"https:\/\/pe.gatech.edu\/certificates\/health-humanitarian-supply-chain-management-certificate","title":"Health \u0026 Humanitarian Supply Chain Management Certificate"},{"url":"https:\/\/chhs.gatech.edu\/course-scholarships","title":"Apply for a Scholarship!"}],"groups":[{"id":"1250","name":"Center for Health and Humanitarian Systems (CHHS)"},{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"},{"id":"1243","name":"The Supply Chain and Logistics Institute (SCL)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"10377","name":"Career\/Professional development"}],"invited_audience":[{"id":"78771","name":"Public"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[{"value":"\u003Cp\u003Echhs@gatech.edu\u0026nbsp;\u003C\/p\u003E","format":"limited_html"}],"email":[],"slides":[],"orientation":[],"userdata":""}},"686763":{"#nid":"686763","#data":{"type":"event","title":"AI for Students Lunch and Learn","body":[{"value":"\u003Ch2 dir=\u0022ltr\u0022\u003EAI for Students Lunch and Learn | Jan 20, 2026 (11-12:15pm)\u003C\/h2\u003E\u003Cdiv\u003E\u003Cp\u003EOpen to Georgia Tech students from all majors \u2014 ISyE, Business, CS, ECE, and beyond \u2014 to encourage broad participation and interdisciplinary exchange.\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EThe event is designed to bring together Georgia Tech students who are exploring AI in supply chain, logistics, and related fields\u003C\/strong\u003E. The goal is to share ideas, compare approaches, and build connections across majors and programs.\u003C\/p\u003E\u003Cp\u003EIf you\u2019ve used AI in any way\u2014whether for a class, research, or a side project\u2014we\u2019d love to hear from you. Presentations are brief and informal (3\u20135 minutes), and slides are optional. Your insights can spark great conversations!\u003C\/p\u003E\u003Cp\u003EWe will review registrations to ensure a good balance of presenters and listeners for an interactive session. If you\u2019re presenting, you\u2019ll share a short talk. If you\u2019re attending as a listener, we expect you to actively engage\u2014ask questions, share feedback, and join the discussion during networking. Your input helps make this session valuable for everyone!\u003C\/p\u003E\u003Cp\u003E\u003Ca href=\u0022https:\/\/eforms.scl.gatech.edu\/ai-student-exchange\u0022 rel=\u0022noreferrer noopener\u0022 target=\u0022_blank\u0022\u003E\u003Cstrong\u003EClick here to register Online to Attend\u003C\/strong\u003E\u003C\/a\u003E\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003C\/div\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003E\u003Cstrong\u003EAn event designed to bring together Georgia Tech students who are exploring AI in supply chain, logistics, and related fields\u003C\/strong\u003E. The goal is to share ideas, compare approaches, and build connections across majors and programs.\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"An event to bring together Georgia Tech students who are exploring and using AI in supply chain, logistics, and related fields."}],"uid":"27233","created_gmt":"2025-12-05 21:51:47","changed_gmt":"2025-12-05 21:58:01","author":"Andy Haleblian","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2026-01-20T11:00:00-05:00","event_time_end":"2026-01-20T12:15:00-05:00","event_time_end_last":"2026-01-20T12:15:00-05:00","gmt_time_start":"2026-01-20 16:00:00","gmt_time_end":"2026-01-20 17:15:00","gmt_time_end_last":"2026-01-20 17:15:00","rrule":null,"timezone":"America\/New_York"},"location":"Georgia Tech Exhibition Hall (Centennial Room - 2nd floor)","extras":["free_food"],"hg_media":{"678772":{"id":"678772","type":"image","title":"AI for Students Lunch and Learn","body":null,"created":"1764965081","gmt_created":"2025-12-05 20:04:41","changed":"1764965112","gmt_changed":"2025-12-05 20:05:12","alt":"AI for Students Lunch and Learn","file":{"fid":"262847","name":"AI-for-Students.jpg","image_path":"\/sites\/default\/files\/2025\/12\/05\/AI-for-Students.jpg","image_full_path":"http:\/\/hg.gatech.edu\/\/sites\/default\/files\/2025\/12\/05\/AI-for-Students.jpg","mime":"image\/jpeg","size":257519,"path_740":"http:\/\/hg.gatech.edu\/sites\/default\/files\/styles\/740xx_scale\/public\/2025\/12\/05\/AI-for-Students.jpg?itok=JbqPxwxm"}}},"media_ids":["678772"],"related_links":[{"url":"https:\/\/eforms.scl.gatech.edu\/ai-student-exchange","title":"Register Online to Attend"}],"groups":[{"id":"1250","name":"Center for Health and Humanitarian Systems (CHHS)"},{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"},{"id":"1243","name":"The Supply Chain and Logistics Institute (SCL)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"26411","name":"Training\/Workshop"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"177814","name":"Postdoc"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[{"value":"\u003Cp\u003Eevent@scl.gatech.edu\u003C\/p\u003E","format":"limited_html"}],"email":[],"slides":[],"orientation":[],"userdata":""}},"686701":{"#nid":"686701","#data":{"type":"event","title":"SCL Lunch and Learn: \u201cForecasting 2026: What\u2019s Next for Supply Chain\u0022","body":[{"value":"\u003Cp\u003E\u003Cem\u003E\u003Cstrong\u003EJoin SCL Managing Director Chris Gaffney for an insightful Lunch and Learn as he unpacks the biggest trends shaping supply chain in 2026.\u003C\/strong\u003E\u003C\/em\u003E\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EThursday, January 8, 2026 | 12-1pm ET\u003C\/strong\u003E\u003C\/p\u003E\u003Cdiv\u003E\u003Cdiv\u003E\u003Cdiv\u003E\u003Cdiv\u003E\u003Cdiv\u003E\u003Cp\u003EFrom disruptive technologies to shifting global dynamics, the supply chain landscape is evolving faster than ever. Whether you\u0027re navigating nearshoring strategies, exploring AI-driven logistics, or simply trying to keep pace with change, this session will help you spot what\u0027s coming and act before the competition does.\u003C\/p\u003E\u003C\/div\u003E\u003C\/div\u003E\u003C\/div\u003E\u003C\/div\u003E\u003C\/div\u003E\u003Cp\u003E\u003Ca href=\u0022https:\/\/gatech.zoom.us\/webinar\/register\/7117646884984\/WN_vZsL5sr4SFm6MpjRSLGp3g\u0022\u003E\u003Cstrong\u003ERegister Online to Attend\u003C\/strong\u003E\u003C\/a\u003E\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EFrom disruptive technologies to shifting global dynamics, the supply chain landscape is evolving faster than ever. Join SCL Managing Director Chris Gaffney for an insightful Lunch and Learn as he unpacks the biggest trends shaping supply chain in 2026.\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Join SCL Managing Director Chris Gaffney for an insightful Lunch and Learn as he unpacks the biggest trends shaping supply chain in 2026."}],"uid":"27233","created_gmt":"2025-12-02 16:15:33","changed_gmt":"2025-12-02 16:22:54","author":"Andy Haleblian","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2026-01-08T12:00:00-05:00","event_time_end":"2026-01-08T13:00:00-05:00","event_time_end_last":"2026-01-08T13:00:00-05:00","gmt_time_start":"2026-01-08 17:00:00","gmt_time_end":"2026-01-08 18:00:00","gmt_time_end_last":"2026-01-08 18:00:00","rrule":null,"timezone":"America\/New_York"},"location":"Online via Zoom","extras":[],"hg_media":{"678741":{"id":"678741","type":"image","title":"SCL Lunch and Learn: \u201cForecasting 2026: What\u2019s Next for Supply Chain\u0022","body":null,"created":"1764692303","gmt_created":"2025-12-02 16:18:23","changed":"1764692303","gmt_changed":"2025-12-02 16:18:23","alt":"SCL Lunch and Learn: \u201cForecasting 2026: What\u2019s Next for Supply Chain\u0022","file":{"fid":"262813","name":"hg_LNL_2026SC_20260108.png","image_path":"\/sites\/default\/files\/2025\/12\/02\/hg_LNL_2026SC_20260108.png","image_full_path":"http:\/\/hg.gatech.edu\/\/sites\/default\/files\/2025\/12\/02\/hg_LNL_2026SC_20260108.png","mime":"image\/png","size":162848,"path_740":"http:\/\/hg.gatech.edu\/sites\/default\/files\/styles\/740xx_scale\/public\/2025\/12\/02\/hg_LNL_2026SC_20260108.png?itok=Jz1ckD4Y"}}},"media_ids":["678741"],"related_links":[{"url":"https:\/\/gatech.zoom.us\/webinar\/register\/7117646884984\/WN_vZsL5sr4SFm6MpjRSLGp3g","title":"Register Online to Attend the Webinar"},{"url":"https:\/\/www.scl.gatech.edu\/education\/professional-education","title":"Professional Education at the Georgia Tech Supply Chain and Logistics Institute"}],"groups":[{"id":"1250","name":"Center for Health and Humanitarian Systems (CHHS)"},{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"},{"id":"1243","name":"The Supply Chain and Logistics Institute (SCL)"}],"categories":[],"keywords":[{"id":"3615","name":"information session"},{"id":"186857","name":"go-gtmi"},{"id":"122741","name":"physical internet"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"10377","name":"Career\/Professional development"}],"invited_audience":[{"id":"78771","name":"Public"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[{"value":"\u003Cp\u003Ecourse@scl.gatech.edu\u003C\/p\u003E","format":"limited_html"}],"email":[],"slides":[],"orientation":[],"userdata":""}},"686646":{"#nid":"686646","#data":{"type":"event","title":"ISyE Seminar - Tudor Manole","body":[{"value":"\u003Ch3\u003E\u003Cstrong\u003ETitle:\u0026nbsp;\u003C\/strong\u003E\u003C\/h3\u003E\u003Cp\u003EA Statistical Framework for Benchmarking Quantum Computers\u003C\/p\u003E\u003Ch3\u003E\u003Cstrong\u003EAbstract\u003C\/strong\u003E:\u0026nbsp;\u003C\/h3\u003E\u003Cdiv\u003EThe last two decades have witnessed quantum computing technologies increasingly move from theoretical proposals to functioning experimental platforms, reaching major milestones such as the demonstration of beyond-classical computational tasks. The central challenge in the sustained development of large-scale quantum computers is the presence of hardware errors, which must be identified and quantified before they can be mitigated. In this talk, I will develop a statistical perspective on this problem of benchmarking quantum devices, using an experimental platform known as random circuit sampling. Data arising from this experiment can be described through a high-dimensional discrete latent variable model parametrized by hardware-level error rates. We develop computationally efficient estimators for these error rates, which incorporate side information about the model via simulations from a reference quantum computer. These estimators achieve the information-theoretic estimation limits for this problem, implying that reliable estimation is possible even for large-scale quantum devices that evade classical computational abilities.\u0026nbsp;We apply our methods to benchmark a recent state-of-the-art quantum processor, obtaining a detailed report with hundreds of error rates which were largely unavailable from past studies. I will conclude by placing these results in the broader context of my interdisciplinary work in the physical sciences, and by discussing some of my other research interests in nonparametric statistics and statistical optimal transport.\u003C\/div\u003E\u003Cdiv\u003E\u0026nbsp;\u003C\/div\u003E\u003Ch3\u003E\u003Cstrong\u003EBio\u003C\/strong\u003E:\u0026nbsp;\u003C\/h3\u003E\u003Cdiv\u003ETudor Manole is a Norbert Wiener postdoctoral associate in the Statistics and Data Science Center at the Massachusetts Institute of Technology (MIT). He received his PhD in Statistics at Carnegie Mellon University, where he was advised by Larry Wasserman and Sivaraman Balakrishnan. He is a\u0026nbsp;recipient of the Umesh K. Gavaskar Memorial PhD Thesis Award, and the Lawrence D. Brown Ph.D. Student Award. His recent research interests include statistical optimal transport, latent variable models, nonparametric hypothesis testing, and their applications to the physical sciences, particularly in the areas of quantum computing and high energy physics.\u0026nbsp;\u003C\/div\u003E\u003Cdiv\u003E\u003Cbr\u003E\u0026nbsp;\u003C\/div\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Ch3\u003E\u003Cstrong\u003EAbstract\u003C\/strong\u003E:\u0026nbsp;\u003C\/h3\u003E\u003Cp\u003EThe last two decades have witnessed quantum computing technologies increasingly move from theoretical proposals to functioning experimental platforms, reaching major milestones such as the demonstration of beyond-classical computational tasks. The central challenge in the sustained development of large-scale quantum computers is the presence of hardware errors, which must be identified and quantified before they can be mitigated. In this talk, I will develop a statistical perspective on this problem of benchmarking quantum devices, using an experimental platform known as random circuit sampling. Data arising from this experiment can be described through a high-dimensional discrete latent variable model parametrized by hardware-level error rates. We develop computationally efficient estimators for these error rates, which incorporate side information about the model via simulations from a reference quantum computer. These estimators achieve the information-theoretic estimation limits for this problem, implying that reliable estimation is possible even for large-scale quantum devices that evade classical computational abilities.\u0026nbsp;We apply our methods to benchmark a recent state-of-the-art quantum processor, obtaining a detailed report with hundreds of error rates which were largely unavailable from past studies. I will conclude by placing these results in the broader context of my interdisciplinary work in the physical sciences, and by discussing some of my other research interests in nonparametric statistics and statistical optimal transport.\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"A Statistical Framework for Benchmarking Quantum Computers"}],"uid":"34977","created_gmt":"2025-12-01 13:08:52","changed_gmt":"2025-12-01 13:08:52","author":"Julie Smith","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2025-12-08T11:00:00-05:00","event_time_end":"2025-12-08T12:00:00-05:00","event_time_end_last":"2025-12-08T12:00:00-05:00","gmt_time_start":"2025-12-08 16:00:00","gmt_time_end":"2025-12-08 17:00:00","gmt_time_end_last":"2025-12-08 17:00:00","rrule":null,"timezone":"America\/New_York"},"location":"Groseclose 402","extras":[],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"177814","name":"Postdoc"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"686620":{"#nid":"686620","#data":{"type":"event","title":"ISyE Seminar - Emily Zhang","body":[{"value":"\u003Ch3\u003E\u003Cstrong\u003ETitle:\u003C\/strong\u003E\u0026nbsp;\u003C\/h3\u003E\u003Cp\u003EHeterogeneous Treatment Effects in Panel Data: Applications to the Healthy Incentives Program\u003C\/p\u003E\u003Ch3\u003E\u003Cstrong\u003EAbstract:\u003C\/strong\u003E\u003C\/h3\u003E\u003Cp\u003EIn this talk, we will discuss work motivated by studying the Healthy Incentives Program (HIP), a food-subsidy program. Our goal is to quantify how adding new vendors affects program utilization using observational panel data. In particular, the effects may be heterogeneous, and the timing of the interventions may be highly irregular. This is an instance of a core problem in causal inference: estimating heterogeneous treatment effects using panel data with general treatment patterns.\u003C\/p\u003E\u003Cp\u003ETo address this problem, we introduce the Panel Clustering Estimator (PaCE). PaCE partitions observations into clusters with similar treatment effects using a regression tree and leverages the low-rank structure of the panel data to estimate the average treatment effect within each cluster. Our theoretical results identify conditions on the treatment patterns under which the treatment effects are recoverable, and we establish convergence guarantees under those conditions. Computational experiments show that PaCE achieves higher accuracy than existing approaches while remaining interpretable. Applying PaCE to HIP data, we identify the heterogeneous impacts of vendor additions on HIP utilization across Massachusetts ZIP codes and uncover key demographic and contextual factors driving these differences. Our findings provide valuable insights for future budget planning and for identifying which ZIP codes to target with vendor additions.\u003C\/p\u003E\u003Ch3\u003E\u003Cstrong\u003EBio:\u003C\/strong\u003E\u003C\/h3\u003E\u003Cp\u003EEmily Zhang is a fifth-year PhD candidate at the MIT Operations Research Center, advised by Professors Retsef Levi and Georgia Perakis. Her research addresses critical challenges in food systems, focusing on reducing food waste and improving equitable access to healthy food. Her work spans modeling, optimization, causal inference, inventory management, and data-driven operations, and has been conducted in collaboration with the Massachusetts Department of Transitional Assistance and nonprofit organizations such as Met Council. Prior to her PhD, she earned dual B.S. degrees in Computer Science and Mathematics from MIT.\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Ch3\u003E\u003Cstrong\u003EAbstract:\u003C\/strong\u003E\u003C\/h3\u003E\u003Cp\u003EIn this talk, we will discuss work motivated by studying the Healthy Incentives Program (HIP), a food-subsidy program. Our goal is to quantify how adding new vendors affects program utilization using observational panel data. In particular, the effects may be heterogeneous, and the timing of the interventions may be highly irregular. This is an instance of a core problem in causal inference: estimating heterogeneous treatment effects using panel data with general treatment patterns.\u003C\/p\u003E\u003Cp\u003ETo address this problem, we introduce the Panel Clustering Estimator (PaCE). PaCE partitions observations into clusters with similar treatment effects using a regression tree and leverages the low-rank structure of the panel data to estimate the average treatment effect within each cluster. Our theoretical results identify conditions on the treatment patterns under which the treatment effects are recoverable, and we establish convergence guarantees under those conditions. Computational experiments show that PaCE achieves higher accuracy than existing approaches while remaining interpretable. Applying PaCE to HIP data, we identify the heterogeneous impacts of vendor additions on HIP utilization across Massachusetts ZIP codes and uncover key demographic and contextual factors driving these differences. Our findings provide valuable insights for future budget planning and for identifying which ZIP codes to target with vendor additions.\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Heterogeneous Treatment Effects in Panel Data: Applications to the Healthy Incentives Program"}],"uid":"34977","created_gmt":"2025-11-25 15:19:15","changed_gmt":"2025-11-25 15:21:57","author":"Julie Smith","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2025-12-11T11:00:00-05:00","event_time_end":"2025-12-11T12:00:00-05:00","event_time_end_last":"2025-12-11T12:00:00-05:00","gmt_time_start":"2025-12-11 16:00:00","gmt_time_end":"2025-12-11 17:00:00","gmt_time_end_last":"2025-12-11 17:00:00","rrule":null,"timezone":"America\/New_York"},"location":"Groseclose 402","extras":[],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"177814","name":"Postdoc"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"686569":{"#nid":"686569","#data":{"type":"event","title":"ISyE Seminar - Anna Gilbert ","body":[{"value":"\u003Cp\u003ETitle:\u003C\/p\u003E\u003Cp\u003ESeeing the Forest for the Trees\u003C\/p\u003E\u003Cp\u003EAbstract:\u003C\/p\u003E\u003Cp\u003ERecent papers in the graph machine learning literature have introduced a number of approaches for hyperbolic representation learning. The asserted benefits are improved performance on a variety of graph tasks, node classification and link prediction included. Claims have also been made about the geometric suitability of particular hierarchical graph datasets to representation in hyperbolic space. Despite these claims, our work makes a surprising discovery: when simple Euclidean models with comparable numbers of parameters are properly trained in the same environment, in most cases, they perform as well, if not better, than all introduced hyperbolic graph representation learning models, even on graph datasets previously claimed to be the most hyperbolic as measured by Gromov delta-hyperbolicity (i.e., perfect trees).\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EThis observation gives rise to a simple question: how can this be? We answer this question by taking a careful look at the field of hyperbolic graph representation learning as it stands today, and find that a number of results do not diligently present baselines, make faulty modelling assumptions when constructing algorithms, and use misleading metrics to quantify geometry of graph datasets. We take a closer look at each of these three problems, elucidate the issues, perform an analysis of methods, and introduce a parametric family of benchmark datasets to ascertain the applicability of (hyperbolic) graph neural networks.\u003C\/p\u003E\u003Cp\u003EUnfortunately, these problems are not specific to hyperbolic graph neural nets but are indicative of more fundamental problems in graph machine learning more generally. We show, surprisingly, that node features are oftentimes more-than-sufficient for many common graph benchmarks, breaking this critical assumption. When comparing against a well-tuned feature-only MLP baseline on seven of the most commonly used graph learning datasets, one gains little benefit from using graph structure on five datasets. We posit that these datasets do not benefit considerably from graph learning because the features themselves already contain enough graph information to obviate or substantially reduce the need for the graph.\u003C\/p\u003E\u003Cp\u003EBio:\u003C\/p\u003E\u003Cp\u003EAnna Gilbert received her S.B. degree from the University of Chicago and a Ph.D. from Princeton University, both in Mathematics. In 1997, she was a postdoctoral fellow at Yale University and AT\u0026amp;T Labs-Research. From 1998 to 2004, she was a member of technical staff at AT\u0026amp;T Labs-Research in Florham Park, NJ. From 2004 to 2020, she was with the Department of Mathematics (with a secondary appointment in Electrical and Computer Engineering) at the University of Michigan, where she eventually became the Herman H. Goldstine Collegiate Professor. In 2020, she moved to Yale University as the John C. Malone Professor of Mathematics and Professor of Statistics \u0026amp; Data Science. In 2023, she left the Mathematics Department and is now in Statistics \u0026amp; Data Science. Dr. Gilbert has received several awards, including a Sloan Research Fellowship (2006), an NSF CAREER award (2006), the National Academy of Sciences Award for Initiatives in Research (2008), the Association of Computing Machinery (ACM) Douglas Engelbart Best Paper award (2008), the EURASIP Signal Processing Best Paper award (2010), and the SIAM Ralph E. Kleinman Prize (2013).\u003C\/p\u003E\u003Cp\u003EDr. Gilbert\u0027s research interests include analysis, probability, discrete mathematics, and algorithms. She is especially interested in randomized algorithms with applications to harmonic analysis, signal and image processing, and massive datasets.\u003C\/p\u003E\u003Cp\u003EWebsite: https:\/\/annacgilbert.github.io\/cv\/\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003ERecent papers in the graph machine learning literature have introduced a number of approaches for hyperbolic representation learning. The asserted benefits are improved performance on a variety of graph tasks, node classification and link prediction included. Claims have also been made about the geometric suitability of particular hierarchical graph datasets to representation in hyperbolic space. Despite these claims, our work makes a surprising discovery: when simple Euclidean models with comparable numbers of parameters are properly trained in the same environment, in most cases, they perform as well, if not better, than all introduced hyperbolic graph representation learning models, even on graph datasets previously claimed to be the most hyperbolic as measured by Gromov delta-hyperbolicity (i.e., perfect trees).\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EThis observation gives rise to a simple question: how can this be? We answer this question by taking a careful look at the field of hyperbolic graph representation learning as it stands today, and find that a number of results do not diligently present baselines, make faulty modelling assumptions when constructing algorithms, and use misleading metrics to quantify geometry of graph datasets. We take a closer look at each of these three problems, elucidate the issues, perform an analysis of methods, and introduce a parametric family of benchmark datasets to ascertain the applicability of (hyperbolic) graph neural networks.\u003C\/p\u003E\u003Cp\u003EUnfortunately, these problems are not specific to hyperbolic graph neural nets but are indicative of more fundamental problems in graph machine learning more generally. We show, surprisingly, that node features are oftentimes more-than-sufficient for many common graph benchmarks, breaking this critical assumption. When comparing against a well-tuned feature-only MLP baseline on seven of the most commonly used graph learning datasets, one gains little benefit from using graph structure on five datasets. We posit that these datasets do not benefit considerably from graph learning because the features themselves already contain enough graph information to obviate or substantially reduce the need for the graph.\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":" Seeing the Forest for the Trees"}],"uid":"36527","created_gmt":"2025-11-21 16:46:46","changed_gmt":"2025-11-21 16:53:56","author":"hulrich6","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2025-12-05T11:00:00-05:00","event_time_end":"2025-12-05T12:00:00-05:00","event_time_end_last":"2025-12-05T12:00:00-05:00","gmt_time_start":"2025-12-05 16:00:00","gmt_time_end":"2025-12-05 17:00:00","gmt_time_end_last":"2025-12-05 17:00:00","rrule":null,"timezone":"America\/New_York"},"location":"Groseclose 402","extras":[],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"177814","name":"Postdoc"},{"id":"78771","name":"Public"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"686302":{"#nid":"686302","#data":{"type":"event","title":"ISyE Seminar - David Simchi-Levi","body":[{"value":"\u003Cp\u003ETitle:\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EFrom Democratizing Optimization with LLM to Improving LLM Performance with OR Techniques\u003C\/p\u003E\u003Cp\u003EAbstract:\u003C\/p\u003E\u003Cp\u003ERecent breakthroughs in Large Language Models (LLM) have captured public imagination and interest, while mathematical optimization remains largely underappreciated outside expert circles. In this talk, we show that LLM can finally bridge the persistent gap between optimization\u2019s potent capabilities and its limited real-world uptake. We present the 4I framework\u2014Insight, Interpretability, Interactivity, Improvisation\u2014as a set of design principles for combining LLM with mathematical optimization. Insight establishes a trusted, up-to-date view of the state; Interpretability explains model logic and trade-offs; Interactivity enables conversational what-if analysis; and Improvisation supports event-driven re-optimization.\u003C\/p\u003E\u003Cp\u003EWe also demonstrate how OR techniques can optimize LLM inference under memory constraints. We formulate LLM inference as a multi-stage online scheduling problem with stochastic arrivals and dynamic resource consumption. We develop a fluid dynamics approximation that provides a tractable performance benchmark and guides algorithm design. Building on this foundation, we introduce two algorithms. The Waiting for Accumulated Inference Threshold (WAIT) algorithm optimizes scheduling when output lengths are known using dynamically maintained thresholds. For the realistic scenario where output lengths are unknown at arrival, the Nested WAIT algorithm adaptively learns prompt characteristics through a hierarchical multi-segment framework. We establish theoretical near optimality guarantees under heavy traffic conditions, balancing throughput, latency, and Time to First Token (TTFT). Experiments using Llama-7B on A100 GPUs demonstrate 15-30% throughput improvements and reduced latency versus industry baselines (vLLM and Sarathi).\u003C\/p\u003E\u003Cp\u003EBio:\u003C\/p\u003E\u003Cp\u003EDavid Simchi-Levi holds the MIT William Barton Rogers Professorship (named after the founder \u0026amp; first president of MIT), is a Professor of Engineering Systems at MIT and serves as the head of the MIT Data Science Lab. He is considered one of the premier thought leaders in supply chain management and business analytics. His Ph.D. students have accepted faculty positions in leading academic institutes including U. of California Berkeley, Carnegie Mellon U., Columbia U., Cornell U., Duke U., Georgia Tech, Harvard U., U. of Illinois Urbana-Champaign, U. of Michigan, Purdue U. and Virginia Tech.\u003C\/p\u003E\u003Cp\u003EProfessor Simchi-Levi is the former Editor-in-Chief of Management Science (2018-2023), one of the two flagship journals of INFORMS. He served as the Editor-in-Chief for Operations Research (2006-2012), the other flagship journal of INFORMS and for Naval Research Logistics (2003-2005). In 2023, he was elected a member of the National Academy of Engineering. In 2020, he was awarded the prestigious INFORMS Impact Prize for playing a leading role in developing and disseminating a new highly impactful paradigm for the identification and mitigation of risks in global supply chains. He is an INFORMS Fellow and MSOM Distinguished Fellow and the recipient of the 2020 INFORMS Koopman Award given to an outstanding publication in military operations research; Ford Motor Company 2015 Engineering Excellence Award; 2014 INFORMS Daniel H. Wagner Prize for Excellence in Operations Research Practice; 2014 INFORMS Revenue Management and Pricing Section Practice Award; and 2009 INFORMS Revenue Management and Pricing Section Prize.\u003C\/p\u003E\u003Cp\u003EHe was the founder of LogicTools which provided software solutions and professional services for supply chain optimization. LogicTools became part of IBM in 2009. In 2012 he co-founded OPS Rules, an operations analytics consulting company. The company became part of Accenture in 2016. In 2014, he co-founded Opalytics, a cloud analytics platform company focusing on operations and supply chain decisions. The company became part of the Accenture Applied Intelligence in 2018.\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003ERecent breakthroughs in Large Language Models (LLM) have captured public imagination and interest, while mathematical optimization remains largely underappreciated outside expert circles. In this talk, we show that LLM can finally bridge the persistent gap between optimization\u2019s potent capabilities and its limited real-world uptake. We present the 4I framework\u2014Insight, Interpretability, Interactivity, Improvisation\u2014as a set of design principles for combining LLM with mathematical optimization. Insight establishes a trusted, up-to-date view of the state; Interpretability explains model logic and trade-offs; Interactivity enables conversational what-if analysis; and Improvisation supports event-driven re-optimization.\u003C\/p\u003E\u003Cp\u003EWe also demonstrate how OR techniques can optimize LLM inference under memory constraints. We formulate LLM inference as a multi-stage online scheduling problem with stochastic arrivals and dynamic resource consumption. We develop a fluid dynamics approximation that provides a tractable performance benchmark and guides algorithm design. Building on this foundation, we introduce two algorithms. The Waiting for Accumulated Inference Threshold (WAIT) algorithm optimizes scheduling when output lengths are known using dynamically maintained thresholds. For the realistic scenario where output lengths are unknown at arrival, the Nested WAIT algorithm adaptively learns prompt characteristics through a hierarchical multi-segment framework. We establish theoretical near optimality guarantees under heavy traffic conditions, balancing throughput, latency, and Time to First Token (TTFT). Experiments using Llama-7B on A100 GPUs demonstrate 15-30% throughput improvements and reduced latency versus industry baselines (vLLM and Sarathi).\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"From Democratizing Optimization with LLM to Improving LLM Performance with OR Techniques"}],"uid":"36527","created_gmt":"2025-11-07 19:26:16","changed_gmt":"2025-11-18 20:08:00","author":"hulrich6","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2025-11-21T11:00:00-05:00","event_time_end":"2025-11-21T12:00:00-05:00","event_time_end_last":"2025-11-21T12:00:00-05:00","gmt_time_start":"2025-11-21 16:00:00","gmt_time_end":"2025-11-21 17:00:00","gmt_time_end_last":"2025-11-21 17:00:00","rrule":null,"timezone":"America\/New_York"},"location":"Groseclose 402","extras":[],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"177814","name":"Postdoc"},{"id":"78771","name":"Public"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"686503":{"#nid":"686503","#data":{"type":"event","title":"Hybrid Intelligence: Building the Bridge Between Optimization and Quantum Computing","body":[{"value":"\u003Cp\u003EQuantum computing promises to transform how we solve optimization problems\u2014but currently available Noisy Intermediate Scale Quantum (NISQ era) devices alone cannot get us there. A promising path forward lies in hybrid approaches that combine the strengths of classical algorithms with the emerging capabilities of quantum hardware, opening the door to scalable and high-performance solutions. In this talk, I will present recent advances in quantum optimization that bring this vision to life. I will discuss how warm-starting the Quantum Approximate Optimization Algorithm (QAOA) with classical relaxations can dramatically improve performance, how hybrid pipelines leverage combinatorial structure to achieve stronger approximations and noise resilience, and how benchmark families\u2014such as strongly regular graphs and low-degree graphs\u2014help us probe the boundary of quantum advantage. These results connect classical approximation algorithms, convex optimization, and quantum circuit design. Looking ahead, I will outline a research vision for hybrid intelligence, where classical and quantum algorithms operate in synergy rather than competition\u2014rethinking optimization for the next era of computing.\u003Cbr\u003E\u003Cbr\u003ESwati Gupta is an Associate Professor and the Class of 1947 Career Development Professor at the MIT Sloan School of Management in the Operations Research and Statistics Group. Prior to this, she held a Fouts Family Early Career Professorship as an Assistant Professor at the Stewart School of Industrial \u0026amp; Systems Engineering at Georgia Tech from 2018-2023, where she served as the lead of Ethical AI in the NSF AI Institute on Advances in Optimization from 2021-2023. She received a Ph.D. in Operations Research from MIT in 2017, following a joint Masters and B.Tech in Computer Science from IIT Delhi. Her research bridges optimization, machine learning, and algorithmic fairness, to design algorithms that are both theoretically rigorous and socially impactful, with applications in healthcare, hiring, energy, quantum computing, and beyond. Her work has been recognized by the 2023 NSF CAREER Award, INFORMS Doing Good with OR 2022 (finalist), the JP Morgan Early Career Faculty Recognition in 2021, the NSF CISE Research Initiation Initiative Award in 2019, INFORMS Computing Society 2016 (special recognition), and the INFORMS Service Science Section 2016 (finalist).\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EThis talk explores recent advances in hybrid quantum\u2013classical optimization, showing how classical warm-starts, structured pipelines, and benchmark problems can significantly boost QAOA performance and guide the path toward practical quantum advantage.\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"A look at recent advances in hybrid quantum\u2013classical optimization, showing how combining classical methods with emerging quantum hardware can improve performance and move us closer to practical quantum advantage."}],"uid":"36458","created_gmt":"2025-11-18 12:33:43","changed_gmt":"2025-11-18 12:52:53","author":"mellis74","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2025-11-18T15:30:00-05:00","event_time_end":"2025-11-18T16:30:00-05:00","event_time_end_last":"2025-11-18T16:30:00-05:00","gmt_time_start":"2025-11-18 20:30:00","gmt_time_end":"2025-11-18 21:30:00","gmt_time_end_last":"2025-11-18 21:30:00","rrule":null,"timezone":"America\/New_York"},"location":"Groseclose Executive Boardroom GC402","extras":[],"hg_media":{"678653":{"id":"678653","type":"image","title":"Swati Gupta","body":null,"created":"1763470253","gmt_created":"2025-11-18 12:50:53","changed":"1763470253","gmt_changed":"2025-11-18 12:50:53","alt":"Swati Gupta","file":{"fid":"262712","name":"Swati-Gupta.jpg","image_path":"\/sites\/default\/files\/2025\/11\/18\/Swati-Gupta_0.jpg","image_full_path":"http:\/\/hg.gatech.edu\/\/sites\/default\/files\/2025\/11\/18\/Swati-Gupta_0.jpg","mime":"image\/jpeg","size":77868,"path_740":"http:\/\/hg.gatech.edu\/sites\/default\/files\/styles\/740xx_scale\/public\/2025\/11\/18\/Swati-Gupta_0.jpg?itok=AHBk083P"}}},"media_ids":["678653"],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"174045","name":"Graduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"686199":{"#nid":"686199","#data":{"type":"event","title":"SCL Spring 2026 Supply Chain Day Career Fair","body":[{"value":"\u003Cp\u003EEmployers and Georgia Tech students \u2013 please join us for our spring Supply Chain Day!\u0026nbsp;\u003C\/p\u003E\u003Ch3\u003E\u003Cstrong\u003EEvent Details\u003C\/strong\u003E\u003C\/h3\u003E\u003Ch4\u003EOn Campus\/In-Person (Georgia Tech Exhibition Hall)\u003C\/h4\u003E\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EWednesday, January 14, 2026 | 10am-2pm ET\u0026nbsp;\u003C\/strong\u003E\u003C\/li\u003E\u003C\/ul\u003E\u003Ch3\u003EStudents\u003C\/h3\u003E\u003Cp\u003E\u003Cstrong\u003EWe strongly encourage you to attend to seek full-time employment\u003C\/strong\u003E, \u003Cstrong\u003Einternships, and projects\u003C\/strong\u003E (rather than waiting until the end of the semester).\u003C\/p\u003E\u003Ch3\u003EOrganizations\u003C\/h3\u003E\u003Cp\u003EIf you are interested in hosting a table for our upcoming session, please let us know after reviewing the below information within our website. Early registration closes December 1st!\u003C\/p\u003E\u003Ch4\u003EMORE INFORMATION AND EVENT REGISTRATION\u003C\/h4\u003E\u003Cp\u003EVisit\u0026nbsp;\u003Ca href=\u0022https:\/\/www.scl.gatech.edu\/outreach\/supplychainday\u0022\u003E\u003Cstrong\u003Ehttps:\/\/www.scl.gatech.edu\/outreach\/supplychainday\u003C\/strong\u003E\u003C\/a\u003E.\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EGeorgia Tech Supply Chain\u0026nbsp;students and employers, please join us for our fall Supply Chain Day! We will be hosting an on campus session\u0026nbsp;Wednesday, January 14, 2026 from 10am-2pm ET at the Georgia Tech Exhibition Hall.\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Supply chain and logistics career fair where industry supply chain representatives meet Georgia Tech students."}],"uid":"27233","created_gmt":"2025-11-05 14:41:52","changed_gmt":"2025-11-05 15:02:55","author":"Andy Haleblian","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2026-01-14T10:00:00-05:00","event_time_end":"2026-01-14T14:00:00-05:00","event_time_end_last":"2026-01-14T14:00:00-05:00","gmt_time_start":"2026-01-14 15:00:00","gmt_time_end":"2026-01-14 19:00:00","gmt_time_end_last":"2026-01-14 19:00:00","rrule":null,"timezone":"America\/New_York"},"location":"Georgia Tech Exhibition Hall","extras":[],"hg_media":{"678547":{"id":"678547","type":"image","title":"Jan 14, 2026 Supply Chain and Logistics Career Fair","body":null,"created":"1762354713","gmt_created":"2025-11-05 14:58:33","changed":"1762354713","gmt_changed":"2025-11-05 14:58:33","alt":"Jan 14, 2026 Supply Chain and Logistics Career Fair","file":{"fid":"262599","name":"HgSCDaySymplicityBanner_20260114.png","image_path":"\/sites\/default\/files\/2025\/11\/05\/HgSCDaySymplicityBanner_20260114.png","image_full_path":"http:\/\/hg.gatech.edu\/\/sites\/default\/files\/2025\/11\/05\/HgSCDaySymplicityBanner_20260114.png","mime":"image\/png","size":704380,"path_740":"http:\/\/hg.gatech.edu\/sites\/default\/files\/styles\/740xx_scale\/public\/2025\/11\/05\/HgSCDaySymplicityBanner_20260114.png?itok=3Y7qUJ_T"}}},"media_ids":["678547"],"related_links":[{"url":"https:\/\/www.scl.gatech.edu\/outreach\/supplychainday","title":"Register online to attend (for Georgia Tech students)"},{"url":"https:\/\/www.scl.gatech.edu","title":"Supply Chain and Logistics Institute website"}],"groups":[{"id":"1250","name":"Center for Health and Humanitarian Systems (CHHS)"},{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"},{"id":"1243","name":"The Supply Chain and Logistics Institute (SCL)"}],"categories":[],"keywords":[{"id":"780","name":"employment"},{"id":"9845","name":"GTSCL"},{"id":"233","name":"Logistics"},{"id":"167074","name":"Supply Chain"},{"id":"1996","name":"Recruiting"},{"id":"5172","name":"career day"},{"id":"122741","name":"physical internet"},{"id":"186857","name":"go-gtmi"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"10377","name":"Career\/Professional development"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"177814","name":"Postdoc"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[{"value":"\u003Cp\u003Eevent@scl.gatech.edu\u003C\/p\u003E","format":"limited_html"}],"email":[],"slides":[],"orientation":[],"userdata":""}},"686066":{"#nid":"686066","#data":{"type":"event","title":"ISyE Seminar - Tirthankar Dasgupta","body":[{"value":"\u003Cdiv\u003ETitle:\u003C\/div\u003E\u003Cdiv\u003E\u0026nbsp;\u003C\/div\u003E\u003Cdiv\u003EReconnecting Sampling, Design, and Causality: A Modern Perspective on Classical Foundations\u003C\/div\u003E\u003Cdiv\u003E\u0026nbsp;\u003C\/div\u003E\u003Cdiv\u003EAbstract:\u003C\/div\u003E\u003Cdiv\u003E\u0026nbsp;\u003C\/div\u003E\u003Cdiv\u003EThis talk explores the deep connections between two foundational pillars of twentieth-century statistics\u2014survey sampling and experimental design. Though these connections became somewhat esoteric in the late twentieth century, they have experienced a revival through the framework of finite-population causal inference. Central to this connection is the concept of potential outcomes (or counterfactuals), first introduced by Neyman in 1923 and later expanded and formalized by Rubin in the 1970s. Through illustrative examples, we will show how the classical results developed in the early twentieth century can be reinterpreted and extended to address contemporary challenges, particularly as randomized experiments gain renewed prominence across the social, behavioral, and biomedical sciences.\u003C\/div\u003E\u003Cdiv\u003E\u0026nbsp;\u003C\/div\u003E\u003Cdiv\u003EBio:\u003C\/div\u003E\u003Cdiv\u003E\u003Cdiv\u003E\u003Cdiv\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003ETirthankar Dasgupta is a Professor and Co-Director of Graduate Studies in the Department of Statistics, Rutgers University. \u0026nbsp;His research interests include experimental design, causal inference, statistical modeling of physical and engineering systems and quality engineering. He obtained his Ph.D. in industrial engineering with specialization in engineering statistics from Georgia Institute of Technology in 2007 under the supervision of \u003Ca href=\u0022https:\/\/www.isye.gatech.edu\/users\/jeff-wu\u0022 rel=\u0022noreferrer noopener\u0022 target=\u0022_blank\u0022\u003EProf C. F. Jeff Wu\u003C\/a\u003E and received the Sigma-Xi best doctoral thesis award for his dissertation. He joined Harvard University in 2008 as an Assistant Professor and was promoted to the rank of Associate Professor in 2012. In the same year, he received the David Pickard award from Harvard University for excellence in teaching and mentoring. Prof Dasgupta has served as the research advisor of seven doctoral students till date. Part of his research has been funded by the \u003Ca href=\u0022https:\/\/www.nsf.gov\/div\/index.jsp?div=CMMI\u0022 rel=\u0022noreferrer noopener\u0022 target=\u0022_blank\u0022\u003EDivision of Civil, Mechanical and Manufacturing Innovation (CMMI), Division of Mathematical Sciences (DMS) and Division of the Social and Economic Sciences (SES) of the National Science Foundation.\u0026nbsp;\u003C\/a\u003ECurrently he serves on the Editorial Boards of The Journal of the American Statistical Association and the Journal of the Royal Statistical Society (Series B).\u003C\/p\u003E\u003C\/div\u003E\u003C\/div\u003E\u003C\/div\u003E\u003Cdiv\u003E\u003Cbr\u003E\u0026nbsp;\u003C\/div\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EThis talk explores the deep connections between two foundational pillars of twentieth-century statistics\u2014survey sampling and experimental design. Though these connections became somewhat esoteric in the late twentieth century, they have experienced a revival through the framework of finite-population causal inference. Central to this connection is the concept of potential outcomes (or counterfactuals), first introduced by Neyman in 1923 and later expanded and formalized by Rubin in the 1970s. Through illustrative examples, we will show how the classical results developed in the early twentieth century can be reinterpreted and extended to address contemporary challenges, particularly as randomized experiments gain renewed prominence across the social, behavioral, and biomedical sciences.\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Reconnecting Sampling, Design, and Causality: A Modern Perspective on Classical Foundations"}],"uid":"36527","created_gmt":"2025-10-29 13:58:46","changed_gmt":"2025-10-29 14:04:48","author":"hulrich6","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2025-11-14T11:00:00-05:00","event_time_end":"2025-11-14T12:00:00-05:00","event_time_end_last":"2025-11-14T12:00:00-05:00","gmt_time_start":"2025-11-14 16:00:00","gmt_time_end":"2025-11-14 17:00:00","gmt_time_end_last":"2025-11-14 17:00:00","rrule":null,"timezone":"America\/New_York"},"location":"Groseclose 402","extras":[],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"177814","name":"Postdoc"},{"id":"78771","name":"Public"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"685892":{"#nid":"685892","#data":{"type":"event","title":"Ad Hoc ISyE Seminar - Zhenyu Hu","body":[{"value":"\u003Cp\u003ETitle:\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EGacha: A Simple Mechanism to Screen a Budget-Constrained Buyer\u003C\/p\u003E\u003Cp\u003EAbstract:\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EA lottery mechanism that allows repeated purchases until the buyer wins a designated item is widely used for both digital goods in Gacha games (e.g., \u0026nbsp;\u003Cem\u003EGenshin Impact\u003C\/em\u003E) and physical goods such as collectible toys (e.g., Labubu). We study this mechanism---referred to as a Gacha mechanism---in what is arguably the simplest possible setting: a seller offering one single item to a budget-constrained buyer. While the optimal mechanism in this setting typically involves designing a large (potentially \u003Cem\u003Einfinite\u003C\/em\u003E) menu of lotteries that can only be purchased once, the Gacha mechanism requires only a selling price and a winning probability, making it far more practical to implement. We show that the Gacha mechanism is particularly effective at screening along the budget dimension. While the posted price mechanism can perform arbitrarily poorly compared to the optimal mechanism when the buyer\u2019s valuation is public, the Gacha mechanism achieves at least 63.2% of the optimal revenue. It becomes \u003Cem\u003Easymptotically optimal\u003C\/em\u003E as the valuation grows large. Moreover, when the seller has almost no information about the buyer\u2019s budget distribution, 63.2% is also the max-min revenue ratio guarantee, which can be achieved by a Gacha mechanism with vanishing winning probability. We also show that the Gacha mechanism is less effective at screening along the valuation dimension. When the valuation is private and the budget is public, the optimal Gacha mechanism reduces to a posted-price mechanism that achieves at least 50% of the optimal revenue. When both valuation and budget are private and independent, and the valuation follows a monotone hazard rate distribution, the Gacha mechanism guarantees at least 23% of the optimal revenue. Finally, we explore two extensions of the Gacha mechanism---one that includes the option of direct purchase, and another that incorporates a pity system.\u003C\/p\u003E\u003Cp\u003EBio:\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EZhenyu Hu is a Dean\u2019s Chair Associate Professor at the Department of Analytics \u0026amp; Operations at the National University of Singapore. He obtained his PhD in industrial engineering from the University of Illinois at Urbana-Champaign and a bachelor in mathematics from Sun Yat-sen University. His research focuses on dynamic pricing and revenue management, supply chain management, and mechanism and information design. He currently serves as an associate editor for M\u0026amp;SOM.\u0026nbsp;\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EA lottery mechanism that allows repeated purchases until the buyer wins a designated item is widely used for both digital goods in Gacha games (e.g., \u0026nbsp;\u003Cem\u003EGenshin Impact\u003C\/em\u003E) and physical goods such as collectible toys (e.g., Labubu). We study this mechanism---referred to as a Gacha mechanism---in what is arguably the simplest possible setting: a seller offering one single item to a budget-constrained buyer. While the optimal mechanism in this setting typically involves designing a large (potentially \u003Cem\u003Einfinite\u003C\/em\u003E) menu of lotteries that can only be purchased once, the Gacha mechanism requires only a selling price and a winning probability, making it far more practical to implement. We show that the Gacha mechanism is particularly effective at screening along the budget dimension. While the posted price mechanism can perform arbitrarily poorly compared to the optimal mechanism when the buyer\u2019s valuation is public, the Gacha mechanism achieves at least 63.2% of the optimal revenue. It becomes \u003Cem\u003Easymptotically optimal\u003C\/em\u003E as the valuation grows large. Moreover, when the seller has almost no information about the buyer\u2019s budget distribution, 63.2% is also the max-min revenue ratio guarantee, which can be achieved by a Gacha mechanism with vanishing winning probability. We also show that the Gacha mechanism is less effective at screening along the valuation dimension. When the valuation is private and the budget is public, the optimal Gacha mechanism reduces to a posted-price mechanism that achieves at least 50% of the optimal revenue. When both valuation and budget are private and independent, and the valuation follows a monotone hazard rate distribution, the Gacha mechanism guarantees at least 23% of the optimal revenue. Finally, we explore two extensions of the Gacha mechanism---one that includes the option of direct purchase, and another that incorporates a pity system.\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Gacha: A Simple Mechanism to Screen a Budget-Constrained Buyer"}],"uid":"36527","created_gmt":"2025-10-21 20:23:46","changed_gmt":"2025-10-21 20:27:55","author":"hulrich6","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2025-10-30T10:00:00-04:00","event_time_end":"2025-10-30T11:00:00-04:00","event_time_end_last":"2025-10-30T11:00:00-04:00","gmt_time_start":"2025-10-30 14:00:00","gmt_time_end":"2025-10-30 15:00:00","gmt_time_end_last":"2025-10-30 15:00:00","rrule":null,"timezone":"America\/New_York"},"location":"ISYE Main 228","extras":[],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"177814","name":"Postdoc"},{"id":"78771","name":"Public"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"685866":{"#nid":"685866","#data":{"type":"event","title":"SCL Lunch and Learn: \u201cCan Your Supply Chain Trust AI?\u0022","body":[{"value":"\u003Cp\u003E\u003Cem\u003E\u003Cstrong\u003EAI is revolutionizing supply chains with speed and intelligence, but its ethical implications in areas like logistics, procurement, and risk management demand deeper scrutiny.\u003C\/strong\u003E\u003C\/em\u003E\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EThursday, December 4, 2025 | 12-1pm ET\u003C\/strong\u003E\u003C\/p\u003E\u003Cdiv\u003E\u003Cdiv\u003E\u003Cdiv\u003E\u003Cdiv\u003E\u003Cdiv\u003E\u003Cp\u003EWe trust AI to make faster, smarter supply chain decisions. But can it be trusted to be ethical? Join Georgia Tech\u0027s Supply Chain and Logistics Institute and Rosemarie Santa Gonzalez to rethink what \u0022ethical AI\u0022 really means in high-stakes environments like logistics, procurement, and risk management. This session will spark new thinking and help you move from AI ambition to AI accountability.\u003C\/p\u003E\u003Cdiv\u003E\u003Ca href=\u0022https:\/\/gatech.zoom.us\/webinar\/register\/5517609939377\/WN_hqRyO-H7Swy7UxWGPquiAQ\u0022\u003E\u003Cstrong\u003ERegister Online to Attend\u003C\/strong\u003E\u003C\/a\u003E\u003C\/div\u003E\u003C\/div\u003E\u003C\/div\u003E\u003C\/div\u003E\u003C\/div\u003E\u003C\/div\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EJoin Rosemarie Santa Gonzalez with the Georgia Tech Supply Chain and Logistics Institute as she discusses whether AI can be trusted to act ethically in high-stakes environments like logistics, procurement, and risk management.\u0026nbsp;\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"AI is revolutionizing supply chains with speed and intelligence, but its ethical implications in areas like logistics, procurement, and risk management demand deeper scrutiny."}],"uid":"27233","created_gmt":"2025-10-20 20:53:43","changed_gmt":"2025-10-21 12:33:28","author":"Andy Haleblian","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2025-12-04T12:00:00-05:00","event_time_end":"2025-12-04T13:00:00-05:00","event_time_end_last":"2025-12-04T13:00:00-05:00","gmt_time_start":"2025-12-04 17:00:00","gmt_time_end":"2025-12-04 18:00:00","gmt_time_end_last":"2025-12-04 18:00:00","rrule":null,"timezone":"America\/New_York"},"location":"Online via Zoom","extras":[],"hg_media":{"678396":{"id":"678396","type":"image","title":"Can Your Supply Chain Trust AI?","body":null,"created":"1760994059","gmt_created":"2025-10-20 21:00:59","changed":"1760994059","gmt_changed":"2025-10-20 21:00:59","alt":"Can Your Supply Chain Trust AI?","file":{"fid":"262416","name":"hg_LNL_EthicalAI_20251204.png","image_path":"\/sites\/default\/files\/2025\/10\/20\/hg_LNL_EthicalAI_20251204.png","image_full_path":"http:\/\/hg.gatech.edu\/\/sites\/default\/files\/2025\/10\/20\/hg_LNL_EthicalAI_20251204.png","mime":"image\/png","size":145610,"path_740":"http:\/\/hg.gatech.edu\/sites\/default\/files\/styles\/740xx_scale\/public\/2025\/10\/20\/hg_LNL_EthicalAI_20251204.png?itok=s5K1lRze"}}},"media_ids":["678396"],"related_links":[{"url":"https:\/\/gatech.zoom.us\/webinar\/register\/5517609939377\/WN_hqRyO-H7Swy7UxWGPquiAQ","title":"Register Online to Attend the Webinar"},{"url":"https:\/\/www.scl.gatech.edu\/education\/professional-education\/course\/gaiascp","title":"SCL\u0027s Generative AI Application for Supply Chain Professionals course"}],"groups":[{"id":"1250","name":"Center for Health and Humanitarian Systems (CHHS)"},{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"},{"id":"1243","name":"The Supply Chain and Logistics Institute (SCL)"}],"categories":[],"keywords":[{"id":"3615","name":"information session"},{"id":"186857","name":"go-gtmi"},{"id":"122741","name":"physical internet"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"10377","name":"Career\/Professional development"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"177814","name":"Postdoc"},{"id":"78771","name":"Public"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[{"value":"\u003Cp\u003Ecourse@scl.gatech.edu\u003C\/p\u003E","format":"limited_html"}],"email":[],"slides":[],"orientation":[],"userdata":""}},"685789":{"#nid":"685789","#data":{"type":"event","title":"ISyE Seminar - Luis Nunes Vicente","body":[{"value":"\u003Cp\u003ETitle: Reducing Sample Complexity in Stochastic Derivative-Free Optimization via Tail Bounds and Hypothesis Testing\u003C\/p\u003E\u003Cp\u003EAbstract:\u003C\/p\u003E\u003Cp\u003EWe introduce and analyze new probabilistic strategies for enforcing sufficient decrease conditions in stochastic derivative-free optimization, with the goal of reducing sample complexity and simplifying convergence analysis. First, we develop a new tail bound condition imposed on the estimated reduction in function value, which permits flexible selection of the power used in the sufficient decrease test, q in (1,2]. This approach allows us to reduce the number of samples per iteration from the standard O(delta^{\u22124}) to O(delta^{-2q}), assuming\u0026nbsp;that the noise\u0026nbsp;moment of order q\/(q-1) is bounded. Second, we formulate the sufficient decrease condition as a sequential hypothesis testing problem, in which the algorithm adaptively collects samples until the evidence suffices to accept or reject a candidate step. This test provides statistical guarantees on decision errors and can further reduce the required sample size, particularly in the Gaussian noise setting, where it can approach\u0026nbsp;O(delta^{\u22122-r})\u0026nbsp;when the decrease is of the order of delta^r. We incorporate both techniques into stochastic direct-search and trust-region methods for potentially non-smooth, noisy objective functions, and establish their global convergence rates and properties.\u003C\/p\u003E\u003Cp\u003EBio:\u003C\/p\u003E\u003Cp\u003ELuis Nunes Vicente is the Timothy J. Wilmott \u201980 Endowed Faculty Professor and Chair of Lehigh University\u2019s Department of Industrial and Systems Engineering (ISE). His research interests include Continuous Optimization, Computational Science and Engineering, and Machine Learning and Data Science. He obtained his PhD from Rice University in 1996, under a Fulbright scholarship, receiving from Rice the Ralph Budd Thesis Award. He was one of the three finalists of the 94-96 A. W. Tucker Prize of the Mathematical Optimization Society (MOS). In 2015, he was awarded the Lagrange Prize of SIAM (Society for Industrial and Applied Mathematics) and MOS for the co-authorship of the book \u201cIntroduction to Derivative-Free Optimization, MPS-SIAM Series on Optimization, SIAM, Philadelphia, 2009\u201d. He is a SIAM Fellow (Class of 2024). He was elected chair of the SIAM Activity Group on Optimization for 2023-2025 and President of the Association of Chairs of Operations Research Departments (ACORD) at INFORMS for 2024-2025. He has been chairing Lehigh ISE since August 2018.\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EWe introduce and analyze new probabilistic strategies for enforcing sufficient decrease conditions in stochastic derivative-free optimization, with the goal of reducing sample complexity and simplifying convergence analysis. First, we develop a new tail bound condition imposed on the estimated reduction in function value, which permits flexible selection of the power used in the sufficient decrease test, q in (1,2]. This approach allows us to reduce the number of samples per iteration from the standard O(delta^{\u22124}) to O(delta^{-2q}), assuming\u0026nbsp;that the noise\u0026nbsp;moment of order q\/(q-1) is bounded. Second, we formulate the sufficient decrease condition as a sequential hypothesis testing problem, in which the algorithm adaptively collects samples until the evidence suffices to accept or reject a candidate step. This test provides statistical guarantees on decision errors and can further reduce the required sample size, particularly in the Gaussian noise setting, where it can approach\u0026nbsp;O(delta^{\u22122-r})\u0026nbsp;when the decrease is of the order of delta^r. We incorporate both techniques into stochastic direct-search and trust-region methods for potentially non-smooth, noisy objective functions, and establish their global convergence rates and properties.\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Reducing Sample Complexity in Stochastic Derivative-Free Optimization via Tail Bounds and Hypothesis Testing"}],"uid":"36527","created_gmt":"2025-10-17 18:16:36","changed_gmt":"2025-10-17 18:21:02","author":"hulrich6","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2025-11-07T11:00:00-05:00","event_time_end":"2025-11-07T12:00:00-05:00","event_time_end_last":"2025-11-07T12:00:00-05:00","gmt_time_start":"2025-11-07 16:00:00","gmt_time_end":"2025-11-07 17:00:00","gmt_time_end_last":"2025-11-07 17:00:00","rrule":null,"timezone":"America\/New_York"},"location":"Groseclose 402","extras":[],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"177814","name":"Postdoc"},{"id":"78771","name":"Public"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"685778":{"#nid":"685778","#data":{"type":"event","title":"Professional Education Course: Systems Operations and Strategic Interactions in Supply Chains","body":[{"value":"\u003Cp\u003EClasses\u0026nbsp;will be taught by LIVE video instruction similar to the experience you would receive in person with the same interactive components.\u0026nbsp;Each course will run for one week Monday through Thursday from 10am to 1:30pm ET each day with an optional extra day on Friday.\u003C\/p\u003E\u003Ch3\u003E\u003Cstrong\u003ECourse Description\u003C\/strong\u003E\u003C\/h3\u003E\u003Cp\u003EOften the lack of cooperation and coordination between organizations or stakeholders lead to inefficiencies, despite having common goals. A systems view is needed to ensure appropriate use of scarce resources to meet the multiple, and often conflicting, short- and long-term goals from multiple constituents. This course will focus on conceptual and modeling skills to understand and effectively manage supply chains and operations from a systems perspective. Models will address system characteristics (e.g., demand dependencies) that drive system dynamics and policies to regulate performance. Course topics include methods for improving coordination and collaboration, addressing demand dependencies, and reliably measuring and evaluating system performance.\u003C\/p\u003E\u003Ch3\u003E\u003Cstrong\u003EWho Should Attend\u003C\/strong\u003E\u003C\/h3\u003E\u003Cp\u003EThis course is designed for representatives from governmental or non-governmental organizations, private corporations, military, and foundations, including but not limited to senior executives overseeing administrative and operational functions of an organization, logistics and supply chain managers, program managers, directors of field operations, directors of emergency\/disaster preparedness and response, and public health professionals.\u003C\/p\u003E\u003Ch3\u003E\u003Cstrong\u003EHow You Will Benefit\u003C\/strong\u003E\u003C\/h3\u003E\u003Cul\u003E\u003Cli\u003EIdentify opportunities for coordination within organizations and collaboration across organizations for increased efficiency and improved outcomes.\u003C\/li\u003E\u003Cli\u003EDescribe the strategic behavior of decision-makers and the impact of the market (or contract) structure on the participant\u0027s actions and the overall system dynamics.\u003C\/li\u003E\u003Cli\u003EDefine evaluation metrics in alignment with the system goals and structure system operations and incentives that address and evaluate these metrics.\u003C\/li\u003E\u003C\/ul\u003E\u003Ch3\u003E\u003Cstrong\u003EWhat Is Covered\u003C\/strong\u003E\u003C\/h3\u003E\u003Cul\u003E\u003Cli\u003EHow coordination and collaboration can improve supply chain efficiency and effectiveness\u003C\/li\u003E\u003Cli\u003EHow events, decisions and actions in one part of a system, such as a supply chain, impact other parts of the system\u003C\/li\u003E\u003Cli\u003ESystem-wide inventory variability and costs mitigation and reduction\u003C\/li\u003E\u003Cli\u003EEvaluation metrics\u003C\/li\u003E\u003C\/ul\u003E\u003Ch3\u003E\u003Cstrong\u003EAbout the Course and the\u0026nbsp;HHSCM Course Series\u003C\/strong\u003E\u003C\/h3\u003E\u003Cp\u003EThis course is the first in a 3-part virtually synchronous professional education program. Register and pay for all three required\u0026nbsp;\u003Ca href=\u0022https:\/\/pe.gatech.edu\/node\/54\u0022\u003EHealth and Humanitarian Supply Chain Management Certificate courses\u003C\/a\u003E\u0026nbsp;and receive a discount of $400 off per course. Enter coupon code\u0026nbsp;\u003Cstrong\u003ESCL-HHS\u003C\/strong\u003E\u0026nbsp;at checkout with the Georgia Tech Professional Education website..\u0026nbsp;\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EAdditionally, there are scholarships available for the certificate program. Apply at \u003Ca href=\u0022https:\/\/chhs.gatech.edu\/course-scholarships\u0022\u003Ehttps:\/\/chhs.gatech.edu\/course-scholarships\u003C\/a\u003E by the noted deadline.\u0026nbsp;\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EQuestions? Reach out to\u0026nbsp;\u003Ca href=\u0022mailto:chhs@gatech.edu\u0022\u003Echhs@gatech.edu\u003C\/a\u003E!\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EThis course focuses on conceptual and modeling skills to understand and effectively manage supply chains and operations from a systems perspective. Models will address system characteristics (e.g., demand dependencies) that drive system dynamics and policies to regulate performance. Course topics include methods for improving coordination and collaboration, addressing demand dependencies, and reliably measuring and evaluating system performance.\u003C\/p\u003E\r\n","format":"limited_html"}],"field_summary_sentence":[{"value":"Explore conceptual and modeling skills to understand and effectively manage humanitarian response from a systems perspective."}],"uid":"27233","created_gmt":"2025-10-17 14:53:05","changed_gmt":"2025-10-17 14:53:38","author":"Andy Haleblian","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2026-03-30T10:00:00-04:00","event_time_end":"2026-04-03T13:30:00-04:00","event_time_end_last":"2026-04-03T13:30:00-04:00","gmt_time_start":"2026-03-30 14:00:00","gmt_time_end":"2026-04-03 17:30:00","gmt_time_end_last":"2026-04-03 17:30:00","rrule":null,"timezone":"America\/New_York"},"location":"Virtual\/Instructor-led","extras":[],"related_links":[{"url":"https:\/\/chhs.gatech.edu\/education\/professional-education","title":"Course Details via Center for Health and Humanitarian Systems website"},{"url":"https:\/\/pe.gatech.edu\/courses\/systems-operations-and-strategic-interactions-supply-chains","title":"Registration link via Georgia Tech Professional Education"},{"url":"https:\/\/pe.gatech.edu\/certificates\/health-humanitarian-supply-chain-management-certificate","title":"Health \u0026 Humanitarian Supply Chain Management Certificate"},{"url":"https:\/\/chhs.gatech.edu\/course-scholarships","title":"Apply for a Scholarship!"}],"groups":[{"id":"1250","name":"Center for Health and Humanitarian Systems (CHHS)"},{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"},{"id":"1243","name":"The Supply Chain and Logistics Institute (SCL)"}],"categories":[],"keywords":[{"id":"192986","name":"supply chain, logistics, humanitarian"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"10377","name":"Career\/Professional development"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"78771","name":"Public"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[{"value":"\u003Cp\u003E\u003Ca href=\u0022mailto:chhs@gatech.edu\u0022\u003Echhs@gatech.edu\u003C\/a\u003E\u003C\/p\u003E","format":"limited_html"}],"email":[],"slides":[],"orientation":[],"userdata":""}},"685776":{"#nid":"685776","#data":{"type":"event","title":"Professional Education Course: Responsive Supply Chain Design and Operations","body":[{"value":"\u003Cp\u003EClasses\u0026nbsp;will be taught by LIVE video instruction similar to the experience you would receive in person with the same interactive components.\u0026nbsp;Each course will run for one week Monday through Thursday from 10am to 1:30pm ET each day with an optional extra day on Friday.\u003C\/p\u003E\u003Ch3\u003E\u003Cstrong\u003ECourse Description\u003C\/strong\u003E\u003C\/h3\u003E\u003Cp\u003EMeeting demand in a timely and cost-effective manner is important both in public and private supply chains, and heavily depend on the design and operation of these supply chains. Demand is affected by ongoing factors such as local economy, infrastructure, and geographic location, as well as unexpected events such as natural or manmade disasters or other large-scale disruptions. Designing and operating responsive supply chains requires the consideration of uncertainty in timing, scope, scale, and understanding of various topics such as forecasting, distribution network design, and inventory management. This course will examine methods and models for making supply chain design and operational decisions and explore the significant value that is obtained through informed decision-making in advance of an unpredictable event or long-term strategy for meeting the need of customers and beneficiaries.\u003C\/p\u003E\u003Ch3\u003E\u003Cstrong\u003EWho Should Attend\u003C\/strong\u003E\u003C\/h3\u003E\u003Cp\u003EThis course is designed for representatives from governmental or non-governmental organizations, private corporations, military, and foundations, including but not limited to senior executives overseeing administrative and operational functions of an organization, logistics and supply chain managers, program managers, directors of field operations, directors of emergency\/disaster preparedness and response, and public health professionals.\u003C\/p\u003E\u003Ch3\u003E\u003Cstrong\u003EHow You Will Benefit\u003C\/strong\u003E\u003C\/h3\u003E\u003Cul\u003E\u003Cli\u003EProvide immediate impact to your organization through learnings gained from applied and real-world case studies.\u003C\/li\u003E\u003Cli\u003ELearn to bring NGOs, businesses, or government entities together to enhance collaboration, cooperation, and communication.\u003C\/li\u003E\u003Cli\u003EDiscover current trends and procedures to help organizations and team members get and stay ahead of the curve.\u003C\/li\u003E\u003Cli\u003EBuild a critical knowledge base to make tactical decisions around inventory, routing, and distribution.\u003C\/li\u003E\u003Cli\u003EDeliver best practices to measure and evaluate the efficiency, impact, and outcomes of focused initiatives or ongoing logistics and supply chain operations.\u003C\/li\u003E\u003Cli\u003ETransform the health and humanitarian sectors with increased capacity to participate in planning and strategic decision-making for effective supply-chain management.\u003C\/li\u003E\u003C\/ul\u003E\u003Ch3\u003E\u003Cstrong\u003EWhat Is Covered\u003C\/strong\u003E\u003C\/h3\u003E\u003Cul\u003E\u003Cli\u003ENetwork modeling approaches\u003C\/li\u003E\u003Cli\u003EForecasting techniques\u003C\/li\u003E\u003Cli\u003EStrategies for making decisions under uncertainty\u003C\/li\u003E\u003Cli\u003EOther data-driven analytical approaches\u003C\/li\u003E\u003C\/ul\u003E\u003Ch3\u003E\u003Cstrong\u003EAbout the Course and the\u0026nbsp;HHSCM Course Series\u003C\/strong\u003E\u003C\/h3\u003E\u003Cp\u003EThis course is the first in a 3-part virtually synchronous professional education program. Register and pay for all three required\u0026nbsp;\u003Ca href=\u0022https:\/\/pe.gatech.edu\/node\/54\u0022\u003EHealth and Humanitarian Supply Chain Management Certificate courses\u003C\/a\u003E\u0026nbsp;and receive a discount of $400 off per course. Enter coupon code\u0026nbsp;\u003Cstrong\u003ESCL-HHS\u003C\/strong\u003E\u0026nbsp;at checkout with the Georgia Tech Professional Education website..\u0026nbsp;\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EAdditionally, there are scholarships available for the certificate program. Apply at \u003Ca href=\u0022https:\/\/chhs.gatech.edu\/course-scholarships\u0022\u003Ehttps:\/\/chhs.gatech.edu\/course-scholarships\u003C\/a\u003E by the noted deadline.\u0026nbsp;\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EQuestions? Reach out to\u0026nbsp;\u003Ca href=\u0022mailto:chhs@gatech.edu\u0022\u003Echhs@gatech.edu\u003C\/a\u003E!\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EThis course examines methods and models for making pre-planning decisions and explores the significant value that is obtained through informed decision-making in advance of an unpredictable event or long-term strategy for sustaining wellness.\u003C\/p\u003E\r\n","format":"limited_html"}],"field_summary_sentence":[{"value":"Explore the significant value that is obtained through informed decision-making in advance of an unpredictable event or long-term development"}],"uid":"27233","created_gmt":"2025-10-17 14:50:35","changed_gmt":"2025-10-17 14:52:40","author":"Andy Haleblian","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2026-03-09T10:00:00-04:00","event_time_end":"2026-03-13T13:30:00-04:00","event_time_end_last":"2026-03-13T13:30:00-04:00","gmt_time_start":"2026-03-09 14:00:00","gmt_time_end":"2026-03-13 17:30:00","gmt_time_end_last":"2026-03-13 17:30:00","rrule":null,"timezone":"America\/New_York"},"location":"Virtual\/Instructor-led","extras":[],"related_links":[{"url":"https:\/\/chhs.gatech.edu\/education\/professional-education","title":"Course Details via Center for Health and Humanitarian Systems website"},{"url":"https:\/\/pe.gatech.edu\/courses\/responsive-supply-chain-design-and-operations","title":"Registration link via Georgia Tech Professional Education"},{"url":"https:\/\/pe.gatech.edu\/certificates\/health-humanitarian-supply-chain-management-certificate","title":"Health \u0026 Humanitarian Supply Chain Management Certificate"},{"url":"https:\/\/chhs.gatech.edu\/course-scholarships","title":"Apply for a Scholarship!"}],"groups":[{"id":"1250","name":"Center for Health and Humanitarian Systems (CHHS)"},{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"},{"id":"1243","name":"The Supply Chain and Logistics Institute (SCL)"}],"categories":[],"keywords":[{"id":"167074","name":"Supply Chain"},{"id":"233","name":"Logistics"},{"id":"8039","name":"Humanitarian"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"10377","name":"Career\/Professional development"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"78771","name":"Public"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[{"value":"\u003Cp\u003Echhs@gatech.edu\u003C\/p\u003E","format":"limited_html"}],"email":[],"slides":[],"orientation":[],"userdata":""}},"685692":{"#nid":"685692","#data":{"type":"event","title":"ISyE Statistic Seminar - Zhu Han","body":[{"value":"\u003Ch3\u003ETitle:\u0026nbsp;\u003C\/h3\u003E\u003Cp\u003EGenerative AI Enabled Semantic Communication\u003C\/p\u003E\u003Ch3\u003EAbstract:\u003C\/h3\u003E\u003Cp\u003ESemantic communication (SemCom), a prominent feature of 6G, aims to address communication problems at the semantic level by transferring semantic information accurately and efficiently. Advances in generative artificial intelligence (GAI), such as the development of large language models and improved generative capabilities, have significantly facilitated the implementation of SemCom. This talk presents three cases of GAI empowering SemCom: The first case is a Swin-Transformer-based dynamic SemCom system that optimizes semantic communication efficiency by dynamically adjusting the compression rate based on network conditions for multi-user scenarios with varying computing capacities. The second case is a federated learning framework designed to enhance global model performance in decentralized environments by leveraging Federated Local Loss (FedLol) for efficient aggregation, reduced communication overhead, and effective image reconstruction. The third case is an AI-generated content framework (AIGC-SCM) for remote monitoring, utilizing GAI to achieve high-fidelity reconstruction of compressed content while maintaining semantic consistency and optimizing energy efficiency. Experimental results and demo confirm the effectiveness of these methods and provide practical insights for integrating SemCom with GAI.\u003C\/p\u003E\u003Ch3\u003EBio:\u0026nbsp;\u003C\/h3\u003E\u003Cp\u003EZhu Han received the B.S. degree in electronic engineering from Tsinghua University, in 1997, and the M.S. and Ph.D. degrees in electrical and computer engineering from the University of Maryland, College Park, in 1999 and 2003, respectively. From 2000 to 2002, he was an R\u0026amp;D Engineer of JDSU, Germantown, Maryland. From 2003 to 2006, he was a Research Associate at the University of Maryland. From 2006 to 2008, he was an assistant professor at Boise State University, Idaho. Currently, he is a John and Rebecca Moores Professor in the Electrical and Computer Engineering Department as well as the Computer Science Department at the University of Houston, Texas. Dr. Han is an NSF CAREER award recipient of 2010, and the winner of the 2021 IEEE Kiyo Tomiyasu Award. He has been an IEEE fellow since 2014, an AAAS fellow since 2020, ACM fellow since 2024, an IEEE Distinguished Lecturer from 2015 to 2018, and an ACM Distinguished Speaker from 2022-2025. Dr. Han is also a 1% highly cited researcher since 2017.\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Ch3\u003EAbstract:\u003C\/h3\u003E\u003Cp\u003ESemantic communication (SemCom), a prominent feature of 6G, aims to address communication problems at the semantic level by transferring semantic information accurately and efficiently. Advances in generative artificial intelligence (GAI), such as the development of large language models and improved generative capabilities, have significantly facilitated the implementation of SemCom. This talk presents three cases of GAI empowering SemCom: The first case is a Swin-Transformer-based dynamic SemCom system that optimizes semantic communication efficiency by dynamically adjusting the compression rate based on network conditions for multi-user scenarios with varying computing capacities. The second case is a federated learning framework designed to enhance global model performance in decentralized environments by leveraging Federated Local Loss (FedLol) for efficient aggregation, reduced communication overhead, and effective image reconstruction. The third case is an AI-generated content framework (AIGC-SCM) for remote monitoring, utilizing GAI to achieve high-fidelity reconstruction of compressed content while maintaining semantic consistency and optimizing energy efficiency. Experimental results and demo confirm the effectiveness of these methods and provide practical insights for integrating SemCom with GAI.\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Generative AI Enabled Semantic Communication"}],"uid":"36767","created_gmt":"2025-10-13 16:43:05","changed_gmt":"2025-10-13 16:47:25","author":"khua31","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2025-10-27T11:00:00-04:00","event_time_end":"2025-10-27T12:00:00-04:00","event_time_end_last":"2025-10-27T12:00:00-04:00","gmt_time_start":"2025-10-27 15:00:00","gmt_time_end":"2025-10-27 16:00:00","gmt_time_end_last":"2025-10-27 16:00:00","rrule":null,"timezone":"America\/New_York"},"location":"Groseclose 402","extras":[],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"177814","name":"Postdoc"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"685581":{"#nid":"685581","#data":{"type":"event","title":"ISyE Seminar - Qiang Huang","body":[{"value":"\u003Cp\u003ETitle: Automated Geometric Qualification of 3D-Printed Products\u003C\/p\u003E\u003Cp\u003EAbstract:\u003C\/p\u003E\u003Cp\u003EGeometric qualification of a product is typically performed by specifying features or regions of interest (ROIs) during design, conducting shape registration to establish correspondence between the inspected product and its design counterpart, and measuring discrepancies for compliance assessment. For complex freeform products, the qualification often requires human intervention to ensure accuracy, particularly in personalized manufacturing through 3D printing. However, geometric variety and complexity can induce operator-to-operator variability due to heterogeneous spatial distributions of geometric distortions. To enable automated product qualification, we propose to specify ROIs as surface patches defined by geometric descriptors indicative of intrinsic deviation patterns. Utilizing these descriptors, ROI specification via shape space dimension reduction, non-rigid intrinsic shape registration, and intrinsic deviation representation can therefore be conducted automatically for product qualification. Finite types of ROIs or surface patches can be extracted based on their intrinsic deviation patterns, independent of covariates such as size and location. A software demo has been developed to implement the qualification process.\u003C\/p\u003E\u003Cp\u003EBio:\u003C\/p\u003E\u003Cp\u003EDr. Qiang Huang is a Professor at the Daniel J. Epstein Department of Industrial and Systems Engineering, University of Southern California (USC), Los Angeles. His research, detailed in his monograph \u0022Domain-informed Machine Learning for Smart Manufacturing\u0022, has been focusing on machine learning for smart manufacturing and quality control for personalized manufacturing. He is an IISE Fellow Award, ASME Fellow, and a senior member of US National Academy of Inventors. He holds eight patents related to quality control in additive manufacturing.\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EGeometric qualification of a product is typically performed by specifying features or regions of interest (ROIs) during design, conducting shape registration to establish correspondence between the inspected product and its design counterpart, and measuring discrepancies for compliance assessment. For complex freeform products, the qualification often requires human intervention to ensure accuracy, particularly in personalized manufacturing through 3D printing. However, geometric variety and complexity can induce operator-to-operator variability due to heterogeneous spatial distributions of geometric distortions. To enable automated product qualification, we propose to specify ROIs as surface patches defined by geometric descriptors indicative of intrinsic deviation patterns. Utilizing these descriptors, ROI specification via shape space dimension reduction, non-rigid intrinsic shape registration, and intrinsic deviation representation can therefore be conducted automatically for product qualification. Finite types of ROIs or surface patches can be extracted based on their intrinsic deviation patterns, independent of covariates such as size and location. A software demo has been developed to implement the qualification process.\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Automated Geometric Qualification of 3D-Printed Products"}],"uid":"36527","created_gmt":"2025-10-07 19:16:53","changed_gmt":"2025-10-07 19:20:37","author":"hulrich6","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2025-10-24T11:00:00-04:00","event_time_end":"2025-10-24T12:00:00-04:00","event_time_end_last":"2025-10-24T12:00:00-04:00","gmt_time_start":"2025-10-24 15:00:00","gmt_time_end":"2025-10-24 16:00:00","gmt_time_end_last":"2025-10-24 16:00:00","rrule":null,"timezone":"America\/New_York"},"location":"Groseclose 402","extras":[],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"177814","name":"Postdoc"},{"id":"78771","name":"Public"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"685574":{"#nid":"685574","#data":{"type":"event","title":"ISyE Seminar - Peihua Qiu","body":[{"value":"\u003Cp\u003ETitle: Forest Expression and Online Monitoring of Dynamic Networks\u003C\/p\u003E\u003Cp\u003EAbstract:\u003C\/p\u003E\u003Cp\u003ENetwork sequences are widely used to describe the longitudinal evolution of dynamic systems. Effective online monitoring of such sequences is crucial for detecting temporal structural changes in these systems. In the statistical process control (SPC) literature, a common approach is to extract key features from observed networks and then apply an SPC chart to monitor these features sequentially over time. However, existing methods often rely on features that are insensitive to certain important structural changes, and the control charts employed may not adequately capture the complex dependence structure among the extracted features. In our recent research, we propose four specific features to characterize the structure of an observed network. Collectively, these features can capture most of the structural changes of interest across various applications. After extracting these features, we employ a multivariate nonparametric control chart for online monitoring. Furthermore, we introduce a novel framework that represents each connected component of a network as a tree and the entire network as a forest. This forest-based representation enables intuitive 3D visualization and provides new structural features that enhance network comparison and monitoring. These new methods for network visualization and monitoring will be discussed in this talk, accompanied by extensive numerical demonstrations.\u003C\/p\u003E\u003Cp\u003EBio:\u003C\/p\u003E\u003Cp\u003EDr. Peihua Qiu is the Dean\u2019s Professor and Founding Chair of the Department of Biostatistics at the University of Florida. He earned his PhD in Statistics from the University of Wisconsin\u2013Madison in 1996. Dr. Qiu has made major contributions to several research areas, including jump regression analysis, image processing, statistical process control, survival analysis, dynamic disease screening, and spatio-temporal disease surveillance. He is the author of three books and more than 180 peer-reviewed journal articles. Dr. Qiu is an elected Fellow of the American Association for the Advancement of Science (AAAS), the American Statistical Association (ASA), the American Society for Quality (ASQ), and the Institute of Mathematical Statistics (IMS), and an elected member of the International Statistical Institute (ISI). He previously served as Editor of Technometrics (2014\u20132016) and is the 2024 recipient of the Shewhart Medal.\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003ENetwork sequences are widely used to describe the longitudinal evolution of dynamic systems. Effective online monitoring of such sequences is crucial for detecting temporal structural changes in these systems. In the statistical process control (SPC) literature, a common approach is to extract key features from observed networks and then apply an SPC chart to monitor these features sequentially over time. However, existing methods often rely on features that are insensitive to certain important structural changes, and the control charts employed may not adequately capture the complex dependence structure among the extracted features. In our recent research, we propose four specific features to characterize the structure of an observed network. Collectively, these features can capture most of the structural changes of interest across various applications. After extracting these features, we employ a multivariate nonparametric control chart for online monitoring. Furthermore, we introduce a novel framework that represents each connected component of a network as a tree and the entire network as a forest. This forest-based representation enables intuitive 3D visualization and provides new structural features that enhance network comparison and monitoring. These new methods for network visualization and monitoring will be discussed in this talk, accompanied by extensive numerical demonstrations.\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Forest Expression and Online Monitoring of Dynamic Networks"}],"uid":"36527","created_gmt":"2025-10-07 13:14:38","changed_gmt":"2025-10-07 13:17:26","author":"hulrich6","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2025-10-31T11:00:00-04:00","event_time_end":"2025-10-31T12:00:00-04:00","event_time_end_last":"2025-10-31T12:00:00-04:00","gmt_time_start":"2025-10-31 15:00:00","gmt_time_end":"2025-10-31 16:00:00","gmt_time_end_last":"2025-10-31 16:00:00","rrule":null,"timezone":"America\/New_York"},"location":"Groseclose 402","extras":[],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"177814","name":"Postdoc"},{"id":"78771","name":"Public"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"685429":{"#nid":"685429","#data":{"type":"event","title":"ISyE Seminar - Ermin Wei","body":[{"value":"\u003Cp\u003ETitle: Incentive Aligned and Robust Distributed Learning Methods\u003C\/p\u003E\u003Cp\u003EAbstract:\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EDistributed and federated learning enables machine learning algorithms to be trained over decentralized edge devices without requiring the exchange of local datasets. We consider two scenarios in this talk. In the first scenario, we have mostly cooperative agents running distributed optimization methods. We analyze how the distribution of data affects agents\u0027 incentives to voluntarily participate and obediently follow traditional federated learning algorithms. We design a Faithful Federated Learning (FFL) mechanism based on FedAvg method and VCG mechanism which achieves (probably approximate) optimality, faithful implementation, voluntary participation, and balanced budget. We then analyze an alternative approach to align individual agent\u2019s incentive to participate by allowing them to opt in or out. We propose a game theoretic framework and study the equilibrium properties with both rational and bounded rational agents. In the second scenario, we turn to a game theoretic formulation, where the agents may be under attack. We characterize the tradeoffs between convergence speed and robustness of learning dynamics.\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EBio:\u003Cstrong\u003E\u0026nbsp;\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003EErmin Wei is an Associate Professor at the Electrical and Computer Engineering Department and Industrial Engineering and Management Sciences Department of Northwestern University. She completed her PhD studies in Electrical Engineering and Computer Science at MIT in 2014, advised by Professor Asu Ozdaglar, where she also obtained her M.S. She received her undergraduate triple degree in Computer Engineering, Finance and Mathematics with a minor in German, from University of Maryland, College Park.\u0026nbsp;Her team won the 2nd place in the GO-competition Challenge 1, an electricity grid optimization competition organized by Department of Energy. Wei\u0027s research interests include distributed optimization methods, convex optimization and analysis, smart grid, communication systems and energy networks and market economic analysis.\u003C\/p\u003E\u003Cdiv\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003C\/div\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EDistributed and federated learning enables machine learning algorithms to be trained over decentralized edge devices without requiring the exchange of local datasets. We consider two scenarios in this talk. In the first scenario, we have mostly cooperative agents running distributed optimization methods. We analyze how the distribution of data affects agents\u0027 incentives to voluntarily participate and obediently follow traditional federated learning algorithms. We design a Faithful Federated Learning (FFL) mechanism based on FedAvg method and VCG mechanism which achieves (probably approximate) optimality, faithful implementation, voluntary participation, and balanced budget. We then analyze an alternative approach to align individual agent\u2019s incentive to participate by allowing them to opt in or out. We propose a game theoretic framework and study the equilibrium properties with both rational and bounded rational agents. In the second scenario, we turn to a game theoretic formulation, where the agents may be under attack. We characterize the tradeoffs between convergence speed and robustness of learning dynamics.\u0026nbsp;\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Incentive Aligned and Robust Distributed Learning Methods"}],"uid":"36527","created_gmt":"2025-10-01 16:50:59","changed_gmt":"2025-10-01 16:54:18","author":"hulrich6","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2025-10-17T11:00:00-04:00","event_time_end":"2025-10-17T12:00:00-04:00","event_time_end_last":"2025-10-17T12:00:00-04:00","gmt_time_start":"2025-10-17 15:00:00","gmt_time_end":"2025-10-17 16:00:00","gmt_time_end_last":"2025-10-17 16:00:00","rrule":null,"timezone":"America\/New_York"},"location":"Groseclose 402","extras":[],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"177814","name":"Postdoc"},{"id":"78771","name":"Public"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"685352":{"#nid":"685352","#data":{"type":"event","title":"ISYE Quantum Computing Seminar - Ojas Parekh","body":[{"value":"\u003Ch2\u003EWhere are the exponential quantum advantages for discrete optimization hiding?\u003C\/h2\u003E\u003Cp\u003E\u003Cstrong\u003ESummary:\u003C\/strong\u003E\u003Cbr\u003EThis seminar examines why exponential quantum advantages for NP-hard discrete optimization remain elusive and explores potential explanations and workarounds through the Maximum Cut problem.\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EAbstract:\u003C\/strong\u003E\u003Cbr\u003EQuantum computing offers hope for realizing exponential advantages over classical computing. Despite over two decades of work studying quantum approaches for discrete optimization, rigorously provable exponential advantages for approximating optimal solutions to NP-hard problems remain scarce. Why? We will discuss a potential explanation and workarounds, using the Maximum Cut problem as a running example.\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003ESpeaker Bio:\u003C\/strong\u003E\u003Cbr\u003EOjas Parekh is a theoretical computer scientist who enjoys applying mathematical techniques to practically motivated interdisciplinary problems. He has worked in a variety of fields including discrete optimization, combinatorics, combinatorial scientific computing, and most recently, quantum and neuromorphic computing. A recent passion is helping shape the emerging fields of quantum approximation algorithms and quantum discrete optimization. He co-directs the Quantum Algorithms and Applications Collaboratory (QuAAC) at Sandia National Laboratories and directs the Department of Energy Fundamental Algorithmic Research toward Quantum Utility project (\u003Ca href=\u0022http:\/\/far-qu.sandia.gov\u0022\u003Efar-qu.sandia.gov\u003C\/a\u003E), a multi-institutional effort tasked with designing novel quantum algorithms to realize advantages over classical computation, especially for optimization, simulation, and machine learning. Ojas believes the universe loves us because pizza exists.\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EThis seminar examines why exponential quantum advantages for NP-hard discrete optimization remain elusive and explores potential explanations and workarounds through the Maximum Cut problem.\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"This seminar explores why exponential quantum speedups for NP-hard discrete optimization remain elusive and highlights potential explanations and workarounds using the Maximum Cut problem as a case study."}],"uid":"36458","created_gmt":"2025-09-29 15:13:50","changed_gmt":"2025-10-01 15:02:58","author":"mellis74","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2025-10-08T11:00:00-04:00","event_time_end":"2025-10-08T12:00:00-04:00","event_time_end_last":"2025-10-08T12:00:00-04:00","gmt_time_start":"2025-10-08 15:00:00","gmt_time_end":"2025-10-08 16:00:00","gmt_time_end_last":"2025-10-08 16:00:00","rrule":null,"timezone":"America\/New_York"},"extras":[],"hg_media":{"678173":{"id":"678173","type":"image","title":"Ojas Parekh","body":null,"created":"1759159970","gmt_created":"2025-09-29 15:32:50","changed":"1759159970","gmt_changed":"2025-09-29 15:32:50","alt":"Ojas Parekh","file":{"fid":"262166","name":"ojas-copy-1024x1024.jpg","image_path":"\/sites\/default\/files\/2025\/09\/29\/ojas-copy-1024x1024_0.jpg","image_full_path":"http:\/\/hg.gatech.edu\/\/sites\/default\/files\/2025\/09\/29\/ojas-copy-1024x1024_0.jpg","mime":"image\/jpeg","size":117385,"path_740":"http:\/\/hg.gatech.edu\/sites\/default\/files\/styles\/740xx_scale\/public\/2025\/09\/29\/ojas-copy-1024x1024_0.jpg?itok=WR2OXn5P"}}},"media_ids":["678173"],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"},{"id":"1243","name":"The Supply Chain and Logistics Institute (SCL)"}],"categories":[],"keywords":[{"id":"4359","name":"quantum computing"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"177814","name":"Postdoc"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"685178":{"#nid":"685178","#data":{"type":"event","title":"SCL Lunch and Learn: \u201cPricing Pressure, Profit Levers: How Smart Companies Are Reshaping the P\u0026L\u0022","body":[{"value":"\u003Cp\u003E\u003Cem\u003E\u003Cstrong\u003ELearn actionable ways to rethink your pricing strategy, rewire your cost model, and build flexibility into your financial future.\u003C\/strong\u003E\u003C\/em\u003E\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EThursday, November 6, 2025 | 12-1pm ET\u003C\/strong\u003E\u003C\/p\u003E\u003Cdiv\u003E\u003Cdiv\u003E\u003Cdiv\u003E\u003Cdiv\u003E\u003Cdiv\u003E\u003Cdiv\u003EWhere are you finding new room to move in your margins? With inflation, new tariffs, and waning consumer demand, pricing has become a balancing act. Many businesses have already squeezed every ounce of efficiency out of their operations. What\u0027s left is a deeper challenge: how to restructure costs without eroding quality or service.\u003C\/div\u003E\u003Cdiv\u003E\u0026nbsp;\u003C\/div\u003E\u003Cdiv\u003EJoin Chris Gaffney and Chuck Easley with the Georgia Tech Supply Chain and Logistics Institute as they discuss actionable ways to rethink your pricing strategy, rewire your cost model, and build flexibility into your financial future.\u003C\/div\u003E\u003C\/div\u003E\u003C\/div\u003E\u003C\/div\u003E\u003C\/div\u003E\u003Cdiv\u003E\u0026nbsp;\u003C\/div\u003E\u003C\/div\u003E\u003Cp\u003E\u003Ca href=\u0022https:\/\/gatech.zoom.us\/webinar\/register\/8617526067420\/WN_UCS543AZTTCgt2_89bPvuA\u0022\u003E\u003Cstrong\u003ERegister Online to Attend\u003C\/strong\u003E\u003C\/a\u003E\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EJoin Chris Gaffney and Chuck Easley with the Georgia Tech Supply Chain and Logistics Institute as they discuss actionable ways to rethink your pricing strategy, rewire your cost model, and build flexibility into your financial future.\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Learn actionable ways to rethink your pricing strategy, rewire your cost model, and build flexibility into your financial future."}],"uid":"27233","created_gmt":"2025-09-23 13:51:59","changed_gmt":"2025-09-23 13:58:09","author":"Andy Haleblian","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2025-11-06T12:00:00-05:00","event_time_end":"2025-11-06T13:00:00-05:00","event_time_end_last":"2025-11-06T13:00:00-05:00","gmt_time_start":"2025-11-06 17:00:00","gmt_time_end":"2025-11-06 18:00:00","gmt_time_end_last":"2025-11-06 18:00:00","rrule":null,"timezone":"America\/New_York"},"location":"Online via Zoom","extras":[],"hg_media":{"678097":{"id":"678097","type":"image","title":"SCL Lunch and Learn: \u201cPricing Pressure, Profit Levers: How Smart Companies Are Reshaping the P\u0026L\u0022","body":null,"created":"1758635779","gmt_created":"2025-09-23 13:56:19","changed":"1758635779","gmt_changed":"2025-09-23 13:56:19","alt":"SCL Lunch and Learn: \u201cPricing Pressure, Profit Levers: How Smart Companies Are Reshaping the P\u0026L\u0022","file":{"fid":"262080","name":"hg_LNL_pricing_20251106.png","image_path":"\/sites\/default\/files\/2025\/09\/23\/hg_LNL_pricing_20251106.png","image_full_path":"http:\/\/hg.gatech.edu\/\/sites\/default\/files\/2025\/09\/23\/hg_LNL_pricing_20251106.png","mime":"image\/png","size":169063,"path_740":"http:\/\/hg.gatech.edu\/sites\/default\/files\/styles\/740xx_scale\/public\/2025\/09\/23\/hg_LNL_pricing_20251106.png?itok=gi0ZEBNE"}}},"media_ids":["678097"],"related_links":[{"url":"https:\/\/gatech.zoom.us\/webinar\/register\/8617526067420\/WN_UCS543AZTTCgt2_89bPvuA","title":"Register Online to Attend the Webinar"},{"url":"https:\/\/www.scl.gatech.edu\/education\/professional-education","title":"Professional Education at the Georgia Tech Supply Chain and Logistics Institute"}],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"},{"id":"1243","name":"The Supply Chain and Logistics Institute (SCL)"}],"categories":[],"keywords":[{"id":"3615","name":"information session"},{"id":"186857","name":"go-gtmi"},{"id":"122741","name":"physical internet"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"10377","name":"Career\/Professional development"}],"invited_audience":[{"id":"78771","name":"Public"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[{"value":"\u003Cp\u003Ecourse@scl.gatech.edu\u003C\/p\u003E","format":"limited_html"}],"email":[],"slides":[],"orientation":[],"userdata":""}},"685147":{"#nid":"685147","#data":{"type":"event","title":"ISyE Statistic Seminar \u2013 Tianbao Yang","body":[{"value":"\u003Cdiv\u003E\u003Ch3\u003E\u003Cstrong\u003ETitle:\u003C\/strong\u003E\u0026nbsp;\u003C\/h3\u003E\u003Cp\u003ENew Applications and Algorithms of Distributionally Robust Optimization in AI\u003C\/p\u003E\u003Ch3\u003E\u003Cstrong\u003EAbstract:\u003C\/strong\u003E\u003C\/h3\u003E\u003Cp\u003EIn this talk, I will present our recent research on new applications and algorithms of Distributionally Robust Optimization (DRO) in AI, with a particular focus on training large foundation models such as contrastive language\u2013image pretraining (CLIP) models. I will introduce a new learning framework, \u003Cstrong\u003EDRRHO risk minimization\u003C\/strong\u003E, which leverages open-weight models to accelerate the training of target models on custom datasets, and demonstrate its application to CLIP. By formulating the problem as a new class of finite-sum coupled compositional optimization, I will discuss how to design efficient algorithms with provable convergence guarantees. Finally, I will highlight the broader applications of these techniques across machine learning and AI.\u003C\/p\u003E\u003C\/div\u003E\u003Ch3\u003EBio:\u003C\/h3\u003E\u003Cdiv\u003ETianbao Yang is a Professor and Herbert H. Richardson Faculty Fellow at CSE department of Texas A\u0026amp;M University, where he directs the lab of Optimization for Machine learning and AI (OptMAI Lab). His research interests center around optimization, machine learning and AI with applications in \u0026nbsp;trustworthy AI and medicine. Before joining TAMU, he was an assistant professor and then tenured Dean\u0027s Excellence associate professor at the Computer Science Department of the University of Iowa from 2014 to 2022. Before that, he worked in Silicon Valley as Machine Learning Researcher for two years at GE Research and NEC Labs. He received the Best Student Paper Award of COLT in 2012, and the NSF Career Award in 2019. He is the founder of the widely used\u0026nbsp;\u003Ca href=\u0022http:\/\/www.libauc.org\/\u0022 rel=\u0022noopener noreferrer\u0022 target=\u0022_blank\u0022 title=\u0022http:\/\/www.libauc.org\/\u0022\u003ELibAUC library\u003C\/a\u003E. He is associate editor of multiple journals, including IEEE Transactions on Pattern Analysis and Machine Intelligence.\u0026nbsp;\u003C\/div\u003E\u003Cdiv\u003E\u003Cbr\u003E\u0026nbsp;\u003C\/div\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Ch3\u003E\u003Cstrong\u003EAbstract:\u003C\/strong\u003E\u003C\/h3\u003E\u003Cp\u003EIn this talk, I will present our recent research on new applications and algorithms of Distributionally Robust Optimization (DRO) in AI, with a particular focus on training large foundation models such as contrastive language\u2013image pretraining (CLIP) models. I will introduce a new learning framework, \u003Cstrong\u003EDRRHO risk minimization\u003C\/strong\u003E, which leverages open-weight models to accelerate the training of target models on custom datasets, and demonstrate its application to CLIP. By formulating the problem as a new class of finite-sum coupled compositional optimization, I will discuss how to design efficient algorithms with provable convergence guarantees. Finally, I will highlight the broader applications of these techniques across machine learning and AI.\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"New Applications and Algorithms of Distributionally Robust Optimization in AI"}],"uid":"36767","created_gmt":"2025-09-22 16:37:14","changed_gmt":"2025-09-22 16:41:09","author":"khua31","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2025-10-21T11:00:00-04:00","event_time_end":"2025-10-21T12:00:00-04:00","event_time_end_last":"2025-10-21T12:00:00-04:00","gmt_time_start":"2025-10-21 15:00:00","gmt_time_end":"2025-10-21 16:00:00","gmt_time_end_last":"2025-10-21 16:00:00","rrule":null,"timezone":"America\/New_York"},"location":"Groseclose 402","extras":[],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"177814","name":"Postdoc"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"684507":{"#nid":"684507","#data":{"type":"event","title":"ISyE Seminar - Art Owen","body":[{"value":"\u003Cp\u003ETitle: Composite Likelihood for a Very Large Scale Binary Regression with Crossed Random Effects\u003C\/p\u003E\u003Cp\u003ESummary:\u003C\/p\u003E\u003Cp\u003ESparsely sampled crossed random effects models arise in product review data, with effects for customers crossed with effects for products. The settings have no balance and the least squares algebra grows as N^(3\/2) or worse. For generalized linear mixed models (GLMMs) there is the further difficulty of a very high dimensional integral. For instance we consider a likelihood with an integral over D~700,000 random effects, using only N~5,000,000 observations. The usual Laplace approximation method evaluates the D dimensional integral using just one integration point, and there is uncertainty about whether that is reliable. The MLE is infeasible in this problem and has only recently been shown to be consistent (Jiang, 2013). For a probit model, we develop a composite likelihood approach based on computing D one dimensional integrals. It is very scalable and we prove consistency which might not hold for the Laplace based method.\u003C\/p\u003E\u003Cp\u003EThis is based on joint work with Ruggero Bellio and Swarnadip Ghosh and Cristiano Varin.\u003C\/p\u003E\u003Cp\u003EBio:\u003C\/p\u003E\u003Cp\u003EArt Owen is the Max H. Stein Professor of Statistics at Stanford University. He is best known for inventing the empirical likelihood and for developing and studying randomized quasi-Monte Carlo methods. His research interests are centered on ways to measure uncertainty and ways to sample. He is a fellow of the American Statistical Association and the Institute of Mathematical Statistics. He received the 2020 Senior Noether Prize in nonparametric statistics from the ASA and the 2021 Gold Medal from the Statistical Society of Canada and became a SIAM Fellow in 2024.\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003ESparsely sampled crossed random effects models arise in product review data, with effects for customers crossed with effects for products. The settings have no balance and the least squares algebra grows as N^(3\/2) or worse. For generalized linear mixed models (GLMMs) there is the further difficulty of a very high dimensional integral. For instance we consider a likelihood with an integral over D~700,000 random effects, using only N~5,000,000 observations. The usual Laplace approximation method evaluates the D dimensional integral using just one integration point, and there is uncertainty about whether that is reliable. The MLE is infeasible in this problem and has only recently been shown to be consistent (Jiang, 2013). For a probit model, we develop a composite likelihood approach based on computing D one dimensional integrals. It is very scalable and we prove consistency which might not hold for the Laplace based method.\u003C\/p\u003E\u003Cp\u003E\r\n\r\nThis is based on joint work with Ruggero Bellio and Swarnadip Ghosh\r\nand Cristiano Varin.\r\n\u003Cbr\u003E\u0026nbsp;\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Composite Likelihood for a Very Large Scale Binary Regression with Crossed Random Effects"}],"uid":"36527","created_gmt":"2025-09-05 19:50:00","changed_gmt":"2025-09-08 13:46:11","author":"hulrich6","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2025-09-19T11:00:00-04:00","event_time_end":"2025-09-19T12:00:00-04:00","event_time_end_last":"2025-09-19T12:00:00-04:00","gmt_time_start":"2025-09-19 15:00:00","gmt_time_end":"2025-09-19 16:00:00","gmt_time_end_last":"2025-09-19 16:00:00","rrule":null,"timezone":"America\/New_York"},"location":"Groseclose 402","extras":[],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"177814","name":"Postdoc"},{"id":"78771","name":"Public"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"684403":{"#nid":"684403","#data":{"type":"event","title":"ISyE Picture Day - Ph.D. STUDENTS","body":[{"value":"\u003Cp\u003EDon\u0027t miss getting your updated headshot for the\u0026nbsp;2025-2026 academic year!\u0026nbsp;If you cannot make it during this time, please\u0026nbsp;\u003Ca href=\u0022https:\/\/outlook.office.com\/book\/ISyEComms@gtvault.onmicrosoft.com\/?ismsaljsauthenabled\u0022\u003Eclick here\u003C\/a\u003E\u0026nbsp;to schedule an appointment with us in the future.\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E(\u003Cem\u003E*If availability doesn\u2019t match, participants can select next best day for their schedule)\u003C\/em\u003E\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EAnnual ISyE Picture Day for the\u0026nbsp;2025-2026 academic year.\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Annual ISyE Picture Day for the\u00a02025-2026 academic year."}],"uid":"36736","created_gmt":"2025-09-04 13:13:06","changed_gmt":"2025-09-04 13:16:03","author":"ebrown386","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2025-09-25T09:00:00-04:00","event_time_end":"2025-09-25T14:00:00-04:00","event_time_end_last":"2025-09-25T14:00:00-04:00","gmt_time_start":"2025-09-25 13:00:00","gmt_time_end":"2025-09-25 18:00:00","gmt_time_end_last":"2025-09-25 18:00:00","rrule":null,"timezone":"America\/New_York"},"location":"Cecil G. Johnson Studio - ISyE Main, 1st Floor ","extras":[],"hg_media":{"677904":{"id":"677904","type":"image","title":"ISyE Picture Day","body":null,"created":"1756991454","gmt_created":"2025-09-04 13:10:54","changed":"1756991454","gmt_changed":"2025-09-04 13:10:54","alt":"ISyE Picture Day ","file":{"fid":"261858","name":"Fall-2025--2-.png","image_path":"\/sites\/default\/files\/2025\/09\/04\/Fall-2025--2-_0.png","image_full_path":"http:\/\/hg.gatech.edu\/\/sites\/default\/files\/2025\/09\/04\/Fall-2025--2-_0.png","mime":"image\/png","size":1743279,"path_740":"http:\/\/hg.gatech.edu\/sites\/default\/files\/styles\/740xx_scale\/public\/2025\/09\/04\/Fall-2025--2-_0.png?itok=-Pcc6fWm"}}},"media_ids":["677904"],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"10377","name":"Career\/Professional development"}],"invited_audience":[{"id":"174045","name":"Graduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"684402":{"#nid":"684402","#data":{"type":"event","title":"ISyE Picture Day - FACULTY","body":[{"value":"\u003Cp\u003EISyE Faculty: Don\u0027t miss getting your updated headshot for the\u0026nbsp;2025-2026 academic year!\u0026nbsp;If you cannot make it during this time, please\u0026nbsp;\u003Ca href=\u0022https:\/\/outlook.office.com\/book\/ISyEComms@gtvault.onmicrosoft.com\/?ismsaljsauthenabled\u0022\u003Eclick here\u003C\/a\u003E\u0026nbsp;to schedule an appointment with us in the future.\u0026nbsp;\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EAnnual ISyE Picture Day for the\u0026nbsp;2025-2026 academic year.\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Annual ISyE Picture Day for the\u00a02025-2026 academic year."}],"uid":"36736","created_gmt":"2025-09-04 13:09:22","changed_gmt":"2025-09-04 13:11:47","author":"ebrown386","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2025-09-24T09:00:00-04:00","event_time_end":"2025-09-24T14:00:00-04:00","event_time_end_last":"2025-09-24T14:00:00-04:00","gmt_time_start":"2025-09-24 13:00:00","gmt_time_end":"2025-09-24 18:00:00","gmt_time_end_last":"2025-09-24 18:00:00","rrule":null,"timezone":"America\/New_York"},"location":"Cecil G. Johnson Studio - ISyE Main, 1st Floor ","extras":[],"hg_media":{"677904":{"id":"677904","type":"image","title":"ISyE Picture Day","body":null,"created":"1756991454","gmt_created":"2025-09-04 13:10:54","changed":"1756991454","gmt_changed":"2025-09-04 13:10:54","alt":"ISyE Picture Day ","file":{"fid":"261858","name":"Fall-2025--2-.png","image_path":"\/sites\/default\/files\/2025\/09\/04\/Fall-2025--2-_0.png","image_full_path":"http:\/\/hg.gatech.edu\/\/sites\/default\/files\/2025\/09\/04\/Fall-2025--2-_0.png","mime":"image\/png","size":1743279,"path_740":"http:\/\/hg.gatech.edu\/sites\/default\/files\/styles\/740xx_scale\/public\/2025\/09\/04\/Fall-2025--2-_0.png?itok=-Pcc6fWm"}}},"media_ids":["677904"],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"10377","name":"Career\/Professional development"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"684400":{"#nid":"684400","#data":{"type":"event","title":"ISyE Picture Day - STAFF","body":[{"value":"\u003Cp\u003EDon\u0027t miss getting your updated headshot for the \u003Cstrong\u003E2025-2026 academic year! \u003C\/strong\u003EIf you cannot make it during this time, please \u003Ca href=\u0022https:\/\/outlook.office.com\/book\/ISyEComms@gtvault.onmicrosoft.com\/?ismsaljsauthenabled\u0022\u003Eclick here\u003C\/a\u003E to schedule an appointment with us in the future.\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EAnnual ISyE Picture Day for the 2025-2026 academic year.\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Annual day for staff to secure or refresh professional headshots"}],"uid":"36736","created_gmt":"2025-09-04 12:55:15","changed_gmt":"2025-09-04 13:08:21","author":"ebrown386","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2025-09-23T09:00:00-04:00","event_time_end":"2025-09-23T14:00:00-04:00","event_time_end_last":"2025-09-23T14:00:00-04:00","gmt_time_start":"2025-09-23 13:00:00","gmt_time_end":"2025-09-23 18:00:00","gmt_time_end_last":"2025-09-23 18:00:00","rrule":null,"timezone":"America\/New_York"},"location":"Cecil G. Johnson Studio - ISyE Main, 1st Floor ","extras":[],"hg_media":{"677902":{"id":"677902","type":"image","title":"ISyE Picture Day","body":null,"created":"1756990639","gmt_created":"2025-09-04 12:57:19","changed":"1756990639","gmt_changed":"2025-09-04 12:57:19","alt":"ISyE Picture Day ","file":{"fid":"261856","name":"Fall-2025--2-.png","image_path":"\/sites\/default\/files\/2025\/09\/04\/Fall-2025--2-.png","image_full_path":"http:\/\/hg.gatech.edu\/\/sites\/default\/files\/2025\/09\/04\/Fall-2025--2-.png","mime":"image\/png","size":1743279,"path_740":"http:\/\/hg.gatech.edu\/sites\/default\/files\/styles\/740xx_scale\/public\/2025\/09\/04\/Fall-2025--2-.png?itok=6mD9m3Zc"}}},"media_ids":["677902"],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"10377","name":"Career\/Professional development"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"684391":{"#nid":"684391","#data":{"type":"event","title":"ISyE Statistic Seminar \u2013 Xiaotong Shen","body":[{"value":"\u003Ch3\u003ETitle:\u0026nbsp;\u003C\/h3\u003E\u003Cp\u003EISyE Statistic Seminar \u2013 Xiaotong Shen\u003C\/p\u003E\u003Ch3\u003EAbstract:\u0026nbsp;\u003C\/h3\u003E\u003Cp\u003ESynthetic data generation is reshaping data science by addressing challenges of scarcity, privacy, and imbalance. Recent advances in generative modeling enable the creation of high-fidelity datasets that capture complex distributions across modalities. These models not only expand data volume but also improve prediction accuracy, often outperforming conventional predictive methods, and can serve as a resampling tool for inference, much like the bootstrap. Moreover, they enhance multimodal analysis and provide targeted data augmentation for applied problems such as class imbalance.\u003C\/p\u003E\u003Cp\u003EThrough case studies in sentiment analysis, predictive modeling, and tabular inference, we demonstrate how generative models enrich supervised learning, strengthen statistical inference, and provide scalable solutions when raw data are limited or biased.\u003C\/p\u003E\u003Cp\u003EThis work is joint with Y. Liu, R. Shen, and X. Tian.\u003C\/p\u003E\u003Ch3\u003EBio:\u0026nbsp;\u003C\/h3\u003E\u003Cp\u003EXiaotong T. Shen is the John Black Johnston Distinguished Professor in the College of Liberal Arts at the University of Minnesota. He earned his Ph.D. in Statistics from the University of Chicago in 1991.\u003C\/p\u003E\u003Cp\u003EProfessor Shen specializes in machine learning and data science, high-dimensional inference, non\/semi-parametric inference, causal relations, graphical models, explainable Machine Intelligence (MI), personalization, recommender systems, natural language processing, generative modeling, and nonconvex minimization. His current research efforts are devoted to further developing causal and constrained inference, generative inference and prediction for black-box learners, and diffusion, normalizing flows, and summarization. The targeted application areas are biomedical sciences, artificial intelligence, and engineering.\u003C\/p\u003E\u003Cp\u003EHe has served on the editorial boards of leading journals\u2014including JMLR, JASA, and the Annals of Statistics\u2014and remains deeply involved in professional service, currently as Chair-Elect of the ASA\u2019s Section on Statistical Learning and Data Science. His distinctions include election as a Fellow of the Institute of Mathematical Statistics, the American Statistical Association, and AAAS, along with honors such as the \u201cScholar of the College\u201d award and the ICSA Distinguished Achievement Award.\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Ch3\u003EAbstract:\u0026nbsp;\u003C\/h3\u003E\u003Cp\u003ESynthetic data generation is reshaping data science by addressing challenges of scarcity, privacy, and imbalance. Recent advances in generative modeling enable the creation of high-fidelity datasets that capture complex distributions across modalities. These models not only expand data volume but also improve prediction accuracy, often outperforming conventional predictive methods, and can serve as a resampling tool for inference, much like the bootstrap. Moreover, they enhance multimodal analysis and provide targeted data augmentation for applied problems such as class imbalance.\u003C\/p\u003E\u003Cp\u003EThrough case studies in sentiment analysis, predictive modeling, and tabular inference, we demonstrate how generative models enrich supervised learning, strengthen statistical inference, and provide scalable solutions when raw data are limited or biased.\u003C\/p\u003E\u003Cp\u003EThis work is joint with Y. Liu, R. Shen, and X. Tian.\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Boosting Data Analytics with High-fidelity Synthetic Data"}],"uid":"36767","created_gmt":"2025-09-03 18:15:58","changed_gmt":"2025-09-03 18:18:08","author":"khua31","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2025-09-30T11:00:00-04:00","event_time_end":"2025-09-30T12:00:00-04:00","event_time_end_last":"2025-09-30T12:00:00-04:00","gmt_time_start":"2025-09-30 15:00:00","gmt_time_end":"2025-09-30 16:00:00","gmt_time_end_last":"2025-09-30 16:00:00","rrule":null,"timezone":"America\/New_York"},"location":"Groseclose 402","extras":[],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"177814","name":"Postdoc"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"683875":{"#nid":"683875","#data":{"type":"event","title":"SCL IRC Seminar: \u0022Data Driven: From Start-Up to Global Leader - A Founder\u0027s Perspective\u0022","body":[{"value":"\u003Cp\u003EThe \u003Ca href=\u0022https:\/\/www.scl.gatech.edu\u0022\u003ESupply Chain and Logistics Institute\u003C\/a\u003E (SCL) and \u003Ca href=\u0022https:\/\/www.scl.gatech.edu\/outreach\/SCLO\u0022\u003ESupply Chain and Logistics Organization\u003C\/a\u003E (SCLO) Student Group is co-hosting a special event seminar featuring \u003Cstrong\u003ENeil Cawse\u003C\/strong\u003E, CEO and founder of \u003Ca href=\u0022https:\/\/www.geotab.com\/\u0022\u003EGeotab\u003C\/a\u003E.\u003C\/p\u003E\u003Ch2\u003E\u003Cstrong\u003EData Driven: From Start-Up to Global Leader - A Founder\u0027s Perspective\u003C\/strong\u003E\u003C\/h2\u003E\u003Ch3\u003Efeaturing \u003Ca href=\u0022https:\/\/www.geotab.com\/blog\/author\/neil-cawse\/\u0022\u003ENeil Cawse\u003C\/a\u003E, CEO and founder of Geotab\u003C\/h3\u003E\u003Ch4\u003EWednesday, September 10, 2025\u003Cbr\u003E9:15-10:00am: Breakfast\/Networking (Centennial Room)\u003Cbr\u003E\u003Cstrong\u003E10:00-11:00am: Seminar\u003C\/strong\u003E (Home Park Room)\u003C\/h4\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Ch4\u003E\u003Cstrong\u003EVenue\/Location\u003C\/strong\u003E\u003C\/h4\u003E\u003Cp\u003E\u003Ca href=\u0022https:\/\/studentcenter.gatech.edu\/exhibition-hall\u0022\u003EGeorgia Tech Exhibition Hall\u003C\/a\u003E (\u003Ca href=\u0022https:\/\/studentcenter.gatech.edu\/exhibition-hall-map\u0022\u003E2nd floor\u003C\/a\u003E)\u0026nbsp;\u003Cbr\u003E\u003Cstrong\u003ECentennial Room 9:15-10am \u003C\/strong\u003E\u003Cem\u003E\u003Cstrong\u003EBreakfast\/Networking\u003C\/strong\u003E\u003C\/em\u003E\u003Cbr\u003E\u003Cstrong\u003EHome Park Room 10-11am Main Seminar\u003C\/strong\u003E\u003Cbr\u003EGeorgia Institute of Technology\u003Cbr\u003E460 4th St NW, Atlanta, GA 30332\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Ch4\u003E\u003Cstrong\u003EABOUT THE SEMINAR\u003C\/strong\u003E\u003C\/h4\u003E\u003Cp\u003ENeil Cawse will share his insights on:\u003C\/p\u003E\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EThe Entrepreneurial Mindset\u003C\/strong\u003E: The essential way of thinking to navigate uncertainty and turn problems into opportunities.\u003C\/li\u003E\u003Cli\u003E\u003Cstrong\u003EBuilding a Data-Driven Business\u003C\/strong\u003E: How to use data as your compass to make smart decisions, from your first MVP to strategic scaling.\u003C\/li\u003E\u003Cli\u003E\u003Cstrong\u003EHard-Won Lessons\u003C\/strong\u003E: The critical takeaways from mistakes and successes, and why a great team and a focus on the customer are non-negotiable.\u003C\/li\u003E\u003Cli\u003E\u003Cstrong\u003ESkills for Today \u0026amp; Tomorrow\u003C\/strong\u003E: The key skills, from data literacy, risk taking and critical thinking, that you need to stay ahead in a constantly evolving landscape.\u003C\/li\u003E\u003C\/ul\u003E\u003Ch4\u003E\u003Cstrong\u003EABOUT THE SPEAKER (\u003C\/strong\u003E\u003Ca href=\u0022https:\/\/digitalmag.theceomagazine.com\/neil-cawse\/?r=us\u0022 target=\u0022_blank\u0022\u003E\u003Cstrong\u003ECEO Magazine article\u003C\/strong\u003E\u003C\/a\u003E\u003Cstrong\u003E)\u003C\/strong\u003E\u003C\/h4\u003E\u003Cp\u003ENeil Cawse, CEO and founder of Geotab, stands at the forefront of connected vehicle technology, leading the industry in innovation and research. His leadership is marked by a commitment to developing cutting-edge technologies that empower businesses to make data-driven decisions. Under Neil\u2019s guidance, Geotab has transformed into a global leader, providing advanced data analytics and insights to businesses worldwide. Neil\u2019s entrepreneurial spirit and passion for technology have been the driving forces behind Geotab\u0027s mission to improve vehicle safety, operational efficiency, and sustainability. With a keen focus on AI, IoT and connected vehicles, he has steered the company through exponential growth, expanding its reach to millions of subscriptions in over 160 countries. Neil\u0027s vision is to create a more connected and sustainable world through the power of data and innovation.\u003C\/p\u003E\u003Cp\u003E\u003Cem\u003EAdditional speakers include\u003C\/em\u003E: \u003Ca href=\u0022https:\/\/www.geotab.com\/blog\/author\/mike-branch\/\u0022\u003E\u003Cstrong\u003EMike Branch\u003C\/strong\u003E\u003C\/a\u003E (Geotab VP of Data \u0026amp; Analytics), \u003Ca href=\u0022https:\/\/www.linkedin.com\/in\/john-ballantyne-76a16217\/\u0022\u003E\u003Cstrong\u003EJohn Ballantyne\u003C\/strong\u003E\u003C\/a\u003E (Geotab VP of Research and Innovation), \u003Ca href=\u0022https:\/\/www.linkedin.com\/in\/samuel-harris-ga\/\u0022\u003E\u003Cstrong\u003ESamuel Harris\u003C\/strong\u003E\u003C\/a\u003E (GDOT Asst State Traffic Engineer), \u003Ca href=\u0022https:\/\/www.isye.gatech.edu\/users\/benoit-montreuil\u0022\u003E\u003Cstrong\u003EBenoit Montreuil\u003C\/strong\u003E\u003C\/a\u003E (SCL Executive Director), \u003Ca href=\u0022https:\/\/ce.gatech.edu\/directory\/person\/yi-chang-james-tsai\u0022\u003E\u003Cstrong\u003EYichang (James) Tsai\u003C\/strong\u003E\u003C\/a\u003E (Prof, Civil and Environmental Engr), \u003Ca href=\u0022https:\/\/ce.gatech.edu\/directory\/person\/sofia-perez-guzman\u0022\u003E\u003Cstrong\u003ESofia Perez-Guzman\u003C\/strong\u003E\u003C\/a\u003E (Asst Prof, Civil and Environmental Engr), \u003Ca href=\u0022https:\/\/www.isye.gatech.edu\/users\/chris-gaffney\u0022\u003E\u003Cstrong\u003EChris Gaffney\u003C\/strong\u003E\u003C\/a\u003E (SCL Managing Director)\u003C\/p\u003E\u003Ch4\u003E\u003Cstrong\u003EIncluded Panel Discussion | Telematics Data: Beyond Fleet Management\u003C\/strong\u003E\u003C\/h4\u003E\u003Cp\u003EThe panelists will review how Traditional AI has improved fleet operations in safety, maintenance, and efficiency. They will then examine how Generative and Agentic AI can automate aspects of fleet management. Finally, the discussion will shift to how transportation planners, operators, and policymakers can benefit from anonymized geotemporal data.\u003C\/p\u003E\u003Ch3\u003E\u003Ca href=\u0022https:\/\/eforms.scl.gatech.edu\/geotab-day\u0022\u003E\u003Cstrong\u003ERegister Online for our Special Event Seminar\u003C\/strong\u003E\u003C\/a\u003E\u003C\/h3\u003E\u003Cp\u003E\u003Cbr\u003EFor questions, please email \u003Ca href=\u0022mailto:event@scl.gatech.edu\u0022\u003Eevent@scl.gatech.edu\u003C\/a\u003E.\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EThe \u003Ca href=\u0022https:\/\/www.scl.gatech.edu\u0022\u003ESupply Chain and Logistics Institute\u003C\/a\u003E (SCL) and \u003Ca href=\u0022https:\/\/www.scl.gatech.edu\/outreach\/SCLO\u0022\u003ESupply Chain and Logistics Organization\u003C\/a\u003E (SCLO) Student Group is co-hosting a special event seminar featuring \u003Cstrong\u003ENeil Cawse\u003C\/strong\u003E, CEO and founder of \u003Ca href=\u0022https:\/\/www.geotab.com\/\u0022\u003EGeotab\u003C\/a\u003E.\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Join the Supply Chain and Logistics Institute for a special event seminar featuring Neil Cawse, CEO and founder of Geotab."}],"uid":"27233","created_gmt":"2025-08-15 19:47:00","changed_gmt":"2025-09-03 13:48:47","author":"Andy Haleblian","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2025-09-10T09:15:00-04:00","event_time_end":"2025-09-10T11:00:00-04:00","event_time_end_last":"2025-09-10T11:00:00-04:00","gmt_time_start":"2025-09-10 13:15:00","gmt_time_end":"2025-09-10 15:00:00","gmt_time_end_last":"2025-09-10 15:00:00","rrule":null,"timezone":"America\/New_York"},"location":"Georgia Tech Exhibition Hall (2nd floor) - Centennial Room (Breakfast) \u0026 Home Park Room (Home Park)","extras":["free_food"],"hg_media":{"677764":{"id":"677764","type":"image","title":"SCL IRC Seminar: \u0022Data Driven: From Start-Up to Global Leader - A Founder\u0027s Perspective\u0022 with Neil Cawse, CEO and founder of Geotab","body":null,"created":"1755796957","gmt_created":"2025-08-21 17:22:37","changed":"1755796957","gmt_changed":"2025-08-21 17:22:37","alt":"SCL IRC Seminar: \u0022Data Driven: From Start-Up to Global Leader - A Founder\u0027s Perspective\u0022 with Neil Cawse, CEO and founder of Geotab","file":{"fid":"261700","name":"GT-SCLIRC_Seminar_GeotabDay.jpg","image_path":"\/sites\/default\/files\/2025\/08\/21\/GT-SCLIRC_Seminar_GeotabDay.jpg","image_full_path":"http:\/\/hg.gatech.edu\/\/sites\/default\/files\/2025\/08\/21\/GT-SCLIRC_Seminar_GeotabDay.jpg","mime":"image\/jpeg","size":578184,"path_740":"http:\/\/hg.gatech.edu\/sites\/default\/files\/styles\/740xx_scale\/public\/2025\/08\/21\/GT-SCLIRC_Seminar_GeotabDay.jpg?itok=9JcpFpf-"}}},"media_ids":["677764"],"related_links":[{"url":"https:\/\/eforms.scl.gatech.edu\/geotab-day","title":"Register Online for our Special Event SCL IRC seminar"},{"url":"https:\/\/www.scl.gatech.edu\/sites\/default\/files\/downloads\/sclirc\/GT-SCLIRC_Seminar_GeotabDay_20250910.pdf","title":"Download the Event Flyer"}],"groups":[{"id":"1250","name":"Center for Health and Humanitarian Systems (CHHS)"},{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"},{"id":"1243","name":"The Supply Chain and Logistics Institute (SCL)"}],"categories":[],"keywords":[{"id":"167074","name":"Supply Chain"},{"id":"233","name":"Logistics"},{"id":"122741","name":"physical internet"},{"id":"186857","name":"go-gtmi"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"78771","name":"Public"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[{"value":"\u003Cp\u003Eevent@scl.gatech.edu\u003C\/p\u003E","format":"limited_html"}],"email":[],"slides":[],"orientation":[],"userdata":""}},"684199":{"#nid":"684199","#data":{"type":"event","title":"ISyE Seminar - Wei Biao Wu","body":[{"value":"\u003Cdiv\u003ETitle: Concentration Bounds for Statistical Learning for Time Dependent Data\u003C\/div\u003E\u003Cdiv\u003E\u0026nbsp;\u003C\/div\u003E\u003Cp\u003ESummary:\u003C\/p\u003E\u003Cp\u003EClassical statistical learning theory primarily concerns independent data. In comparison, it has been much less investigated for time dependent data, which are commonly encountered in economics, engineering, finance, geography, physics and other fields. In this\u0026nbsp;talk, we focus on concentration inequalities for suprema of empirical processes which plays a fundamental role in the statistical learning theory. We derive a Gaussian approximation and an upper bound for the tail probability of the suprema under conditions on the size of the function class, the sample size, temporal dependence and the moment conditions of the underlying time series. Due to the dependence and heavy-tailness, our tail probability bound is substantially different from those classical exponential bounds obtained under the independence assumption in that it involves an extra polynomial decaying term. We allow both short- and long-range dependent processes, where the long-range dependence case has never been previously explored. We showed our tail probability inequality is sharp up to a multiplicative constant. These bounds work as theoretical guarantees for statistical learning applications under dependence.\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EBio:\u003C\/p\u003E\u003Cp\u003EWei Biao Wu earned his Ph.D. in Statistics from the University of Michigan, Ann Arbor, in 2001. He is a Professor in the Department of Statistics at The University of Chicago, Chicago, IL. His research focuses on developing large-sample theory for time-dependent data, introducing the innovative functional dependence measure to establish a robust theoretical framework for asymptotic analysis. He has also designed efficient online algorithms for computing time-series statistics. His research interests span probability theory, statistics, and econometrics.\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EClassical statistical learning theory primarily concerns independent data. In comparison, it has been much less investigated for time dependent data, which are commonly encountered in economics, engineering, finance, geography, physics and other fields. In this\u0026nbsp;talk, we focus on concentration inequalities for suprema of empirical processes which plays a fundamental role in the statistical learning theory. We derive a Gaussian approximation and an upper bound for the tail probability of the suprema under conditions on the size of the function class, the sample size, temporal dependence and the moment conditions of the underlying time series. Due to the dependence and heavy-tailness, our tail probability bound is substantially different from those classical exponential bounds obtained under the independence assumption in that it involves an extra polynomial decaying term. We allow both short- and long-range dependent processes, where the long-range dependence case has never been previously explored. We showed our tail probability inequality is sharp up to a multiplicative constant. These bounds work as theoretical guarantees for statistical learning applications under dependence.\u0026nbsp;\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Concentration Bounds for Statistical Learning for Time Dependent Data"}],"uid":"36527","created_gmt":"2025-08-26 19:34:07","changed_gmt":"2025-08-26 19:38:10","author":"hulrich6","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2025-10-03T11:00:00-04:00","event_time_end":"2025-10-03T12:00:00-04:00","event_time_end_last":"2025-10-03T12:00:00-04:00","gmt_time_start":"2025-10-03 15:00:00","gmt_time_end":"2025-10-03 16:00:00","gmt_time_end_last":"2025-10-03 16:00:00","rrule":null,"timezone":"America\/New_York"},"location":"Groseclose 402","extras":[],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"177814","name":"Postdoc"},{"id":"78771","name":"Public"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"684176":{"#nid":"684176","#data":{"type":"event","title":"ISyE Seminar - Stefan Wild","body":[{"value":"\u003Cdiv\u003ETitle: Improving the Practical Scalability and Robustness of Zeroth-Order Optimization Solvers\u003Cbr\u003E\u003Cbr\u003EAbstract:\u0026nbsp;\u003C\/div\u003E\u003Cdiv\u003EZeroth-order optimization solvers are often deployed in settings where little information regarding a problem\u0027s conditioning or noise level is known. Effective solvers must handle complex applications -- from automated materials discovery to quantum circuit compilation -- each presenting unique challenges. These problem features often limit the scale of the problems on which zeroth-order algorithms can be effectively deployed. We overcome this limitation through novel algorithms based on randomized subspace techniques. We also report on our experience developing adaptive algorithms, which leverage information learned online to adapt critical algorithmic features. We illustrate our approaches in trust-region-based reduced-space methods and show how trained policies can even be deployed effectively in nonstationary cases, where the noise seen changes over the decision space.\u003Cbr\u003E\u003Cbr\u003E\u003Cbr\u003EBio:\u0026nbsp;\u003C\/div\u003E\u003Cdiv\u003EStefan M. Wild is a Senior Scientist and Director of the Applied Mathematics and Computational Research Division at Lawrence Berkeley National Laboratory, a research lab primarily funded by the U.S. Department of Energy\u2019s Office of Science. Wild is also adjunct faculty in Industrial Engineering and Management Sciences at Northwestern University. Wild received his Ph.D. in Operations Research and Information Engineering from Cornell University. Wild is a SIAM Fellow and his research has been recognized by the INFORMS Optimization Society\u0027s \u0026nbsp;Egon Balas Prize and the U.S. Presidential Early Career Award for Scientists and Engineers. Wild is Section Editor for SIAM Review and Associate Editor for Data Science in Science, INFORMS Journal on Computing, Journal of Optimization Theory and Applications, and Mathematical Programming Computation. Wild\u2019s primary research focuses on developing model-based algorithms and software for challenging numerical optimization problems and automated learning under uncertainty, with the goal of accelerating and advancing scientific discoveries.\u003C\/div\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EZeroth-order optimization solvers are often deployed in settings where little information regarding a problem\u0027s conditioning or noise level is known. Effective solvers must handle complex applications -- from automated materials discovery to quantum circuit compilation -- each presenting unique challenges. These problem features often limit the scale of the problems on which zeroth-order algorithms can be effectively deployed. We overcome this limitation through novel algorithms based on randomized subspace techniques. We also report on our experience developing adaptive algorithms, which leverage information learned online to adapt critical algorithmic features. We illustrate our approaches in trust-region-based reduced-space methods and show how trained policies can even be deployed effectively in nonstationary cases, where the noise seen changes over the decision space.\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Improving the Practical Scalability and Robustness of Zeroth-Order Optimization Solvers"}],"uid":"36527","created_gmt":"2025-08-26 18:05:10","changed_gmt":"2025-08-26 18:11:39","author":"hulrich6","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2025-09-12T11:00:00-04:00","event_time_end":"2025-09-12T12:00:00-04:00","event_time_end_last":"2025-09-12T12:00:00-04:00","gmt_time_start":"2025-09-12 15:00:00","gmt_time_end":"2025-09-12 16:00:00","gmt_time_end_last":"2025-09-12 16:00:00","rrule":null,"timezone":"America\/New_York"},"location":"Groseclose 402","extras":[],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"177814","name":"Postdoc"},{"id":"78771","name":"Public"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"683995":{"#nid":"683995","#data":{"type":"event","title":"ISyE Statistic Seminar - Dennis K.J. Lin","body":[{"value":"\u003Ch3\u003E\u003Cstrong\u003ETitle:\u003C\/strong\u003E\u003C\/h3\u003E\u003Cp\u003EAI, BI \u0026amp; SI\u2014Artificial, Biological and Statistical Intelligences\u003C\/p\u003E\u003Ch3\u003EAbstract:\u003C\/h3\u003E\u003Cp\u003EArtificial Intelligence (AI) is clearly one of the hottest subjects these days. Basically, AI employs a huge number of inputs (training data), super-efficient computer power\/memory, and smart algorithms to perform its intelligence. In contrast, Biological Intelligence (BI) is a natural intelligence that requires very little or even no input. This talk will first discuss the fundamental issue of input (training data) for AI. After all, not-so-informative inputs (even if they are huge) will result in a not-so-intelligent AI. Specifically, three issues will be discussed: (1) input bias, (2) data right vs. right data, and (3) sample vs. population. Finally, the importance of Statistical Intelligence (SI) will be introduced. SI is somehow in between AI and BI. It employs important sample data, solid theoretically proven statistical inference\/models, and natural intelligence. In my view, AI will become more and more powerful in many senses, but it will never replace BI. After all, it is said that \u201cThe truth is stranger than fiction, because fiction must make sense.\u201d The ultimate goal of this study is to find out \u201chow can humans use AI, BI, and SI together to do things better.\u201d\u003C\/p\u003E\u003Ch3\u003E\u003Cstrong\u003EBio:\u003C\/strong\u003E\u003C\/h3\u003E\u003Cp\u003EDr. Dennis K. J. Lin is a Distinguished Professor of Statistics at Purdue University. He served as the Department Head during 2020-2022. Prior to this current job, he was a University Distinguished Professor of Supply Chain Management and Statistics at Penn State, where he worked for 25 years. His research interests are data quality, industrial statistics, statistical inference, and data science. He has published nearly 300 SCI\/SSCI papers in a wide variety of journals. He currently serves or has served as an associate editor for more than 10 professional journals and was a co-editor for Applied Stochastic Models for Business and Industry. Dr. Lin is an elected fellow of ASA, IMS, ASQ, \u0026amp; RSS, an elected member of ISI, and a lifetime member of ICSA. He is an honorary chair professor for various universities, including Fudan University, and National Taiwan Normal University and a Chang-Jiang Scholar at Renmin University of China,. His recent awards include, the Youden Address (ASQ, 2010), the Shewell Award (ASQ, 2010), the Don Owen Award (ASA, 2011), the Loutit Address (SSC, 2011), the Hunter Award (ASQ, 2014), the Shewhart Medal (ASQ, 2015), and the SPES Award (ASA, 2016). He was the Deming Lecturer Award at 2020 JSM. His most recent award is \u201cThe 2022 Distinguished Alumni Award\u201d (National Tsing Hua University, Taiwan).\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Ch3\u003E\u003Cstrong\u003EAbstract:\u003C\/strong\u003E\u003C\/h3\u003E\u003Cp\u003EArtificial Intelligence (AI) is clearly one of the hottest subjects these days. Basically, AI employs a huge number of inputs (training data), super-efficient computer power\/memory, and smart algorithms to perform its intelligence. In contrast, Biological Intelligence (BI) is a natural intelligence that requires very little or even no input. This talk will first discuss the fundamental issue of input (training data) for AI. After all, not-so-informative inputs (even if they are huge) will result in a not-so-intelligent AI. Specifically, three issues will be discussed: (1) input bias, (2) data right vs. right data, and (3) sample vs. population. Finally, the importance of Statistical Intelligence (SI) will be introduced. SI is somehow in between AI and BI. It employs important sample data, solid theoretically proven statistical inference\/models, and natural intelligence. In my view, AI will become more and more powerful in many senses, but it will never replace BI. After all, it is said that \u201cThe truth is stranger than fiction, because fiction must make sense.\u201d The ultimate goal of this study is to find out \u201chow can humans use AI, BI, and SI together to do things better.\u201d\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"AI, BI \u0026 SI\u2014Artificial, Biological and Statistical Intelligences"}],"uid":"34977","created_gmt":"2025-08-20 16:52:30","changed_gmt":"2025-08-20 16:57:31","author":"Julie Smith","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2025-09-23T11:00:00-04:00","event_time_end":"2025-09-23T12:00:00-04:00","event_time_end_last":"2025-09-23T12:00:00-04:00","gmt_time_start":"2025-09-23 15:00:00","gmt_time_end":"2025-09-23 16:00:00","gmt_time_end_last":"2025-09-23 16:00:00","rrule":null,"timezone":"America\/New_York"},"location":"ISyE Groseclose 402","extras":[],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"177814","name":"Postdoc"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"680231":{"#nid":"680231","#data":{"type":"event","title":"SCL Course: Machine Learning Applications for Supply Chain Planning (Virtual\/Instructor-Lead)","body":[{"value":"\u003Ch3\u003E\u003Cstrong\u003ECourse Description\u003C\/strong\u003E\u003C\/h3\u003E\u003Cp\u003EThis course is the third in the four-course Supply Chain Analytics Professional certificate program. It introduces the field of machine learning, an area where algorithms learn patterns from data to support proactive decision making, as it applies to supply chain management. You\u2019ll use machine learning to conduct predictive analytics as you forecast future demand, develop inventory policies, perform customer segmentation and predictive maintenance. You\u2019ll use Python and PowerBI to create and analyze regression, clustering, and classification models.\u003C\/p\u003E\u003Cp\u003EThe course is comprised of (4) half-day online instructor-led LIVE group webinars and pre-work (e.g. installing and testing software on your computer, testing connectivity with LMS and meeting software, etc.) to be completed\u0026nbsp;before the first day of the course. An optional pre-course webinar is typically held the Thursday\u0026nbsp;before the course start date (July 6).\u003C\/p\u003E\u003Ch3\u003E\u003Cstrong\u003EWho Should Attend\u003C\/strong\u003E\u003C\/h3\u003E\u003Cp\u003EExperienced business professionals who perform or want to perform analytics to improve their supply chain management processes. They want to tackle strategic goals and to perform leading edge analytics projects that address the full complexity of supply chains.\u003C\/p\u003E\u003Ch3\u003E\u003Cstrong\u003EHow You Will Benefit\u003C\/strong\u003E\u003C\/h3\u003E\u003Cul\u003E\u003Cli\u003EUnderstand the role of machine learning (ML) in Supply Chain Management (SCM)\u003C\/li\u003E\u003Cli\u003EApply advanced analytics techniques to build planning tools that can leverage large and real-time data sets\u003C\/li\u003E\u003Cli\u003EApply ML in demand forecasting and predictive maintenance\u003C\/li\u003E\u003Cli\u003EUnderstand how to assess ML model performance, improve models, and pick the best model for a decision\u003C\/li\u003E\u003Cli\u003EUse Python and PowerBI to build, analyze, and deploy ML models\u003C\/li\u003E\u003C\/ul\u003E\u003Ch3\u003E\u003Cstrong\u003EWhat You Will Learn\u003C\/strong\u003E\u003C\/h3\u003E\u003Cul\u003E\u003Cli\u003EHow ML relates to SCM\u003C\/li\u003E\u003Cli\u003EML algorithms such as regression trees, clustering techniques, decision trees, random forests, logistic regression\u003C\/li\u003E\u003Cli\u003EAspects of ML projects including parameter tuning, cross validation, and assess model performance\u003C\/li\u003E\u003Cli\u003EApplication of ML in demand forecasting for sales and operations planning (S\u0026amp;OP) and inventory management\u003C\/li\u003E\u003Cli\u003EApplication of ML in predictive maintenance\u003C\/li\u003E\u003Cli\u003EHands-on practice with these skills using data from the (fictional) Cardboard Company (CBC)\u003C\/li\u003E\u003C\/ul\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EThe course will cover regression (trees), advanced time series forecasting, various clustering techniques (such as k-means), decision trees, random forests, neural nets, logistic regression, and Bayes classifiers. Using Power BI and Python, you\u2019ll apply the techniques to sensor data of the fictional Cardboard Company\u2019s paper production to build an anomaly detection model that supports proactive production maintenance planning.\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Apply machine learning with Python and Power BI to optimize supply chain forecasting, inventory, and maintenance."}],"uid":"36698","created_gmt":"2025-02-05 19:48:05","changed_gmt":"2025-08-20 14:38:48","author":"dramirez65","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2025-09-15T13:00:00-04:00","event_time_end":"2025-09-18T17:00:00-04:00","event_time_end_last":"2025-09-18T17:00:00-04:00","gmt_time_start":"2025-09-15 17:00:00","gmt_time_end":"2025-09-18 21:00:00","gmt_time_end_last":"2025-09-18 21:00:00","rrule":null,"timezone":"America\/New_York"},"location":"Virtual\/Instructor-led","extras":[],"related_links":[{"url":"https:\/\/www.scl.gatech.edu\/education\/professional-education\/course\/scaml","title":"Course webpage within the SCL website"}],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"},{"id":"1243","name":"The Supply Chain and Logistics Institute (SCL)"}],"categories":[],"keywords":[{"id":"170001","name":"Supply Chain Engineering"},{"id":"194222","name":"Supply chain "},{"id":"9167","name":"machine learning"},{"id":"122741","name":"physical internet"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"10377","name":"Career\/Professional development"}],"invited_audience":[{"id":"78771","name":"Public"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[{"value":"\u003Cp\u003Einfo@scl.gatech.edu\u003C\/p\u003E","format":"limited_html"}],"email":[],"slides":[],"orientation":[],"userdata":""}},"683936":{"#nid":"683936","#data":{"type":"event","title":"Fall 2025 IISE Career Fair ","body":[{"value":"\u003Ch3 dir=\u0022ltr\u0022\u003E\u003Cstrong\u003EAbout\u003C\/strong\u003E\u003C\/h3\u003E\u003Cp\u003EEvery fall and spring semester, during the IISE Career Fair, companies across the nation come to Georgia Tech to recruit some of the nation\u2019s top talent from our Bachelor\u2019s and Master\u2019s programs. Our students are recruited for a variety of roles and perform well past expectations in all positions. We hope that you will join us this semester and meet some of the country\u2019s brightest students.\u003C\/p\u003E\u003Cp\u003EThis fall semester, we will be hosting an in-person career fair on \u003Cstrong\u003ETuesday, September 2nd, 2025\u003C\/strong\u003E, at Georgia Tech\u2019s Exhibition Hall. We highly encourage all companies to register early for the Fall 2025 Career Fair. The first 5 companies to register and pay will have the exclusive opportunity to reserve a dedicated room for additional networking after the event. This private networking session can be tailored to your preferences, giving your company a unique chance to connect with top talent in a more intimate setting. Don\u0027t miss out on this valuable opportunity to enhance your recruitment experience!\u003C\/p\u003E\u003Ch3 dir=\u0022ltr\u0022\u003E\u003Cstrong\u003ECompany Recruiters\u003C\/strong\u003E\u003C\/h3\u003E\u003Cp dir=\u0022ltr\u0022\u003EJoin us at one of the largest Industrial \u0026amp; Systems Engineering career fairs in the nation. Registration closes\u0026nbsp;\u003Cstrong\u003EAugust 27th\u003C\/strong\u003E, so secure your table today!:\u003C\/p\u003E\u003Ch3 dir=\u0022ltr\u0022\u003E\u003Cstrong\u003EStudents\u003C\/strong\u003E\u003C\/h3\u003E\u003Cp dir=\u0022ltr\u0022\u003ELooking for internships or full-time roles? This is your chance to meet leading employers and explore exciting career opportunities in Industrial \u0026amp; Systems Engineering. For more information and to submit your resume, visit\u0026nbsp;\u003Ca href=\u0022https:\/\/www.gtiise.org\/career-fair-info\u0022\u003Ehttps:\/\/www.gtiise.org\/career-fair-info\u003C\/a\u003E.\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp dir=\u0022ltr\u0022\u003EJoin us at one of the largest Industrial \u0026amp; Systems Engineering career fairs in the nation. Registration closes\u0026nbsp;Wednesday, \u003Cstrong\u003EAugust 27\u003C\/strong\u003E so secure your table today!:\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Recruit from the #1-Ranked Industrial Engineering Program in the Nation"}],"uid":"36736","created_gmt":"2025-08-19 13:39:07","changed_gmt":"2025-08-19 14:03:42","author":"ebrown386","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2025-09-02T09:00:00-04:00","event_time_end":"2025-09-02T15:30:00-04:00","event_time_end_last":"2025-09-02T15:30:00-04:00","gmt_time_start":"2025-09-02 13:00:00","gmt_time_end":"2025-09-02 19:30:00","gmt_time_end_last":"2025-09-02 19:30:00","rrule":null,"timezone":"America\/New_York"},"location":"GT Exhibition Hall","extras":[],"hg_media":{"677733":{"id":"677733","type":"image","title":"Screenshot-2025-08-19-at-10.01.53-AM.png","body":null,"created":"1755612122","gmt_created":"2025-08-19 14:02:02","changed":"1755612122","gmt_changed":"2025-08-19 14:02:02","alt":"Fall 2025 IISE Career Fair","file":{"fid":"261666","name":"Screenshot-2025-08-19-at-10.01.53-AM.png","image_path":"\/sites\/default\/files\/2025\/08\/19\/Screenshot-2025-08-19-at-10.01.53-AM.png","image_full_path":"http:\/\/hg.gatech.edu\/\/sites\/default\/files\/2025\/08\/19\/Screenshot-2025-08-19-at-10.01.53-AM.png","mime":"image\/png","size":316209,"path_740":"http:\/\/hg.gatech.edu\/sites\/default\/files\/styles\/740xx_scale\/public\/2025\/08\/19\/Screenshot-2025-08-19-at-10.01.53-AM.png?itok=726L7wAH"}}},"media_ids":["677733"],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"10377","name":"Career\/Professional development"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"78771","name":"Public"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[{"value":"\u003Cp\u003E\u003Cstrong\u003Eiise@gatech.edu\u003C\/strong\u003E\u003C\/p\u003E","format":"limited_html"}],"email":[],"slides":[],"orientation":[],"userdata":""}},"683935":{"#nid":"683935","#data":{"type":"event","title":"ISyE Seminar - Elsayed A. Elsayed","body":[{"value":"\u003Cp\u003ETitle: Stochastic Modeling of Unified Resilience Metrics\u003C\/p\u003E\u003Cp\u003EAbstract:\u003C\/p\u003E\u003Cp\u003EThere has been significant developments of large and complex engineered infrastructure systems such as telecommunication networks, power grids, transportation infrastructure, healthcare delivery systems, information technology, financial systems and supply chain systems. Failures of such systems may result in cascading damages as well as significant interruptions of their services. This presentation focuses on the development of unified resilience metrics based on analytical and AI-enabled stochastic models to evaluate and predict the resilience and availability of complex systems subject to various types of failures, repairs and recovery processes.\u003C\/p\u003E\u003Cp\u003EBio:\u003C\/p\u003E\u003Cp\u003EE. A. Elsayed is Distinguished Professor of the Department of Industrial and Systems Engineering, Rutgers University. He is also the Director of the NSF\/ Industry\/ University Co-operative Research Center for Quality and Reliability Engineering. He was the Chair of ISE, Rutgers University from 1983 to 2001. His research interests are in the areas of quality and reliability engineering and Production Planning and Control. He is a co-author of Quality Engineering in Production Systems, McGraw Hill Book Company, 1989. He is also the author of Reliability Engineering, Addison-Wesley, 1996. These two books received the 1990 and 1997 IIE Joint Publishers Book-of-the-Year Award respectively. His recent book Reliability Engineering 2nd Edition, Wiley, 2021 received the 2012 Outstanding Publications of IIE. The third edition of this book by Wiley in 2021 was selected to be included in the Best Industrial Management eBooks of All Time. Dr. Elsayed has received many awards and honors and was the keynote speaker of many international conferences. Dr. Elsayed was awarded the Doctor Honoris Causes from University of Agers, France in January 2018 for his achievements in the reliability engineering field. In November 2023, he received \u201cFaculty of the Year\u201d award, Rutgers University.\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EThere has been significant developments of large and complex engineered infrastructure systems such as telecommunication networks, power grids, transportation infrastructure, healthcare delivery systems, information technology, financial systems and supply chain systems. Failures of such systems may result in cascading damages as well as significant interruptions of their services. This presentation focuses on the development of unified resilience metrics based on analytical and AI-enabled stochastic models to evaluate and predict the resilience and availability of complex systems subject to various types of failures, repairs and recovery processes.\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Stochastic Modeling of Unified Resilience Metrics"}],"uid":"36527","created_gmt":"2025-08-19 12:43:22","changed_gmt":"2025-08-19 12:56:16","author":"hulrich6","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2025-09-26T11:00:00-04:00","event_time_end":"2025-09-26T12:00:00-04:00","event_time_end_last":"2025-09-26T12:00:00-04:00","gmt_time_start":"2025-09-26 15:00:00","gmt_time_end":"2025-09-26 16:00:00","gmt_time_end_last":"2025-09-26 16:00:00","rrule":null,"timezone":"America\/New_York"},"location":"Groseclose 402","extras":[],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"177814","name":"Postdoc"},{"id":"78771","name":"Public"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"683845":{"#nid":"683845","#data":{"type":"event","title":"SCL Lunch and Learn: \u0022The Future is Integrated: IBP Insights from Georgia Tech\u0022","body":[{"value":"\u003Cp\u003E\u003Cem\u003E\u003Cstrong\u003EExplore how integrated project and supply chain strategies can transform disruption into opportunity and drive lasting results in today\u2019s dynamic business environment\u003C\/strong\u003E\u003C\/em\u003E\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EThursday, August 7, 2025 | 12-1pm ET\u003C\/strong\u003E\u003C\/p\u003E\u003Cdiv\u003E\u003Cdiv\u003E\u003Cdiv\u003E\u003Cdiv\u003E\u003Cdiv\u003E\u003Cp\u003EAn educational webinar where we explore strategies for unlocking business potential through integrated alignment of operations, supply chain, and finance. In this expert-led session, faculty from the Scheller College of Business and Stewart School of Industrial and Systems Engineering guide you through the fundamentals of Integrated Business Planning (IBP), demonstrate its power to transform enterprise performance, and share practical insights for leading organizational change.\u003C\/p\u003E\u003C\/div\u003E\u003C\/div\u003E\u003C\/div\u003E\u003C\/div\u003E\u003C\/div\u003E\u003Cp\u003E\u003Ca href=\u0022https:\/\/gatech.zoom.us\/webinar\/register\/5017513078690\/WN_WKiW9_DmQ36mhPqBpTiOxg\u0022\u003E\u003Cstrong\u003ERegister Online to Attend\u003C\/strong\u003E\u003C\/a\u003E\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EJoin the Georgia Tech Supply Chain and Logistics Institute to explore strategies for unlocking business potential through integrated alignment of operations, supply chain, and finance. In this expert-led session, faculty from the Scheller College of Business and Stewart School of Industrial and Systems Engineering guide you through the fundamentals of Integrated Business Planning (IBP), demonstrate its power to transform enterprise performance, and share practical insights for leading organizational change.\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Discover strategies for unlocking business potential through integrated alignment of operations, supply chain, and finance."}],"uid":"27233","created_gmt":"2025-08-15 15:20:03","changed_gmt":"2025-08-15 15:28:32","author":"Andy Haleblian","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2025-08-07T12:00:00-04:00","event_time_end":"2025-08-07T13:00:00-04:00","event_time_end_last":"2025-08-07T13:00:00-04:00","gmt_time_start":"2025-08-07 16:00:00","gmt_time_end":"2025-08-07 17:00:00","gmt_time_end_last":"2025-08-07 17:00:00","rrule":null,"timezone":"America\/New_York"},"location":"Online via Zoom","extras":[],"hg_media":{"677314":{"id":"677314","type":"image","title":"SCL Lunch and Learn: \u201cThe Future is Integrated: IBP Insights from Georgia Tech\u0022","body":null,"created":"1751323784","gmt_created":"2025-06-30 22:49:44","changed":"1751323784","gmt_changed":"2025-06-30 22:49:44","alt":"SCL Lunch and Learn: \u201cThe Future is Integrated: IBP Insights from Georgia Tech\u0022","file":{"fid":"261200","name":"hg_LNL_IBP_20250807.png","image_path":"\/sites\/default\/files\/2025\/06\/30\/hg_LNL_IBP_20250807.png","image_full_path":"http:\/\/hg.gatech.edu\/\/sites\/default\/files\/2025\/06\/30\/hg_LNL_IBP_20250807.png","mime":"image\/png","size":163165,"path_740":"http:\/\/hg.gatech.edu\/sites\/default\/files\/styles\/740xx_scale\/public\/2025\/06\/30\/hg_LNL_IBP_20250807.png?itok=fKz3Ofyv"}}},"media_ids":["677314"],"related_links":[{"url":"https:\/\/gatech.zoom.us\/webinar\/register\/5017513078690\/WN_WKiW9_DmQ36mhPqBpTiOxg","title":"Register Online to Attend the Webinar"},{"url":"https:\/\/www.scl.gatech.edu\/education\/professional-education","title":"Professional Education at the Georgia Tech Supply Chain and Logistics Institute"}],"groups":[{"id":"1250","name":"Center for Health and Humanitarian Systems (CHHS)"},{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"},{"id":"1243","name":"The Supply Chain and Logistics Institute (SCL)"}],"categories":[],"keywords":[{"id":"3615","name":"information session"},{"id":"186857","name":"go-gtmi"},{"id":"122741","name":"physical internet"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"10377","name":"Career\/Professional development"}],"invited_audience":[{"id":"78771","name":"Public"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[{"value":"\u003Cp\u003Ecourse@scl.gatech.edu\u003C\/p\u003E","format":"limited_html"}],"email":[],"slides":[],"orientation":[],"userdata":""}},"682960":{"#nid":"682960","#data":{"type":"event","title":"SCL Lunch and Learn: \u0022From Chaos to Clarity: Building Resilient Supply Chains Through Smarter Project Management\u0022","body":[{"value":"\u003Cp\u003E\u003Cem\u003E\u003Cstrong\u003EExplore how integrated project and supply chain strategies can transform disruption into opportunity and drive lasting results in today\u2019s dynamic business environment\u003C\/strong\u003E\u003C\/em\u003E\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EThursday, October 2, 2025 | 12-1pm ET\u003C\/strong\u003E\u003C\/p\u003E\u003Cdiv\u003E\u003Cdiv\u003E\u003Cdiv\u003E\u003Cdiv\u003E\u003Cdiv\u003E\u003Cp\u003EIn today\u2019s volatile business landscape, resilience isn\u2019t just a buzzword, it\u2019s a competitive necessity. Join the Georgia Tech Supply Chain and Logistics Institute to explore how project management strategies can turn unpredictable supply chain environments into opportunities for stability and growth. Whether you\u0027re leading cross-functional initiatives, managing vendor transitions, or navigating disruption, you\u2019ll learn why traditional approaches fall short and how integrated project and supply chain thinking can help you deliver lasting results. Perfect for project managers, supply chain professionals, and anyone tasked with delivering complex initiatives in dynamic settings.\u003C\/p\u003E\u003C\/div\u003E\u003C\/div\u003E\u003C\/div\u003E\u003C\/div\u003E\u003C\/div\u003E\u003Cp\u003E\u003Ca href=\u0022https:\/\/gatech.zoom.us\/webinar\/register\/6417550005346\/WN_lpcTMj-NSX6XRrnTPyMUlw\u0022\u003E\u003Cstrong\u003ERegister Online to Attend\u003C\/strong\u003E\u003C\/a\u003E\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EJoin the Georgia Tech Supply Chain and Logistics Institute to explore how project management strategies can turn unpredictable supply chain environments into opportunities for stability and growth.\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Learn how project management strategies can turn unpredictable supply chain environments into opportunities for stability and growth. "}],"uid":"27233","created_gmt":"2025-06-30 22:47:15","changed_gmt":"2025-08-15 15:22:42","author":"Andy Haleblian","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2025-10-02T12:00:00-04:00","event_time_end":"2025-10-02T13:00:00-04:00","event_time_end_last":"2025-10-02T13:00:00-04:00","gmt_time_start":"2025-10-02 16:00:00","gmt_time_end":"2025-10-02 17:00:00","gmt_time_end_last":"2025-10-02 17:00:00","rrule":null,"timezone":"America\/New_York"},"location":"Online via Zoom","extras":[],"hg_media":{"677652":{"id":"677652","type":"image","title":"hg_LNL_SCPMgmt_20251002.png","body":null,"created":"1755001258","gmt_created":"2025-08-12 12:20:58","changed":"1755005189","gmt_changed":"2025-08-12 13:26:29","alt":"From Chaos to Clarity | Building Resilient Supply Chains Through Smarter Project Management","file":{"fid":"261574","name":"hg_LNL_SCPMgmt_20251002.png","image_path":"\/sites\/default\/files\/2025\/08\/12\/hg_LNL_SCPMgmt_20251002.png","image_full_path":"http:\/\/hg.gatech.edu\/\/sites\/default\/files\/2025\/08\/12\/hg_LNL_SCPMgmt_20251002.png","mime":"image\/png","size":117580,"path_740":"http:\/\/hg.gatech.edu\/sites\/default\/files\/styles\/740xx_scale\/public\/2025\/08\/12\/hg_LNL_SCPMgmt_20251002.png?itok=EoRf4bVv"}}},"media_ids":["677652"],"related_links":[{"url":"https:\/\/gatech.zoom.us\/webinar\/register\/6417550005346\/WN_lpcTMj-NSX6XRrnTPyMUlw","title":"Register Online to Attend the Webinar"},{"url":"https:\/\/www.scl.gatech.edu\/education\/professional-education","title":"Professional Education at the Georgia Tech Supply Chain and Logistics Institute"}],"groups":[{"id":"1250","name":"Center for Health and Humanitarian Systems (CHHS)"},{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"},{"id":"1243","name":"The Supply Chain and Logistics Institute (SCL)"}],"categories":[],"keywords":[{"id":"3615","name":"information session"},{"id":"186857","name":"go-gtmi"},{"id":"122741","name":"physical internet"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"10377","name":"Career\/Professional development"}],"invited_audience":[{"id":"78771","name":"Public"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[{"value":"\u003Cp\u003Ecourse@scl.gatech.edu\u003C\/p\u003E","format":"limited_html"}],"email":[],"slides":[],"orientation":[],"userdata":""}},"683763":{"#nid":"683763","#data":{"type":"event","title":"ISyE Seminar - Krishnakumar Balasubramanian","body":[{"value":"\u003Cp\u003ETitle: Riemannian Proximal Sampler for High-accuracy Sampling on Manifolds\u003C\/p\u003E\u003Cp\u003EAbstract:\u200b\u003C\/p\u003E\u003Cp\u003ESampling from densities \u200bdefined on Riemannian manifolds is central to Bayesian inference, generative modeling, and differential privacy. We introduce the Riemannian Proximal Sampler (RPS), whose efficiency hinges on two oracles: Manifold Brownian Increments and the Riemannian Heat Kernel. We establish high-accuracy sampling guarantees for the Riemannian Proximal Sampler, showing that generating samples with \u03b5-accuracy requires O(log(1\/\u03b5)) iterations in Kullback-Leibler divergence assuming access to exact oracles and O(log\u200b^2(1\/\u03b5)) iterations in the total variation metric assuming access to sufficiently accurate inexact oracles. Furthermore, we present two practical implementations of these oracles by leveraging heat-kernel truncation and Varadhan\u0027s asymptotics, respectively. In the latter case, we interpret the Riemannian Proximal Sampler as a discretization of the entropy-regularized Riemannian Proximal Point Method on the associated Wasserstein space. We will discuss numerical results that illustrate the effectiveness of the proposed methodology.\u003C\/p\u003E\u003Cp\u003E\u200bBio:\u003C\/p\u003E\u003Cp\u003EKrishnakumar Balasubramanian is an Associate Professor in the Department of Statistics at the University of California, Davis, affiliated with the Graduate Group in Applied Mathematics, the Center for Data Science and Artificial Intelligence Research (CeDAR), and the TETRAPODS Institute of Data Science. He is also an Amazon Scholar and was a visiting scientist at the Simons Institute for the Theory of Computing at UC Berkeley in Fall 2021 and Fall 2022. Krishna received his Ph.D. in Computer Science from the Georgia Institute of Technology and completed postdoctoral research at Princeton University and the University of Wisconsin\u2013Madison. His research lies at the interface of machine learning and artificial intelligence, statistics and optimization. He is a recipient of several honors, including a Facebook Fellowship (2013), the ICML Best Paper Runner-Up Award (2013), and the INFORMS ICS Prize (2024). He contributes actively to the academic community as an Associate Editor for the Annals of Statistics, IEEE Transactions on Information Theory and the Journal of Machine Learning Research, and serves regularly as a (senior) area chair for leading conferences such as ICML, ICLR, NeurIPS, and COLT.\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003ESampling from densities \u200bdefined on Riemannian manifolds is central to Bayesian inference, generative modeling, and differential privacy. We introduce the Riemannian Proximal Sampler (RPS), whose efficiency hinges on two oracles: Manifold Brownian Increments and the Riemannian Heat Kernel. We establish high-accuracy sampling guarantees for the Riemannian Proximal Sampler, showing that generating samples with \u03b5-accuracy requires O(log(1\/\u03b5)) iterations in Kullback-Leibler divergence assuming access to exact oracles and O(log\u200b^2(1\/\u03b5)) iterations in the total variation metric assuming access to sufficiently accurate inexact oracles. Furthermore, we present two practical implementations of these oracles by leveraging heat-kernel truncation and Varadhan\u0027s asymptotics, respectively. In the latter case, we interpret the Riemannian Proximal Sampler as a discretization of the entropy-regularized Riemannian Proximal Point Method on the associated Wasserstein space. We will discuss numerical results that illustrate the effectiveness of the proposed methodology.\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":" Riemannian Proximal Sampler for High-accuracy Sampling on Manifolds"}],"uid":"36527","created_gmt":"2025-08-12 20:20:21","changed_gmt":"2025-08-15 12:39:36","author":"hulrich6","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2025-09-05T11:00:00-04:00","event_time_end":"2025-09-05T12:00:00-04:00","event_time_end_last":"2025-09-05T12:00:00-04:00","gmt_time_start":"2025-09-05 15:00:00","gmt_time_end":"2025-09-05 16:00:00","gmt_time_end_last":"2025-09-05 16:00:00","rrule":null,"timezone":"America\/New_York"},"location":"Groseclose 402","extras":[],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"177814","name":"Postdoc"},{"id":"78771","name":"Public"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"683755":{"#nid":"683755","#data":{"type":"event","title":"ISyE Seminar - Yuehwern Yih","body":[{"value":"\u003Cp\u003ETitle: Bridging the Cyber\u2013Physical Gaps in Health and Humanitarian Assistance\u003C\/p\u003E\u003Cp\u003EAbstract:\u003C\/p\u003E\u003Cp\u003ETechnological advances in digital platforms, automation, and sensor systems are rapidly expanding capabilities in healthcare delivery and humanitarian assistance. However, in complex operating environments, the integration of these technologies often reveals a critical gap between cyber systems and the physical realities of human action, environmental variability, and contextual constraints. This misalignment can undermine decision quality and service reliability, and in some cases, lead to unintended or suboptimal outcomes.\u003C\/p\u003E\u003Cp\u003EThis talk examines these challenges in last-mile delivery for healthcare and humanitarian assistance, where decision-making is shaped by the dynamic interplay of human judgment, technological capabilities, and on-the-ground realities. Drawing on case studies, it will discuss how discrepancies between data collected for one operational purpose and its subsequent use for modeling or strategic decision-making can propagate through the system\u2014amplifying risks and degrading performance. Emphasis will be placed on the importance of context-aware design, stakeholder engagement, and research translation to deliver high-quality, sustainable solutions.\u003C\/p\u003E\u003Cp\u003EBio:\u003C\/p\u003E\u003Cp\u003EDr. Yuehwern Yih is the Tompkins Professor of Industrial Engineering at Purdue University. She previously served as the Director of LASER PULSE ($70 million USAID funded program) and the Associate Director of Regenstrief Center for Healthcare Engineering. She is a Member of the National Academy of Engineering (NAE) a Senior Member of the National Academy of Inventors (NAI), an Institute of Industrial and Systems Engineers (IISE) Fellow, and an Executive Leadership in Academic Technology, Engineering and Science (ELATE) Fellow.\u003C\/p\u003E\u003Cp\u003EDr. Yih\u2019s core research focuses on understanding dynamics of system behaviors to improve the performance of complex systems under volatile environments including manufacturing systems, supply chains, humanitarian assistance, health care delivery, and global development. Dr. Yih received the IISE David F Baker Distinguished Research Award, the Melinda and Bill Gates Grand Challenge Award, the inaugural Faculty Engagement Fellow (highest honor for engagement at Purdue), and multiple Pritsker Undergraduate Teaching Awards and the Most Impactful Faculty Inventors at Purdue. She has vast experience in interdisciplinary and cross-sector collaboration to address global development challenges, e.g. integrated nutrition system for HIV patients in Kenya, medical supply chains for maternal health in Uganda, humanitarian supply chains in South Sudan and Ukraine, impacting over a million people in need. Dr. Yih received her B.S. in Industrial Engineering from the National Tsing Hua University in Taiwan, and her Ph.D. from the University of Wisconsin-Madison.\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003ETechnological advances in digital platforms, automation, and sensor systems are rapidly expanding capabilities in healthcare delivery and humanitarian assistance. However, in complex operating environments, the integration of these technologies often reveals a critical gap between cyber systems and the physical realities of human action, environmental variability, and contextual constraints. This misalignment can undermine decision quality and service reliability, and in some cases, lead to unintended or suboptimal outcomes.\u003C\/p\u003E\u003Cp\u003EThis talk examines these challenges in last-mile delivery for healthcare and humanitarian assistance, where decision-making is shaped by the dynamic interplay of human judgment, technological capabilities, and on-the-ground realities. Drawing on case studies, it will discuss how discrepancies between data collected for one operational purpose and its subsequent use for modeling or strategic decision-making can propagate through the system\u2014amplifying risks and degrading performance. Emphasis will be placed on the importance of context-aware design, stakeholder engagement, and research translation to deliver high-quality, sustainable solutions.\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Bridging the Cyber\u2013Physical Gaps in Health and Humanitarian Assistance"}],"uid":"36527","created_gmt":"2025-08-12 19:01:42","changed_gmt":"2025-08-13 11:31:51","author":"hulrich6","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2025-08-29T11:00:00-04:00","event_time_end":"2025-08-29T12:00:00-04:00","event_time_end_last":"2025-08-29T12:00:00-04:00","gmt_time_start":"2025-08-29 15:00:00","gmt_time_end":"2025-08-29 16:00:00","gmt_time_end_last":"2025-08-29 16:00:00","rrule":null,"timezone":"America\/New_York"},"location":"Groseclose 402","extras":[],"groups":[{"id":"1250","name":"Center for Health and Humanitarian Systems (CHHS)"},{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"177814","name":"Postdoc"},{"id":"78771","name":"Public"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"683572":{"#nid":"683572","#data":{"type":"event","title":"SCL Lunch and Learn: \u201cIdea to Implementation: How Supply Chain Startups Are Solving Real-World Problems\u0022","body":[{"value":"\u003Cp\u003E\u003Cem\u003E\u003Cstrong\u003ELearn key lessons \u0026amp; strategies from recent health crises to strengthen supply chain resilience\u003C\/strong\u003E\u003C\/em\u003E\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EThursday, September 4, 2025 | 12-1pm ET\u003C\/strong\u003E\u003C\/p\u003E\u003Cdiv\u003E\u003Cdiv\u003E\u003Cdiv\u003E\u003Cdiv\u003E\u003Cdiv\u003E\u003Cdiv\u003EJoin Alex Rhodeen with the Georgia Tech Advanced Technology Development Center (ATDC) as he explores how innovative startups are tackling today\u0027s most pressing supply chain challenges. Drawing from his extensive work with emerging companies, Alex will share real-world examples of how entrepreneurs are moving from problem identification to practical solutions. Learn about the latest approaches being developed in the Georgia Tech startup ecosystem and gain insights into how these innovations are reshaping the future of supply chain management. Perfect for professionals interested in innovation, technology adoption, and the evolving landscape of supply chain solutions.\u003C\/div\u003E\u003C\/div\u003E\u003C\/div\u003E\u003C\/div\u003E\u003C\/div\u003E\u003Cdiv\u003E\u0026nbsp;\u003C\/div\u003E\u003C\/div\u003E\u003Cp\u003E\u003Ca href=\u0022https:\/\/gatech.zoom.us\/webinar\/register\/8617526067420\/WN_0Z12eIZ-QJOCXkN99sawPA\u0022\u003E\u003Cstrong\u003ERegister Online to Attend\u003C\/strong\u003E\u003C\/a\u003E\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EJoin Alex Rhodeen with the Georgia Tech Advanced Technology Development Center (ATDC) as he explores how innovative startups are tackling today\u0027s most pressing supply chain challenges.\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Discover key lessons and strategies from recent health crises to boost supply chain resilience and future emergency preparedness."}],"uid":"27233","created_gmt":"2025-08-06 11:35:04","changed_gmt":"2025-08-06 14:50:30","author":"Andy Haleblian","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2025-09-04T12:00:00-04:00","event_time_end":"2025-09-04T13:00:00-04:00","event_time_end_last":"2025-09-04T13:00:00-04:00","gmt_time_start":"2025-09-04 16:00:00","gmt_time_end":"2025-09-04 17:00:00","gmt_time_end_last":"2025-09-04 17:00:00","rrule":null,"timezone":"America\/New_York"},"location":"Online via Zoom","extras":[],"hg_media":{"677590":{"id":"677590","type":"image","title":"Idea to Implementation: How Supply Chain Startups Are Solving Real-World Problems","body":null,"created":"1754480543","gmt_created":"2025-08-06 11:42:23","changed":"1754480543","gmt_changed":"2025-08-06 11:42:23","alt":"Webinar - Idea to Implementation: How Supply Chain Startups Are Solving Real-World Problems","file":{"fid":"261503","name":"hg_LNL_ATDC_20250904.png","image_path":"\/sites\/default\/files\/2025\/08\/06\/hg_LNL_ATDC_20250904.png","image_full_path":"http:\/\/hg.gatech.edu\/\/sites\/default\/files\/2025\/08\/06\/hg_LNL_ATDC_20250904.png","mime":"image\/png","size":161723,"path_740":"http:\/\/hg.gatech.edu\/sites\/default\/files\/styles\/740xx_scale\/public\/2025\/08\/06\/hg_LNL_ATDC_20250904.png?itok=0xwNqCbW"}}},"media_ids":["677590"],"related_links":[{"url":"https:\/\/gatech.zoom.us\/webinar\/register\/8617526067420\/WN_0Z12eIZ-QJOCXkN99sawPA","title":"Register Online to Attend the Webinar"},{"url":"https:\/\/atdc.org","title":"Advanced Technology Development Center (ATDC)"},{"url":"https:\/\/www.scl.gatech.edu\/education\/professional-education","title":"Professional Education at the Georgia Tech Supply Chain and Logistics Institute"}],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"},{"id":"1243","name":"The Supply Chain and Logistics Institute (SCL)"}],"categories":[],"keywords":[{"id":"3615","name":"information session"},{"id":"186857","name":"go-gtmi"},{"id":"122741","name":"physical internet"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"10377","name":"Career\/Professional development"}],"invited_audience":[{"id":"78771","name":"Public"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[{"value":"\u003Cp\u003Ecourse@scl.gatech.edu\u003C\/p\u003E","format":"limited_html"}],"email":[],"slides":[],"orientation":[],"userdata":""}},"683337":{"#nid":"683337","#data":{"type":"event","title":"Unlocking GenAI in the Supply Chain: From Curiosity to Capability - Learners and Leaders Breakfast Series (Hybrid Event)","body":[{"value":"\u003Cp\u003EJoin us for a dynamic session exploring how Generative AI is reshaping the supply chain landscape. Whether you\u2019re leading teams or just beginning your AI journey, this event will unpack practical, high-ROI use cases\u2014from GenAI tools and techniques that will \u003Cstrong\u003Esave you and your team 10 hours a week\u003C\/strong\u003E, to intelligent agents poised to transform planning and execution. We\u2019ll cover what leaders need to know now, including prompting as a strategic skill, teaching critical thinking, AI policy implications for both students and companies, and real examples of how GenAI can move the needle on decision speed and quality.\u003C\/p\u003E\u003Cp\u003EExpect a fast-paced, \u003Cstrong\u003Einteractive format featuring real demos, \u201cunder-the-hood\u201d views of emerging trends \u003C\/strong\u003Elike agents, and our new \u003Cstrong\u003EAdvanced Analytics Learning Ladder\u003C\/strong\u003E\u2014an actionable guide to upskilling individuals and teams from fundamentals to frontier. Whether you\u2019re attending in person or online, you\u2019ll leave with tools and insights you can apply immediately to reclaim time, boost productivity, and future-proof your supply chain talent strategy.\u003C\/p\u003E\u003Cp\u003E\u003Cem\u003EFeatured Speaker\u003C\/em\u003E: \u003Cstrong\u003EChris Gaffney\u003C\/strong\u003E, Managing Director of the Supply Chain and Logistics Institute and Edenfield Executive-in-Residence, and a Professor of the Practice.\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EThe event will be hosted at Georgia Tech Savannah, but note that this is a hybrid event (attendees can\u0026nbsp;join in-person or virtually, but you must register to attend).\u003C\/strong\u003E\u003C\/p\u003E\u003Ch3\u003E\u003Cstrong\u003EThursday, September 4, 2025\u003C\/strong\u003E\u003C\/h3\u003E\u003Cul\u003E\u003Cli\u003E7:30 am: \u003Cstrong\u003EBreakfast \u0026amp; Networking\u003C\/strong\u003E (on-site at the Georgia Tech Savannah campus)\u003C\/li\u003E\u003Cli\u003E8:00-9:30 am: \u003Cstrong\u003EProgram \u003C\/strong\u003E(This is a hybrid event. You can join in-person or virtually.)\u003C\/li\u003E\u003C\/ul\u003E\u003Cp\u003ECost: \u003Cstrong\u003EFree\u003C\/strong\u003E\u003C\/p\u003E\u003Ch3\u003E\u003Ca href=\u0022https:\/\/www.eventbrite.com\/e\/unlocking-genai-in-the-supply-chain-from-curiosity-to-capability-tickets-1450232557619\u0022\u003ERegister via Eventbrite to Attend\u003C\/a\u003E\u003C\/h3\u003E\u003Cp\u003E\u003Ca href=\u0022https:\/\/hg.gatech.edu\/sites\/default\/files\/documents\/2025-07\/GTSav-GenAI_in_SupplyChain_20250904.pdf\u0022\u003EDownload the Event Flyer\u003C\/a\u003E\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EJoin us for a dynamic session exploring how Generative AI is reshaping the supply chain landscape. Whether you\u0027re leading teams or just beginning your AI journey, this event will unpack practical, high-ROI use cases\u2014from GenAI tools and techniques that will save you and your team 10 hours a week, to intelligent agents poised to transform planning and execution.\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"A hybrid event featuring SCL Managing Director Chris Gaffney and hosted by Georgia Tech Savannah."}],"uid":"27233","created_gmt":"2025-07-29 12:24:52","changed_gmt":"2025-07-29 14:36:34","author":"Andy Haleblian","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2025-09-04T07:30:00-04:00","event_time_end":"2025-09-04T09:30:00-04:00","event_time_end_last":"2025-09-04T09:30:00-04:00","gmt_time_start":"2025-09-04 11:30:00","gmt_time_end":"2025-09-04 13:30:00","gmt_time_end_last":"2025-09-04 13:30:00","rrule":null,"timezone":"America\/New_York"},"location":"Hybrid (Georgia Tech Savannah Campus or Online)","extras":[],"hg_media":{"677512":{"id":"677512","type":"image","title":"Unlocking GenAI in the Supply Chain: From Curiosity to Capability","body":null,"created":"1753792606","gmt_created":"2025-07-29 12:36:46","changed":"1753792606","gmt_changed":"2025-07-29 12:36:46","alt":"Unlocking GenAI in the Supply Chain: From Curiosity to Capability","file":{"fid":"261418","name":"GTSav-Unlocking_GenAI_BreakfastSeries.jpg","image_path":"\/sites\/default\/files\/2025\/07\/29\/GTSav-Unlocking_GenAI_BreakfastSeries.jpg","image_full_path":"http:\/\/hg.gatech.edu\/\/sites\/default\/files\/2025\/07\/29\/GTSav-Unlocking_GenAI_BreakfastSeries.jpg","mime":"image\/jpeg","size":566557,"path_740":"http:\/\/hg.gatech.edu\/sites\/default\/files\/styles\/740xx_scale\/public\/2025\/07\/29\/GTSav-Unlocking_GenAI_BreakfastSeries.jpg?itok=WuJGUu0n"}}},"media_ids":["677512"],"related_files":{"261427":{"fid":"261427","name":"Unlocking GenAI in the Supply Chain: From Curiosity to Capability (September 4, 2025 )","file_path":"\/sites\/default\/files\/documents\/2025-07\/GTSav-GenAI_in_SupplyChain_20250904.pdf","file_full_path":"http:\/\/hg.gatech.edu\/\/sites\/default\/files\/documents\/2025-07\/GTSav-GenAI_in_SupplyChain_20250904.pdf","mime":"application\/pdf","description":"Event Flyer"}},"related_links":[{"url":"https:\/\/www.eventbrite.com\/e\/unlocking-genai-in-the-supply-chain-from-curiosity-to-capability-tickets-1450232557619","title":"Register via Eventbrite to Attend"}],"groups":[{"id":"1250","name":"Center for Health and Humanitarian Systems (CHHS)"},{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"},{"id":"1243","name":"The Supply Chain and Logistics Institute (SCL)"}],"categories":[],"keywords":[{"id":"1317","name":"Georgia Tech Savannah"},{"id":"167074","name":"Supply Chain"},{"id":"233","name":"Logistics"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"78771","name":"Public"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"683211":{"#nid":"683211","#data":{"type":"event","title":"SCL Fall 2025 Supply Chain Day Career Fair","body":[{"value":"\u003Cp\u003EGeorgia Tech Supply Chain students and employers, please join us for our fall Supply Chain Day!\u0026nbsp;\u003C\/p\u003E\u003Ch3\u003E\u003Cstrong\u003EEvent Details\u003C\/strong\u003E\u003C\/h3\u003E\u003Ch4\u003EOn Campus\/In-Person (Georgia Tech Exhibition Hall)\u003C\/h4\u003E\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EMonday, September 15, 2025 | 10am-2pm ET\u003C\/strong\u003E\u003C\/li\u003E\u003C\/ul\u003E\u003Ch3\u003EStudents\u003C\/h3\u003E\u003Cp\u003E\u003Cstrong\u003EWe strongly encourage you to attend to seek full-time employment\u003C\/strong\u003E, \u003Cstrong\u003Einternships, and projects\u003C\/strong\u003E (rather than waiting until the end of the semester).\u003C\/p\u003E\u003Ch3\u003EOrganizations\u003C\/h3\u003E\u003Cp\u003EIf you are interested in hosting a table for the upcoming session, please let us know after reviewing the below information within our website. Early registration closes August 1st!\u003C\/p\u003E\u003Ch4\u003EMORE INFORMATION AND EVENT REGISTRATION\u003C\/h4\u003E\u003Cp\u003EVisit\u0026nbsp;\u003Ca href=\u0022https:\/\/www.scl.gatech.edu\/outreach\/supplychainday\u0022\u003E\u003Cstrong\u003Ehttps:\/\/www.scl.gatech.edu\/outreach\/supplychainday\u003C\/strong\u003E\u003C\/a\u003E.\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EGeorgia Tech Supply Chain\u0026nbsp;students and employers, please join us for our fall Supply Chain Day! 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We will also explore strategies to improve preparedness for future large-scale public health emergencies.\u003C\/div\u003E\u003C\/div\u003E\u003C\/div\u003E\u003C\/div\u003E\u003C\/div\u003E\u003Cdiv\u003E\u0026nbsp;\u003C\/div\u003E\u003C\/div\u003E\u003Cp\u003E\u003Ca href=\u0022https:\/\/gatech.zoom.us\/webinar\/register\/5017490578285\/WN_08LG6S-OQ9Cp6S1EQB1zoQ\u0022\u003E\u003Cstrong\u003ERegister Online to Attend\u003C\/strong\u003E\u003C\/a\u003E\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EJoin us while we examine key lessons from recent health crises to help you strengthen supply chain resilience and responsiveness. We will also explore strategies to improve preparedness for future large-scale public health emergencies.\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Discover key lessons and strategies from recent health crises to boost supply chain resilience and future emergency preparedness."}],"uid":"27233","created_gmt":"2025-06-05 13:11:20","changed_gmt":"2025-06-24 18:08:45","author":"Andy Haleblian","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2025-07-03T12:00:00-04:00","event_time_end":"2025-07-03T13:00:00-04:00","event_time_end_last":"2025-07-03T13:00:00-04:00","gmt_time_start":"2025-07-03 16:00:00","gmt_time_end":"2025-07-03 17:00:00","gmt_time_end_last":"2025-07-03 17:00:00","rrule":null,"timezone":"America\/New_York"},"location":"Online via Zoom","extras":[],"hg_media":{"677182":{"id":"677182","type":"image","title":"SCL Lunch and Learn: \u201cAre You Prepared for the Next Crisis?\u0022","body":null,"created":"1749129504","gmt_created":"2025-06-05 13:18:24","changed":"1749129504","gmt_changed":"2025-06-05 13:18:24","alt":"SCL Lunch and Learn: \u201cAre You Prepared for the Next Crisis?\u0022","file":{"fid":"261056","name":"hg-HHSCM.png","image_path":"\/sites\/default\/files\/2025\/06\/05\/hg-HHSCM.png","image_full_path":"http:\/\/hg.gatech.edu\/\/sites\/default\/files\/2025\/06\/05\/hg-HHSCM.png","mime":"image\/png","size":137638,"path_740":"http:\/\/hg.gatech.edu\/sites\/default\/files\/styles\/740xx_scale\/public\/2025\/06\/05\/hg-HHSCM.png?itok=yiHV5eTI"}}},"media_ids":["677182"],"related_links":[{"url":"https:\/\/gatech.zoom.us\/webinar\/register\/5017490578285\/WN_08LG6S-OQ9Cp6S1EQB1zoQ","title":"Register Online to Attend the Webinar"},{"url":"https:\/\/chhs.gatech.edu\/education\/professional-education","title":"Professional Education at the Center for Health and Humanitarian Systems"},{"url":"https:\/\/www.scl.gatech.edu\/education\/professional-education","title":"Professional Education at the Georgia Tech Supply Chain and Logistics Institute"}],"groups":[{"id":"1250","name":"Center for Health and Humanitarian Systems (CHHS)"},{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"},{"id":"1243","name":"The Supply Chain and Logistics Institute (SCL)"}],"categories":[],"keywords":[{"id":"3615","name":"information session"},{"id":"186857","name":"go-gtmi"},{"id":"122741","name":"physical internet"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"10377","name":"Career\/Professional development"}],"invited_audience":[{"id":"78771","name":"Public"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[{"value":"\u003Cp\u003Ecourse@scl.gatech.edu\u003C\/p\u003E","format":"limited_html"}],"email":[],"slides":[],"orientation":[],"userdata":""}},"682838":{"#nid":"682838","#data":{"type":"event","title":"SCL Course: Creating Business Value with Statistical Analysis (Virtual\/Instructor-led)","body":[{"value":"\u003Ch3\u003E\u003Cstrong\u003ECourse Description\u003C\/strong\u003E\u003C\/h3\u003E\u003Cp\u003EThis course is the second in the four-course Supply Chain Analytics Professional certificate program. It emphasizes operational performance metrics to align supply chain management with strategic business goals. You\u2019ll learn several statistics concepts (e.g. variance analysis, hypothesis testing, forecasting methods) along with inventory management models. You\u2019ll use diagnostic analytics with PowerBI and Python to conduct demand and service profiling, undertake root cause analysis, and use time series forecasting in inventory management.\u003C\/p\u003E\u003Cp\u003EThe online version of the course is comprised of (4) half-day online instructor-led LIVE group webinars and pre-work (e.g. installing and testing software on your computer, testing connectivity with LMS and meeting software, etc.) to be completed before the first day of the course.\u003C\/p\u003E\u003Ch3\u003E\u003Cstrong\u003EWho Should Attend\u003C\/strong\u003E\u003C\/h3\u003E\u003Cp\u003EExperienced business professionals who perform or want to perform analytics to improve their supply chain management processes. They want to tackle strategic goals and to perform leading edge analytics projects that address the full complexity of supply chains.\u003C\/p\u003E\u003Ch3\u003E\u003Cstrong\u003EHow You Will Benefit\u003C\/strong\u003E\u003C\/h3\u003E\u003Cul\u003E\u003Cli\u003EUnderstand why and how to align Supply Chain Management (SCM) strategy with business strategy\u003C\/li\u003E\u003Cli\u003ELearn statistics techniques as they relate to SCM\u003C\/li\u003E\u003Cli\u003EUnderstand inventory management models and how to apply statistics techniques to them\u003C\/li\u003E\u003Cli\u003ECreate time series forecasts based on SCM data\u003C\/li\u003E\u003Cli\u003EUtilize Python and PowerBI to perform statistical analyses, create time series forecasts and visualize results\u003C\/li\u003E\u003C\/ul\u003E\u003Ch3\u003E\u003Cstrong\u003EWhat Is Covered\u003C\/strong\u003E\u003C\/h3\u003E\u003Cul\u003E\u003Cli\u003EThe importance of aligning SCM and business strategy\u003C\/li\u003E\u003Cli\u003EHow to ask the right business questions as they relate to SCM\u003C\/li\u003E\u003Cli\u003EHow to use statistics to identify issues, compare data, and forecast decision outcomes\u003C\/li\u003E\u003Cli\u003EStatistical\u0026nbsp;concepts including variance analysis and hypothesis testing\u003C\/li\u003E\u003Cli\u003EInventory management models\u003C\/li\u003E\u003Cli\u003EApplying statistics to inventory management models\u003C\/li\u003E\u003Cli\u003EForecasting techniques including time series forecasting\u003C\/li\u003E\u003Cli\u003EHands-on practice with these skills using data from the (fictional) Cardboard Company (CBC)\u003C\/li\u003E\u003C\/ul\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003ELearn statistics concepts (e.g. variance analysis, hypothesis testing, forecasting methods) and inventory management models to improve operational performance metrics and align supply chain management with strategic business goals.\u003C\/p\u003E\r\n","format":"limited_html"}],"field_summary_sentence":[{"value":"Learn statistics concepts (e.g. variance analysis, hypothesis testing, forecasting methods) and inventory management models."}],"uid":"27233","created_gmt":"2025-06-23 17:36:05","changed_gmt":"2025-06-23 17:37:01","author":"Andy Haleblian","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2026-04-13T13:00:00-04:00","event_time_end":"2026-04-16T17:00:00-04:00","event_time_end_last":"2026-04-16T17:00:00-04:00","gmt_time_start":"2026-04-13 17:00:00","gmt_time_end":"2026-04-16 21:00:00","gmt_time_end_last":"2026-04-16 21:00:00","rrule":null,"timezone":"America\/New_York"},"location":"Virtual\/Instructor-led","extras":[],"related_links":[{"url":"https:\/\/www.scl.gatech.edu\/education\/professional-education\/course\/scabv","title":"Course detail within the SCL website"}],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"},{"id":"1243","name":"The Supply Chain and Logistics Institute (SCL)"}],"categories":[],"keywords":[{"id":"7251","name":"analytics"},{"id":"167074","name":"Supply Chain"},{"id":"122741","name":"physical internet"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"10377","name":"Career\/Professional development"}],"invited_audience":[{"id":"78771","name":"Public"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[{"value":"\u003Cp\u003E\u003Ca href=\u0022mailto:info@scl.gatech.edu\u0022\u003Einfo@scl.gatech.edu\u003C\/a\u003E\u003C\/p\u003E","format":"limited_html"}],"email":[],"slides":[],"orientation":[],"userdata":""}},"682837":{"#nid":"682837","#data":{"type":"event","title":"SCL Course: Transforming Supply Chain Management and Performance Analysis (Virtual\/Instructor-led)","body":[{"value":"\u003Ch3\u003E\u003Cstrong\u003ECourse Description\u003C\/strong\u003E\u003C\/h3\u003E\u003Cp\u003EThis course is the first in the four-course Supply Chain Analytics Professional certificate program. It prepares you to apply leading-edge analytical methods and technology enablers across the supply chain. You\u2019ll learn the dynamics of supply chains, the most relevant planning challenges, and the roles of different types of analytics. Next, you\u2019ll learn about data cleansing, exploratory data analysis, and visualization. You\u2019ll use Python and PowerBI to analyze the causes of underperformance and to build dashboards to visualize supply chain data. You will leave knowing how to gather, analyze, and prepare your data through descriptive analytics before you dig into deeper applications.\u003C\/p\u003E\u003Cp\u003EThe online version of the course is comprised of (4) half-day instructor-led LIVE group webinars and pre-work (e.g. installing and testing software on your computer, testing connectivity with LMS and meeting software, etc.) to be completed before the first day of the course.\u003C\/p\u003E\u003Ch3\u003E\u003Cstrong\u003EWho Should Attend\u003C\/strong\u003E\u003C\/h3\u003E\u003Cp\u003EExperienced business professionals who perform or want to perform analytics to improve their supply chain management processes. They want to tackle strategic goals and to perform leading edge analytics projects that address the full complexity of supply chains.\u003C\/p\u003E\u003Ch3\u003E\u003Cstrong\u003EHow You Will Benefit\u003C\/strong\u003E\u003C\/h3\u003E\u003Cul\u003E\u003Cli\u003EUnderstand the most relevant planning challenges across the strategic, tactical, and operational levels of supply chains\u003C\/li\u003E\u003Cli\u003ELearn the difference between analytics types, the links between them, and how to best use them to improve\u0026nbsp;supply chain management (SCM)\u0026nbsp;processes\u003C\/li\u003E\u003Cli\u003EUse\u0026nbsp;Key Performance Indicators (KPIs)\u0026nbsp;to find causes of underperformance in supply chains and to plan for analytics projects that will address strategic SCM goals\u003C\/li\u003E\u003Cli\u003EUtilize Python and PowerBI to understand, visualize, and analyze data in order to prepare for deeper analytics\u003C\/li\u003E\u003C\/ul\u003E\u003Ch3\u003E\u003Cstrong\u003EWhat Is Covered\u003C\/strong\u003E\u003C\/h3\u003E\u003Cul\u003E\u003Cli\u003EThe role of analytics in SCM\u003C\/li\u003E\u003Cli\u003ETypes of analytics (descriptive, diagnostic, predictive, and prescriptive) and the relationships between them\u003C\/li\u003E\u003Cli\u003EPreprocessing (cleaning and integrating) data as it relates to SCM\u003C\/li\u003E\u003Cli\u003EConducting exploratory data analysis on supply chain data\u003C\/li\u003E\u003Cli\u003EBest practices for visualizing data and building dashboards\u003C\/li\u003E\u003Cli\u003EIdentifying and analyzing KPIs of SCM\u003C\/li\u003E\u003Cli\u003EHands-on practice with these skills using data from the (fictional) Cardboard Company (CBC)\u003C\/li\u003E\u003C\/ul\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003ELearn the dynamics of supply chains, the most relevant planning challenges, and the roles of different types of analytics. Next, you\u2019ll learn about data cleansing, exploratory data analysis, and visualization. You\u2019ll use Python and PowerBI to analyze the causes of underperformance and to build dashboards to visualize supply chain data. You will leave knowing how to gather, analyze, and prepare your data through descriptive analytics before you dig into deeper applications.\u003C\/p\u003E\r\n","format":"limited_html"}],"field_summary_sentence":[{"value":"Learn to apply leading-edge analytical methods and technology enablers across the supply chain"}],"uid":"27233","created_gmt":"2025-06-23 17:33:03","changed_gmt":"2025-06-23 17:33:55","author":"Andy Haleblian","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2026-02-23T13:00:00-05:00","event_time_end":"2026-02-26T17:00:00-05:00","event_time_end_last":"2026-02-26T17:00:00-05:00","gmt_time_start":"2026-02-23 18:00:00","gmt_time_end":"2026-02-26 22:00:00","gmt_time_end_last":"2026-02-26 22:00:00","rrule":null,"timezone":"America\/New_York"},"location":"Virtual\/Instructor-led","extras":[],"related_links":[{"url":"https:\/\/www.scl.gatech.edu\/education\/professional-education\/course\/scapa","title":"Course webpage within the SCL website"}],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"},{"id":"1243","name":"The Supply Chain and Logistics Institute (SCL)"}],"categories":[],"keywords":[{"id":"7251","name":"analytics"},{"id":"167074","name":"Supply Chain"},{"id":"122741","name":"physical internet"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"10377","name":"Career\/Professional development"}],"invited_audience":[{"id":"78771","name":"Public"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[{"value":"\u003Cp\u003E\u003Ca href=\u0022mailto:info@scl.gatech.edu\u0022\u003Einfo@scl.gatech.edu\u003C\/a\u003E\u003C\/p\u003E","format":"limited_html"}],"email":[],"slides":[],"orientation":[],"userdata":""}},"682835":{"#nid":"682835","#data":{"type":"event","title":"SCL Course: Essentials of Negotiations and Stakeholder Influence (Virtual\/Instructor-led)","body":[{"value":"\u003Ch3\u003E\u003Cstrong\u003ECourse Description\u003C\/strong\u003E\u003C\/h3\u003E\u003Cp\u003EEssentials of Negotiations and Stakeholder Influence level-sets the participants\u0027 understanding of negotiation influence and strengthens preparation, planning and execution activities involved with both simple and complex negotiations. The program includes industry techniques and tools for traditional supplier negotiations, as well as tips for internal cross-functional leadership. Participants walk away with a standard industry and customized individual experience which includes their personal Negotiation Style \u201cDNA\u201d to help them embrace their own natural tendencies and strengths. The program includes mock negotiations to reinforce techniques and tactics immediately in a \u201cno judgement zone\u201d environment.\u003C\/p\u003E\u003Ch3\u003E\u003Cstrong\u003EWho Should Attend\u003C\/strong\u003E\u003C\/h3\u003E\u003Cp\u003EThis course is ideal for sourcing initiative leaders, project leaders, business unit leaders, operations managers, sales leaders and procurement \u0026amp; supply management-related professionals who are involved with supplier selection, contract development and supplier performance management.\u003C\/p\u003E\u003Ch3\u003E\u003Cstrong\u003EHow You Will Benefit\u003C\/strong\u003E\u003C\/h3\u003E\u003Cul\u003E\u003Cli\u003EIncrease emphasis negotiation conditioning and philosophy setting before and throughout the entire sourcing engagement process\u003C\/li\u003E\u003Cli\u003EEnhance your toolbox of industry standard negotiation prep tools like the SWOT and BATNA\u003C\/li\u003E\u003Cli\u003EBetter prepare for negotiations by leveraging knowledge of key negotiation terms and counter-offer tactics\u003C\/li\u003E\u003Cli\u003EImprove negotiation table techniques and soft skills to direct and redirect negotiation momentum\u003C\/li\u003E\u003Cli\u003EHeighten ability to successfully utilize your traditional \u0022comfort zone\u0022 approach in combination with your negotiation team\u2019s strengths by leveraging Personal Negotiation Styles\u003C\/li\u003E\u003C\/ul\u003E\u003Ch3\u003E\u003Cstrong\u003EWhat Is Covered\u003C\/strong\u003E\u003C\/h3\u003E\u003Cul\u003E\u003Cli\u003ENegotiation Conditioning Overview\u003C\/li\u003E\u003Cli\u003ENegotiation Preparation Tools\u003C\/li\u003E\u003Cli\u003ENegotiation Execution Techniques\u003C\/li\u003E\u003Cli\u003EStakeholder Engagement \u0026amp; Team Leadership\u003C\/li\u003E\u003Cli\u003ELive Negotiations Simulation \u0026amp; Feedback\u003C\/li\u003E\u003C\/ul\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EThis course\u0026nbsp;level-sets the participants\u0027 understanding of negotiation influence and strengthens preparation, planning and execution activities involved with both simple and complex negotiations. The program includes industry techniques and tools for traditional supplier negotiations, as well as tips for internal cross-functional leadership. Participants walk away with a standard industry and customized individual experience which includes their personal Negotiation Style \u201cDNA\u201d to help them embrace their own natural tendencies and strengths. The program includes mock negotiations to reinforce techniques and tactics immediately in a \u201cno judgement zone\u201d environment.\u003C\/p\u003E\r\n","format":"limited_html"}],"field_summary_sentence":[{"value":"Level-sets the participants\u0027 understanding of negotiation influence and strengthens preparation, planning and execution activities involved with both simple and complex negotiations."}],"uid":"27233","created_gmt":"2025-06-23 16:16:03","changed_gmt":"2025-06-23 16:17:21","author":"Andy Haleblian","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2026-03-16T13:00:00-04:00","event_time_end":"2026-03-19T17:00:00-04:00","event_time_end_last":"2026-03-19T17:00:00-04:00","gmt_time_start":"2026-03-16 17:00:00","gmt_time_end":"2026-03-19 21:00:00","gmt_time_end_last":"2026-03-19 21:00:00","rrule":null,"timezone":"America\/New_York"},"location":"Virtual\/Instructor-led","extras":[],"related_links":[{"url":"https:\/\/www.scl.gatech.edu\/epn","title":"Course webpage within the SCL website"}],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"},{"id":"1243","name":"The Supply Chain and Logistics Institute (SCL)"}],"categories":[],"keywords":[{"id":"167074","name":"Supply Chain"},{"id":"122741","name":"physical internet"},{"id":"110501","name":"purchasing"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"10377","name":"Career\/Professional development"}],"invited_audience":[{"id":"78771","name":"Public"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[{"value":"\u003Cp\u003E\u003Ca href=\u0022mailto:info@scl.gatech.edu\u0022\u003Einfo@scl.gatech.edu\u003C\/a\u003E\u003C\/p\u003E","format":"limited_html"}],"email":[],"slides":[],"orientation":[],"userdata":""}},"682834":{"#nid":"682834","#data":{"type":"event","title":"SCL Course: Contracting and Legal Oversight (Virtual\/Instructor-led)","body":[{"value":"\u003Ch3\u003E\u003Cstrong\u003ECourse Description\u003C\/strong\u003E\u003C\/h3\u003E\u003Cp\u003EContracting and Legal Oversight provides participants with a holistic and integrated understanding of contract law, contract types, key industry standard contract terms, and contract structure to improve their confidence when creating or modifying contract documents. 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Join us for a pragmatic, industry-tested look at how to spot second-order impacts before they derail your strategy\u2014and leave with simple tools to future-proof your next big move.\u003C\/div\u003E\u003C\/div\u003E\u003C\/div\u003E\u003C\/div\u003E\u003C\/div\u003E\u003Cdiv\u003E\u0026nbsp;\u003C\/div\u003E\u003C\/div\u003E\u003Cp\u003E\u003Ca href=\u0022https:\/\/gatech.zoom.us\/webinar\/register\/2517459520368\/WN_jKZEcHcFSWKIrY4A6DxgOQ#\/registration\u0022\u003E\u003Cstrong\u003ERegister Online to Attend\u003C\/strong\u003E\u003C\/a\u003E\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EIn today\u0027s fast-moving world, great decisions aren\u0027t enough\u2014it\u0027s the ripple effects that can make or break you. Join us for a pragmatic, industry-tested look at how to spot second-order impacts before they derail your strategy\u2014and leave with simple tools to future-proof your next big move.\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Join us for a pragmatic, industry-tested look at how to spot second-order impacts before they derail your strategy\u2014and leave with simple tools to future-proof your next big move."}],"uid":"27233","created_gmt":"2025-04-29 18:57:57","changed_gmt":"2025-04-29 19:07:00","author":"Andy Haleblian","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2025-06-05T12:00:00-04:00","event_time_end":"2025-06-05T13:00:00-04:00","event_time_end_last":"2025-06-05T13:00:00-04:00","gmt_time_start":"2025-06-05 16:00:00","gmt_time_end":"2025-06-05 17:00:00","gmt_time_end_last":"2025-06-05 17:00:00","rrule":null,"timezone":"America\/New_York"},"location":"Online via Zoom","extras":[],"hg_media":{"676997":{"id":"676997","type":"image","title":"SCL Lunch and Learn: \u0022Thinking Beyond the First Step: Unintended Consequences in Supply Chain Decision Making\u0022","body":null,"created":"1745953557","gmt_created":"2025-04-29 19:05:57","changed":"1745953557","gmt_changed":"2025-04-29 19:05:57","alt":"SCL Lunch and Learn: \u0022Thinking Beyond the First Step: Unintended Consequences in Supply Chain Decision Making\u0022","file":{"fid":"260854","name":"banner-SCLLNL-unintended-consequences.png","image_path":"\/sites\/default\/files\/2025\/04\/29\/banner-SCLLNL-unintended-consequences.png","image_full_path":"http:\/\/hg.gatech.edu\/\/sites\/default\/files\/2025\/04\/29\/banner-SCLLNL-unintended-consequences.png","mime":"image\/png","size":162081,"path_740":"http:\/\/hg.gatech.edu\/sites\/default\/files\/styles\/740xx_scale\/public\/2025\/04\/29\/banner-SCLLNL-unintended-consequences.png?itok=AUyxcv7E"}}},"media_ids":["676997"],"related_links":[{"url":"https:\/\/gatech.zoom.us\/webinar\/register\/2517459520368\/WN_jKZEcHcFSWKIrY4A6DxgOQ#\/registration","title":"Register Online to Attend the Webinar"},{"url":"https:\/\/www.scl.gatech.edu\/education\/professional-education","title":"Professional Education at the Georgia Tech Supply Chain and Logistics Institute"}],"groups":[{"id":"1250","name":"Center for Health and Humanitarian Systems (CHHS)"},{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"},{"id":"1243","name":"The Supply Chain and Logistics Institute (SCL)"}],"categories":[],"keywords":[{"id":"3615","name":"information session"},{"id":"186857","name":"go-gtmi"},{"id":"122741","name":"physical internet"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"10377","name":"Career\/Professional development"}],"invited_audience":[{"id":"78771","name":"Public"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[{"value":"\u003Cp\u003Ecourse@scl.gatech.edu\u003C\/p\u003E","format":"limited_html"}],"email":[],"slides":[],"orientation":[],"userdata":""}},"681842":{"#nid":"681842","#data":{"type":"event","title":"ISyE Seminar - Ramesh Johari","body":[{"value":"\u003Cp\u003ETitle: \u0026nbsp;\u003C\/p\u003E\u003Cp\u003EWhen Does Interference Matter? \u0026nbsp;Decision-Making in Platform Experiments\u003C\/p\u003E\u003Cp\u003EAbstract:\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;Online platforms and marketplaces use A\/B experiments to test new features and design changes. \u0026nbsp;Due to constraints on inventory, such experiments typically lead to biased estimation of treatment effects due to the presence of *interference* between treatment and control groups; this phenomenon has been extensively studied in recent literature. \u0026nbsp;By contrast, there has been relatively little discussion of the impact of interference on *decision-making*. \u0026nbsp; In this talk, we consider a benchmark Markovian model of a capacity-constrained platform, and study the impact of interference on (1) false positive probability and (2) statistical power. \u0026nbsp;We show that for a particular class of \u0022monotone\u0022 treatments (informally, treatments where the sign of the effect does not depend on the level of available inventory), using the standard t statistic with the na\u00efve difference-in-means estimator and classical variance estimator both correctly controls the false positive probability, and generally yields *higher* statistical power than any unbiased estimation method. \u0026nbsp;We show that in principle, these effects can be undermined when treatments are not monotone.\u003C\/p\u003E\u003Cp\u003EOur results have important implications for the practical deployment of debiasing strategies for A\/B experiments. \u0026nbsp;In particular, they highlight the need for platforms to carefully define their objectives and understand the nature of their interventions when determining appropriate estimation and decision-making approaches. \u0026nbsp;Notably, when interventions are monotone, the platform may actually be worse off by pursuing a debiased decision-making approach.\u003C\/p\u003E\u003Cp\u003EJoint work with Hannah Li, Anushka Murthy, and Gabriel Weintraub.\u003C\/p\u003E\u003Cp\u003EBio:\u0026nbsp;\u003C\/p\u003E\u003Cp\u003ERamesh Johari is a Professor at Stanford University, with a full-time appointment in the Department of Management Science and Engineering (MS\u0026amp;E), and a courtesy appointment in the Department of Electrical Engineering (EE). He is an associate director of Stanford Data Science, and co-director of the Stanford Causal Science Center. He is a member of the Operations Research group and the Social Algorithms Lab (SOAL) in MS\u0026amp;E, the Information Systems Laboratory in EE, and the Institute for Computational and Mathematical Engineering. He received an A.B. in Mathematics from Harvard, a Certificate of Advanced Study in Mathematics from Cambridge, and a Ph.D. in Electrical Engineering and Computer Science from MIT. \u0026nbsp;His current research interests include market design, causal inference, and experimentation.\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EOnline platforms and marketplaces use A\/B experiments to test new features and design changes. \u0026nbsp;Due to constraints on inventory, such experiments typically lead to biased estimation of treatment effects due to the presence of *interference* between treatment and control groups; this phenomenon has been extensively studied in recent literature. \u0026nbsp;By contrast, there has been relatively little discussion of the impact of interference on *decision-making*. \u0026nbsp; In this talk, we consider a benchmark Markovian model of a capacity-constrained platform and study the impact of interference on (1) false positive probability and (2) statistical power. \u0026nbsp;We show that for a particular class of \u0022monotone\u0022 treatments (informally, treatments where the sign of the effect does not depend on the level of available inventory), using the standard t statistic with the na\u00efve difference-in-means estimator and classical variance estimator both correctly controls the false positive probability, and generally yields *higher* statistical power than any unbiased estimation method. \u0026nbsp;We show that in principle, these effects can be undermined when treatments are not monotone.\u003C\/p\u003E\u003Cp\u003EOur results have important implications for the practical deployment of debiasing strategies for A\/B experiments. \u0026nbsp;In particular, they highlight the need for platforms to carefully define their objectives and understand the nature of their interventions when determining appropriate estimation and decision-making approaches. \u0026nbsp;Notably, when interventions are monotone, the platform may actually be worse off by pursuing a debiased decision-making approach.\u003C\/p\u003E\u003Cp\u003EJoint work with Hannah Li, Anushka Murthy, and Gabriel Weintraub.\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"When Does Interference Matter?  Decision-Making in Platform Experiments"}],"uid":"36374","created_gmt":"2025-04-16 16:14:46","changed_gmt":"2025-04-16 16:18:01","author":"mwelch39","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2025-04-18T11:30:00-04:00","event_time_end":"2025-04-18T12:30:00-04:00","event_time_end_last":"2025-04-18T12:30:00-04:00","gmt_time_start":"2025-04-18 15:30:00","gmt_time_end":"2025-04-18 16:30:00","gmt_time_end_last":"2025-04-18 16:30:00","rrule":null,"timezone":"America\/New_York"},"location":" ISYE Groseclose 402","extras":[],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"177814","name":"Postdoc"},{"id":"78771","name":"Public"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"679651":{"#nid":"679651","#data":{"type":"event","title":"11th International Physical Internet Conference (IPIC 2025)","body":[{"value":"\u003Ch3\u003E\u003Cstrong\u003EPlease join us for the 11th International Physical Internet Conference taking place June 18-20, 2025 in Hong Kong, China at The Hong Kong Polytechnic University.\u0026nbsp;The event is hosted by\u0026nbsp;the Department of Industrial \u0026amp; Systems Engineering (ISE) and the Research Institute of Advanced Manufacturing (RIAM) of The Hong Kong Polytechnic University.\u003C\/strong\u003E\u003C\/h3\u003E\u003Cp\u003EThe Physical Internet Initiative aims at transforming the way physical objects are moved, stored, realized, supplied and used, pursuing global logistics efficiency and sustainability. Originating from Professor \u003Ca href=\u0022https:\/\/www.scl.gatech.edu\/users\/benoit-montreuil\u0022\u003E\u003Cstrong\u003EBenoit Montreuil\u003C\/strong\u003E\u003C\/a\u003E in 2006, this ground breaking vision, revolutionizing current paradigms, has stirred great interest from scientific, industrial as well as governmental communities.\u003C\/p\u003E\u003Cp\u003EThe \u003Ca href=\u0022https:\/\/www.pi.events\/\u0022\u003E\u003Cstrong\u003EInternational Physical Internet Conference\u003C\/strong\u003E\u003C\/a\u003E\u0026nbsp;aims to provide an open forum for researchers, industry representatives, government officials and citizens to together explore, discuss, introduce leading edge concepts, methodologies, recent projects, technological advancements, start-up initiatives, for current and future Physical Internet implementation.\u003C\/p\u003E\u003Cp\u003EConference topics include inter-connected logistics, PI fundamentals, business models, governance and implementation, cross-chain control, synchromodal transportation, IT systems, stakeholders and their roles. New business models, enabling technologies and experimentations already underway will be presented, making this meeting a unique opportunity to learn, network and discuss the latest results and challenges about interconnected logistics.\u003C\/p\u003E\u003Cp\u003EAnd, because logistics is global, participants will be from all over the world including researchers, industrial and international institution members, local authorities and standardization committees.\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cdiv\u003E\u003Cdiv\u003E\u003Cdiv\u003E\u003Cp\u003EPlease join us for the 11th International Physical Internet Conference taking place June 18-20, 2025 in Hong Kong, China.\u003C\/p\u003E\u003C\/div\u003E\u003C\/div\u003E\u003C\/div\u003E\u003Cdiv\u003E\u003Cdiv\u003E\u003Cbr\u003E\u0026nbsp;\u003C\/div\u003E\u003C\/div\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Please join us for the 11th International Physical Internet Conference taking place June 18-20, 2025 in Hong Kong, China."}],"uid":"36698","created_gmt":"2025-01-16 14:01:34","changed_gmt":"2025-04-16 15:34:22","author":"dramirez65","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2025-06-18T09:00:00-04:00","event_time_end":"2025-06-20T18:00:00-04:00","event_time_end_last":"2025-06-20T18:00:00-04:00","gmt_time_start":"2025-06-18 13:00:00","gmt_time_end":"2025-06-20 22:00:00","gmt_time_end_last":"2025-06-20 22:00:00","rrule":null,"timezone":"America\/New_York"},"location":" The Hong Kong Polytechnic University (PolyU)","extras":[],"hg_media":{"676856":{"id":"676856","type":"image","title":"11th International Physical Internet Conference","body":null,"created":"1744817559","gmt_created":"2025-04-16 15:32:39","changed":"1744817559","gmt_changed":"2025-04-16 15:32:39","alt":"11th International Physical Internet Conference","file":{"fid":"260701","name":"IPIC2025_banner_sq.png","image_path":"\/sites\/default\/files\/2025\/04\/16\/IPIC2025_banner_sq.png","image_full_path":"http:\/\/hg.gatech.edu\/\/sites\/default\/files\/2025\/04\/16\/IPIC2025_banner_sq.png","mime":"image\/png","size":1298846,"path_740":"http:\/\/hg.gatech.edu\/sites\/default\/files\/styles\/740xx_scale\/public\/2025\/04\/16\/IPIC2025_banner_sq.png?itok=wXyX_Lus"}}},"media_ids":["676856"],"related_links":[{"url":"https:\/\/ipic2025.pi.events\/","title":"Conference Website"}],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"},{"id":"1243","name":"The Supply Chain and Logistics Institute (SCL)"}],"categories":[],"keywords":[{"id":"122741","name":"physical internet"},{"id":"143871","name":"Physical Internet Center"},{"id":"194222","name":"Supply chain "},{"id":"233","name":"Logistics"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1789","name":"Conference\/Symposium"},{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"78771","name":"Public"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[{"value":"\u003Cp\u003EPlease direct questions relating to the conference to \u003Ca href=\u0022mailto:ipic2025.hk@polyu.edu.hk\u0022\u003Eipic2025.hk@polyu.edu.hk\u003C\/a\u003E\u003C\/p\u003E","format":"limited_html"}],"email":[],"slides":[],"orientation":[],"userdata":""}},"681579":{"#nid":"681579","#data":{"type":"event","title":"ISYE Statistic Seminar - Kathryn Maupin","body":[{"value":"\u003Cp\u003ETitle: Bayesian Optimal Design of Pulsed Power Experiments\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EAbstract:\u003C\/p\u003E\u003Cp\u003ETraditionally, there are two pillars of science: theory and experimentation. These two inform one another and lead scientists to make educated guesses and decisions toward advancing science. More recently, the driving force behind scientific advancement has not just focused on how much information can be learned, but how quickly. Additionally, experimental data can be costly and difficult to obtain. With these motivations in mind, the field of experimental design aims to maximize the information gained from as few experimental data points as possible. Computation has emerged as a third pillar of science to complement the traditional two and has been used to facilitate optimal experimental design.\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003ESandia\u2019s Z machine is the world\u2019s most powerful and efficient laboratory radiation source. Z experiments often exhibit large current losses, so a principal uncertainty is how effectively current can be delivered. Power flow simulations are very intensive, making them infeasible to use in critical design and optimization studies. Developing a consistent picture of how losses develop and evolve would improve understanding of present-day experiments and better constrain circuit model representations, providing a basis for quantifying uncertainties in circuit models applied to Z and improve confidence in predictions of target performance. This presentation details the implementation of a Bayesian optimization study to maximize the information gained from Z experimental data and design.\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E* SNL is managed and operated by NTESS under DOE NNSA contract DE-NA0003525\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EBio:\u003C\/p\u003E\u003Cp\u003EKathryn Maupin is a Principal Member of the Technical Staff at Sandia National Laboratories. Motivated by a passion for transforming uncertainty into actionable insights, Kathryn leverages her extensive expertise in model validation, model form error quantification, and Bayesian analyses to drive innovative solutions that enhance research outcomes.\u003C\/p\u003E\u003Cp\u003EKathryn earned her PhD in Computational Science, Engineering, and Mathematics, along with her M.S. in Computational and Applied Mathematics, both from The University of Texas at Austin. Her fascination with mathematical modeling began at the University of California, San Diego, where she completed her B.A. in Applied Mathematics.\u003C\/p\u003E\u003Cp\u003EWhen she is not immersed in data and algorithms, Kathryn enjoys the chaos of family life with her three children and three dogs. Looking ahead, Kathryn aspires to continue pushing the boundaries of computational science while encouraging others to confront ubiquitous uncertainty in their work.\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003E\u003Cstrong\u003EAbstract:\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003ETraditionally, there are two pillars of science: theory and experimentation. These two inform one another and lead scientists to make educated guesses and decisions toward advancing science. More recently, the driving force behind scientific advancement has not just focused on how much information can be learned, but how quickly. Additionally, experimental data can be costly and difficult to obtain. With these motivations in mind, the field of experimental design aims to maximize the information gained from as few experimental data points as possible. Computation has emerged as a third pillar of science to complement the traditional two and has been used to facilitate optimal experimental design.\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Title: Bayesian Optimal Design of Pulsed Power Experiments"}],"uid":"36433","created_gmt":"2025-04-03 19:44:32","changed_gmt":"2025-04-03 19:46:03","author":"mrussell89","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2025-04-08T11:00:00-04:00","event_time_end":"2025-04-08T12:00:00-04:00","event_time_end_last":"2025-04-08T12:00:00-04:00","gmt_time_start":"2025-04-08 15:00:00","gmt_time_end":"2025-04-08 16:00:00","gmt_time_end_last":"2025-04-08 16:00:00","rrule":null,"timezone":"America\/New_York"},"location":"Groseclose 402","extras":[],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"177814","name":"Postdoc"},{"id":"78771","name":"Public"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"681452":{"#nid":"681452","#data":{"type":"event","title":"ISyE Seminar - Walter Rei","body":[{"value":"\u003Cp\u003ETitle:\u003C\/p\u003E\u003Cp\u003EDealing with Ambiguity in Humanitarian Decision-Making\u003C\/p\u003E\u003Cp\u003EAbstract:\u003C\/p\u003E\u003Cp\u003EOne of the major challenges humanitarian organizations face in response planning is managing the inherent ambiguity and uncertainty of disaster situations. In post-disaster contexts, information from various sources (assessing both the needs of affected populations and the extent of damage in the impacted area) often contains missing elements and inconsistencies, which can hinder effective decision-making. In this talk, I will present a new methodological framework that combines graph clustering and stochastic optimization to support humanitarian decision-makers in analyzing the implications of divergent estimates from multiple data sources and in efficiently integrating these estimates into the decision-making process. The usefulness of the proposed approach is demonstrated through a realistic case study on shelter location planning for internally displaced people (IDPs) in a conflict setting, specifically the Syrian civil war. We use needs assessment data from two reliable sources toestimate shelter requirements in Idleb, a district of Syria. Our case study shows that the framework enables decision-makers to assess the degree of ambiguity in the data and the level of consensus across sources, ultimately supporting better-informed decisions and more effective planning for the delivery of humanitarian aid.\u003C\/p\u003E\u003Cp\u003EBio:\u003C\/p\u003E\u003Cp\u003EWalter Rei is a Professor of Operations Research in the Department of Analytics, Operations, and Information Technologies at the \u00c9cole des Sciences de la Gestion, Universit\u00e9 du Qu\u00e9bec \u00e0 Montr\u00e9al, Canada. He currently holds the Canada Research Chair in Stochastic Optimization of Transport and Logistics Systems and is also a member of the Interuniversity Research Centre on Enterprise Networks, Logistics and Transportation (CIRRELT). His research focuses on the development of efficient solution methodologies for integer programs and combinatorial optimization models relevant to transportation and logistics problems involving uncertainty.\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EOne of the major challenges humanitarian organizations face in response planning is managing the inherent ambiguity and uncertainty of disaster situations. In post-disaster contexts, information from various sources (assessing both the needs of affected populations and the extent of damage in the impacted area) often contains missing elements and inconsistencies, which can hinder effective decision-making. In this talk, I will present a new methodological framework that combines graph clustering and stochastic optimization to support humanitarian decision-makers in analyzing the implications of divergent estimates from multiple data sources and in efficiently integrating these estimates into the decision-making process. The usefulness of the proposed approach is demonstrated through a realistic case study on shelter location planning for internally displaced people (IDPs) in a conflict setting, specifically the Syrian civil war. We use needs assessment data from two reliable sources to estimate shelter requirements in Idleb, a district of Syria. Our case study shows that the framework enables decision-makers to assess the degree of ambiguity in the data and the level of consensus across sources, ultimately supporting better-informed decisions and more effective planning for the delivery of humanitarian aid.\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Dealing with Ambiguity in Humanitarian Decision-Making"}],"uid":"36374","created_gmt":"2025-03-31 12:42:04","changed_gmt":"2025-03-31 12:44:28","author":"mwelch39","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2025-04-11T11:30:00-04:00","event_time_end":"2025-04-11T12:30:00-04:00","event_time_end_last":"2025-04-11T12:30:00-04:00","gmt_time_start":"2025-04-11 15:30:00","gmt_time_end":"2025-04-11 16:30:00","gmt_time_end_last":"2025-04-11 16:30:00","rrule":null,"timezone":"America\/New_York"},"location":" ISYE Groseclose 402","extras":[],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"177814","name":"Postdoc"},{"id":"78771","name":"Public"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"681451":{"#nid":"681451","#data":{"type":"event","title":"ISyE Seminar - Ann Campbell","body":[{"value":"\u003Cp\u003ETitle:\u003C\/p\u003E\u003Cp\u003EThe Search for Parking for Commercial Last-Mile Delivery in Urban Environments (or should they?)\u003C\/p\u003E\u003Cp\u003EAbstract:\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EParking is a time-consuming, and thus expensive, part of last-mile delivery in urban environments. \u0026nbsp;To build insights into driver parking behavior, we introduce the Stochastic Parking Problem (SPP) to model the search process for parking where delivery drivers may choose to park at available parking spots or double park at unavailable parking spots at the risk of receiving a fine. \u0026nbsp;To this end, the Stochastic Parking Problem (SPP) models the parking search process as a Markov Decision Process. We characterize the structure of the optimal parking policy for the SPP based on the probability that each parking spot is available and the expected cost of double parking. Further, we provide a polynomial-time algorithm to find the optimal policy for the SPP. \u0026nbsp;We utilize the optimal policy for the SPP to derive managerial insights regarding how drivers should approach the parking search process. In doing so, we identify sufficient enforcement levels to eliminate double parking from optimal parking decisions for last-mile delivery drivers.\u003C\/p\u003E\u003Cp\u003EBio:\u003C\/p\u003E\u003Cp\u003EAnn Melissa Campbell is the Clement T. and Sylvia H. Hanson Family Chair in Manufacturing Productivity in the Department of Business Analytics at the Henry B. Tippie College of Business. \u0026nbsp;Her research focuses on freight transportation, especially on problems related to new and emerging business models. \u0026nbsp;She is a recipient of the NSF CAREER Award and serves as an Area Editor for Transportation Science. \u0026nbsp;As department chair, she led the department\u2019s efforts to win the 2021 INFORMS UPS George D. Smith Prize for excellence in analytics education. \u0026nbsp;Since 2022, she has chaired the annual FutureBAProf workshop focused on educating PhD students and postdocs about academic careers in business schools.\u003Cbr\u003E\u0026nbsp;\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EParking is a time-consuming, and thus expensive, part of last-mile delivery in urban environments. \u0026nbsp;To build insights into driver parking behavior, we introduce the Stochastic Parking Problem (SPP) to model the search process for parking where delivery drivers may choose to park at available parking spots or double park at unavailable parking spots at the risk of receiving a fine. \u0026nbsp;To this end, the Stochastic Parking Problem (SPP) models the parking search process as a Markov Decision Process. We characterize the structure of the optimal parking policy for the SPP based on the probability that each parking spot is available and the expected cost of double parking. Further, we provide a polynomial-time algorithm to find the optimal policy for the SPP. \u0026nbsp;We utilize the optimal policy for the SPP to derive managerial insights regarding how drivers should approach the parking search process. In doing so, we identify sufficient enforcement levels to eliminate double parking from optimal parking decisions for last-mile delivery drivers.\u003Cbr\u003E\u0026nbsp;\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"The Search for Parking for Commercial Last-Mile Delivery in Urban Environments (or should they?)"}],"uid":"36374","created_gmt":"2025-03-31 12:35:26","changed_gmt":"2025-03-31 12:38:30","author":"mwelch39","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2025-04-04T11:30:00-04:00","event_time_end":"2025-04-04T12:30:00-04:00","event_time_end_last":"2025-04-04T12:30:00-04:00","gmt_time_start":"2025-04-04 15:30:00","gmt_time_end":"2025-04-04 16:30:00","gmt_time_end_last":"2025-04-04 16:30:00","rrule":null,"timezone":"America\/New_York"},"location":" ISYE Groseclose 402","extras":[],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"177814","name":"Postdoc"},{"id":"78771","name":"Public"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"681432":{"#nid":"681432","#data":{"type":"event","title":"ISyE Community Research Project Showcase","body":[{"value":"\u003Cdiv\u003EWe are excited to invite you to the \u003Ca href=\u0022https:\/\/www.isye.gatech.edu\/news\/turn-learning-action-industrial-engineering-community-research-projects\u0022\u003E2025 Project Showcase, of the Community Research Project (CRP)\u003C\/a\u003E at \u003Ca href=\u0022https:\/\/www.isye.gatech.edu\u0022\u003EGeorgia Tech\u0027s H. Milton Stewart School of Industrial and Systems Engineering.\u003C\/a\u003E This event celebrates the integration of theoretical knowledge to real-world challenges with Atlanta-based organizations, while improving societal conditions.\u003C\/div\u003E\u003Cdiv\u003E\u0026nbsp;\u003C\/div\u003E\u003Cdiv\u003EEvent Details:\u003C\/div\u003E\u003Cul\u003E\u003Cli\u003E\u003Cdiv\u003EPoster Symposium:\u0026nbsp;Wednesday, April 2, 2025, from 10:00 AM to 4:00 PM\u003C\/div\u003E\u003C\/li\u003E\u003Cli\u003E\u003Cdiv\u003EProject Showcase Event:\u0026nbsp;Wednesday, April 2, 2025, from 5:00 PM to 8:00 PM\u003C\/div\u003E\u003C\/li\u003E\u003Cli\u003E\u003Cdiv\u003ELocation:\u0026nbsp;ISyE Main Atrium\u003C\/div\u003E\u003C\/li\u003E\u003C\/ul\u003E\u003Cdiv\u003EThe CRP offers a low-stakes Senior Design experience, providing early undergraduates with the opportunity to be introduced to research while developing much needed professional skills. This program integrates leadership development, team building skills, and community partnerships\u0026nbsp;to create a holistic student experience.\u003C\/div\u003E\u003Cdiv\u003E\u0026nbsp;\u003C\/div\u003E\u003Cdiv\u003ECRP Key Collaborators:\u003Cbr\u003EOur project is supported by a network of experts who help students navigate common leadership challenges, working effectively in a group setting, and translating their experience to their resume and during interviews. Key collaborators include:\u003C\/div\u003E\u003Cdiv\u003EDr. Stacey Doremus: Leadership Education and Development\u003C\/div\u003E\u003Cul\u003E\u003Cli\u003E\u003Cdiv\u003EDr. Mary Lynn Realff: Effective Team Dynamic Initiative\u003C\/div\u003E\u003C\/li\u003E\u003Cli\u003E\u003Cdiv\u003EDr. Brandy Blake: Professional and Technical Communication\u003C\/div\u003E\u003C\/li\u003E\u003Cli\u003E\u003Cdiv\u003ELaura Garcia: UG Career Education\u003C\/div\u003E\u003C\/li\u003E\u003Cli\u003E\u003Cdiv\u003EDr. Sarah Brackmann: Community-Based Learning\u003C\/div\u003E\u003C\/li\u003E\u003C\/ul\u003E\u003Cdiv\u003EThrough these collaborations, undergraduates incorporate High-Impact Practices (HIPs) to encourage collaborative projects, service learning, and undergraduate research. \u0026nbsp;We believe that experiential learning is a potent tool for connecting theoretical knowledge with practical application, and we are excited to share our journey with you.\u003C\/div\u003E\u003Cdiv\u003E\u0026nbsp;\u003C\/div\u003E\u003Cdiv\u003ETo register or for more information, please \u003Ca href=\u0022https:\/\/forms.office.com\/r\/AGzakTQbRU\u0022 id=\u0022OWA1a153157-5ecf-5df7-2d22-d038b294241f\u0022 rel=\u0022noopener noreferrer\u0022 target=\u0022_blank\u0022 title=\u0022https:\/\/forms.office.com\/r\/AGzakTQbRU\u0022\u003E\u003Cstrong\u003ECLICK HERE\u003C\/strong\u003E\u003C\/a\u003E\u0026nbsp;or email us at \u003Ca href=\u0022mailto:case@isye.gatech.edu\u0022 id=\u0022OWA3e130c25-990f-e766-b53c-9395ae1a449c\u0022 title=\u0022mailto:case@isye.gatech.edu\u0022\u003Ecase@isye.gatech.edu\u003C\/a\u003E.\u003C\/div\u003E\u003Cdiv\u003E\u0026nbsp;\u003C\/div\u003E\u003Cdiv\u003EThank you for your support, and we look forward to seeing you at the showcase!\u003C\/div\u003E\u003Cdiv\u003E\u0026nbsp;\u003C\/div\u003E\u003Cp\u003ERead the full story here: \u003Ca href=\u0022https:\/\/www.isye.gatech.edu\/news\/turn-learning-action-industrial-engineering-community-research-projects\u0022\u003Ehttps:\/\/www.isye.gatech.edu\/news\/turn-learning-action-industrial-engineering-community-research-projects\u003C\/a\u003E\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003E10 undergraduate industrial engineering groups will be presenting their research to field professionals, providing solutions to Atlanta\u0027s industry conglomerates\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"10 undergraduate industrial engineering groups will be presenting their research to field professionals, providing solutions to Atlanta\u0027s industry conglomerates"}],"uid":"36284","created_gmt":"2025-03-28 16:14:25","changed_gmt":"2025-03-28 16:22:47","author":"chenriquez8","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2025-04-02T10:00:00-04:00","event_time_end":"2025-04-02T20:00:00-04:00","event_time_end_last":"2025-04-02T20:00:00-04:00","gmt_time_start":"2025-04-02 14:00:00","gmt_time_end":"2025-04-03 00:00:00","gmt_time_end_last":"2025-04-03 00:00:00","rrule":null,"timezone":"America\/New_York"},"location":"ISyE Main Atrium","extras":["free_food","freebies"],"hg_media":{"676574":{"id":"676574","type":"image","title":"Community Research Project Team","body":null,"created":"1742319861","gmt_created":"2025-03-18 17:44:21","changed":"1742319861","gmt_changed":"2025-03-18 17:44:21","alt":"Community Research Project Team","file":{"fid":"260378","name":"SQUARE-PICS.png","image_path":"\/sites\/default\/files\/2025\/03\/18\/SQUARE-PICS.png","image_full_path":"http:\/\/hg.gatech.edu\/\/sites\/default\/files\/2025\/03\/18\/SQUARE-PICS.png","mime":"image\/png","size":1503875,"path_740":"http:\/\/hg.gatech.edu\/sites\/default\/files\/styles\/740xx_scale\/public\/2025\/03\/18\/SQUARE-PICS.png?itok=h_rR3AM8"}}},"media_ids":["676574"],"related_links":[{"url":"https:\/\/www.isye.gatech.edu\/news\/turn-learning-action-industrial-engineering-community-research-projects","title":"Learn More! Turn Learning into Action with Industrial Engineering Community Research Projects"}],"groups":[{"id":"660354","name":"Center for Academics, Success, and Equity"},{"id":"1250","name":"Center for Health and Humanitarian Systems (CHHS)"},{"id":"1237","name":"College of Engineering"},{"id":"660346","name":"Master of Science in Analytics"},{"id":"1188","name":"Research Horizons"},{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"},{"id":"1243","name":"The Supply Chain and Logistics Institute (SCL)"}],"categories":[],"keywords":[{"id":"1191","name":"industrial engineering"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"10377","name":"Career\/Professional development"},{"id":"1788","name":"Other\/Miscellaneous"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"177814","name":"Postdoc"},{"id":"78771","name":"Public"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[{"value":"\u003Cp\u003EValarie McConico, Program and Operations Manager, vmcconico3@gatech.edu\u003C\/p\u003E","format":"limited_html"}],"email":[],"slides":[],"orientation":[],"userdata":""}},"681348":{"#nid":"681348","#data":{"type":"event","title":"SCL IRC Seminar: \u0022Tariffs, Uncertainty, and Trade\u0022","body":[{"value":"\u003Cp\u003EThe \u003Ca href=\u0022https:\/\/www.scl.gatech.edu\u0022\u003ESupply Chain and Logistics Institute\u003C\/a\u003E and \u003Ca href=\u0022https:\/\/www.scl.gatech.edu\/outreach\/SCLO\u0022\u003ESupply Chain \u0026amp; Logistics Organization\u003C\/a\u003E Student Group is co-hosting a special event seminar featuring Jerry Parrish from Metro Atlanta Chamber of Commerce.\u003C\/p\u003E\u003Ch2\u003ETariffs, Uncertainty, and Trade\u0026nbsp;\u003C\/h2\u003E\u003Ch3\u003Efeaturing Jerry Parrish, Chief Economist with the Metro Atlanta Chamber of Commerce\u003C\/h3\u003E\u003Ch4\u003EApril 10, 2025\u003Cbr\u003E11am-12pm ET | Seminar\u003C\/h4\u003E\u003Cp\u003E\u003Cstrong\u003EVenue\/Location\u003C\/strong\u003E\u003Cbr\u003E\u003Ca href=\u0022https:\/\/studentcenter.gatech.edu\/parking-information\u0022\u003EGeorgia Tech Exhibition Hall\u003C\/a\u003E - \u003Ca href=\u0022https:\/\/studentcenter.gatech.edu\/sites\/default\/files\/2023-03\/Exhibition%20Hall%20Maps%20PDF.pdf\u0022\u003EHome Park Room\u003C\/a\u003E (2nd floor)\u003Cbr\u003EGeorgia Institute of Technology\u003Cbr\u003E460 4th St NW, Atlanta, GA 30332\u003Cbr\u003E\u0026nbsp;\u003C\/p\u003E\u003Ch3\u003ESESSION OVERVIEW\u003C\/h3\u003E\u003Cp\u003EJoin us for an insightful seminar where Dr. Parrish will delve into the complex interplay between tariffs, inflation, interest rates, and other economic factors. The seminar will explore how these elements influence not only the economy of Georgia but also the broader American and global economies. Dr. Parrish will provide an analysis of current economic trends, shedding light on the uncertainties and challenges faced by businesses and policymakers in today\u0027s interconnected world. Attendees will gain a deeper understanding of the economic forces at play and their potential impacts on trade, investment, and economic growth.\u003C\/p\u003E\u003Ch3\u003ESESSION SPEAKER\u003C\/h3\u003E\u003Cp\u003EJerry D. Parrish, Ph.D. is the Chief Economist at the Metro Atlanta Chamber. He previously served as the Director of State and Local Policy Analysis at the Florida Institute of Government at Florida State University, where he also spent twelve years on the faculty of the Masters of Applied Economics Program.\u003C\/p\u003E\u003Cp\u003EDr. Parrish has held several prominent positions, including Chief Economist and Director of Research for the Florida Chamber Foundation, Chief Economist and Director of the Center for Competitive Florida at Florida TaxWatch, and Associate Director of the Center for Economic Forecasting \u0026amp; Analysis (CEFA) at Florida State University. His extensive experience in the private sector includes management roles at international manufacturing companies.\u003C\/p\u003E\u003Cp\u003EJerry holds a B.S. in Agricultural Business and Economics from Auburn University, an M.B.A. from Bellarmine University, an M.S. in Economics from the University of North Carolina at Charlotte, and a Ph.D. in Economics from Auburn University.\u003C\/p\u003E\u003Ch3\u003E\u003Ca href=\u0022https:\/\/eforms.scl.gatech.edu\/apr10seminar\u0022\u003E\u003Cstrong\u003ERegister Online for our Special Event Seminar\u003C\/strong\u003E\u003C\/a\u003E\u003C\/h3\u003E\u003Cp\u003E\u003Cbr\u003EFor questions, please email \u003Ca href=\u0022mailto:event@scl.gatech.edu\u0022\u003Eevent@scl.gatech.edu\u003C\/a\u003E.\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EThe \u003Ca href=\u0022https:\/\/www.scl.gatech.edu\u0022\u003ESupply Chain and Logistics Institute\u003C\/a\u003E and \u003Ca href=\u0022https:\/\/www.scl.gatech.edu\/outreach\/SCLO\u0022\u003ESupply Chain \u0026amp; Logistics Organization\u003C\/a\u003E Student Group is co-hosting a special event seminar featuring Jerry Parrish with the Metro Atlanta Chamber of Commerce.\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Join the Supply Chain and Logistics Institute for a special event seminar featuring Jerry Parrish with the Metro Atlanta Chamber of Commerce."}],"uid":"27233","created_gmt":"2025-03-24 21:30:13","changed_gmt":"2025-03-24 22:46:05","author":"Andy Haleblian","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2025-04-10T11:00:00-04:00","event_time_end":"2025-04-10T12:00:00-04:00","event_time_end_last":"2025-04-10T12:00:00-04:00","gmt_time_start":"2025-04-10 15:00:00","gmt_time_end":"2025-04-10 16:00:00","gmt_time_end_last":"2025-04-10 16:00:00","rrule":null,"timezone":"America\/New_York"},"location":"Georgia Tech Exhibition Hall - Home Park Room (2nd floor)","extras":[],"hg_media":{"676662":{"id":"676662","type":"image","title":"GT-SCLIRC_Seminar_JerryParrish_20250410.jpg","body":"\u003Cp\u003EClick to Enlarge\u003C\/p\u003E","created":"1742855314","gmt_created":"2025-03-24 22:28:34","changed":"1743075931","gmt_changed":"2025-03-27 11:45:31","alt":"SCL IRC Seminar: \u0022Tariffs, Uncertainty, and Trade\u0022","file":{"fid":"260475","name":"GT-SCLIRC_Seminar_JerryParrish_20250410.jpg","image_path":"\/sites\/default\/files\/2025\/03\/24\/GT-SCLIRC_Seminar_JerryParrish_20250410.jpg","image_full_path":"http:\/\/hg.gatech.edu\/\/sites\/default\/files\/2025\/03\/24\/GT-SCLIRC_Seminar_JerryParrish_20250410.jpg","mime":"image\/jpeg","size":510299,"path_740":"http:\/\/hg.gatech.edu\/sites\/default\/files\/styles\/740xx_scale\/public\/2025\/03\/24\/GT-SCLIRC_Seminar_JerryParrish_20250410.jpg?itok=laQBUvaN"}}},"media_ids":["676662"],"related_links":[{"url":"https:\/\/eforms.scl.gatech.edu\/apr10seminar","title":"Register Online for our Special Event SCL IRC seminar"},{"url":"https:\/\/www.scl.gatech.edu\/sites\/default\/files\/downloads\/sclirc\/GT-SCLIRC_Seminar_JerryParrish_20250410.pdf","title":"Download Flyer"}],"groups":[{"id":"1250","name":"Center for Health and Humanitarian Systems (CHHS)"},{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"},{"id":"1243","name":"The Supply Chain and Logistics Institute (SCL)"}],"categories":[],"keywords":[{"id":"167074","name":"Supply Chain"},{"id":"233","name":"Logistics"},{"id":"122741","name":"physical internet"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"78771","name":"Public"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[{"value":"\u003Cp\u003Eevent@scl.gatech.edu\u003C\/p\u003E","format":"limited_html"}],"email":[],"slides":[],"orientation":[],"userdata":""}},"681238":{"#nid":"681238","#data":{"type":"event","title":"ISyE Seminar - Lawrence Wein","body":[{"value":"\u003Cp\u003E\u003Cstrong\u003ETITLE:\u0026nbsp;\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003EAnalysis of the Genealogy Process in Forensic Investigative Genetic Genealogy\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EABSTRACT:\u0026nbsp;\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003EThe genealogy process is typically the most time-consuming part of -- and a limiting factor in the success of -- forensic investigative genetic genealogy, which is a new approach to solving violent crimes and identifying human remains. We formulate a stochastic dynamic program that -- given the list of matches and their genetic distances to the unknown target -- chooses the best decision at each point in time: which match to investigate (i.e., find its ancestors), which ancestors of these matches to descend from (i.e., find its descendants), or whether to terminate the investigation. The objective is to maximize the probability of finding the target minus a cost on the expected size of the final family tree. We estimate the \u0026nbsp;parameters of our model using data from 17 cases (eight solved, nine unsolved) from the DNA Doe Project. We assess the Proposed Strategy using simulated versions of the 17 DNA Doe Project cases, and compare it to a Benchmark Strategy that ranks matches by their genetic distance to the target and only descends from known common ancestors between a pair of matches. The Proposed Strategy solves cases 25-fold faster than the Benchmark Strategy, and does so by aggressively descending from a set of potential most recent common ancestors between the target and a match even when this set has a low probability of containing the correct most recent common ancestor. This work has been used to solve several stalled cold cases.\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EBIO:\u0026nbsp;\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003ELawrence M. Wein is the Jeffrey S. Skoll Professor of Management Science at the Graduate School of Business, Stanford University. He received a Ph.D. in Operations Research at Stanford University in 1988 and was a professor at MIT\u0027s Sloan School of Management from 1988 to 2002. His research interests are in operations management and public health. He was Editor-in-Chief of Operations Research from 2000 to 2005. He has been awarded a Presidential Young Investigator Award, the Erlang Prize, the Koopman Prize, the INFORMS Expository Writing Award, the Philip McCord Morse Lectureship, the INFORMS President\u2019s Award, the Frederick W. Lanchester Prize, the George E. Kimball Medal, a best paper award from Risk Analysis, and two notable paper awards from the Journal of Forensic Sciences. He is an INFORMS Fellow, a M\u0026amp;SOM Fellow and a member of the National Academy of Engineering.\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EThe genealogy process is typically the most time-consuming part of -- and a limiting factor in the success of -- forensic investigative genetic genealogy, which is a new approach to solving violent crimes and identifying human remains. We formulate a stochastic dynamic program that -- given the list of matches and their genetic distances to the unknown target -- chooses the best decision at each point in time: which match to investigate (i.e., find its ancestors), which ancestors of these matches to descend from (i.e., find its descendants), or whether to terminate the investigation. The objective is to maximize the probability of finding the target minus a cost on the expected size of the final family tree. We estimate the parameters of our model using data from 17 cases (eight solved, nine unsolved) from the DNA Doe Project. We assess the Proposed Strategy using simulated versions of the 17 DNA Doe Project cases, and compare it to a Benchmark Strategy that ranks matches by their genetic distance to the target and only descends from known common ancestors between a pair of matches. The Proposed Strategy solves cases 25-fold faster than the Benchmark Strategy, and does so by aggressively descending from a set of potential most recent common ancestors between the target and a match even when this set has a low probability of containing the correct most recent common ancestor. This work has been used to solve several stalled cold cases.\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Analysis of the Genealogy Process in Forensic Investigative Genetic Genealogy"}],"uid":"36374","created_gmt":"2025-03-19 19:07:28","changed_gmt":"2025-03-19 19:11:39","author":"mwelch39","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2025-03-28T11:30:00-04:00","event_time_end":"2025-03-28T12:30:00-04:00","event_time_end_last":"2025-03-28T12:30:00-04:00","gmt_time_start":"2025-03-28 15:30:00","gmt_time_end":"2025-03-28 16:30:00","gmt_time_end_last":"2025-03-28 16:30:00","rrule":null,"timezone":"America\/New_York"},"location":" ISYE Groseclose 402","extras":[],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"177814","name":"Postdoc"},{"id":"78771","name":"Public"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"681181":{"#nid":"681181","#data":{"type":"event","title":"ISYE Statistics Seminar - Hongquan Xu","body":[{"value":"\u003Cdiv\u003ETitle: Minimum aberration-type criterion and stratification pattern enumerator for selecting space-filling designs\u003C\/div\u003E\u003Cdiv\u003E\u0026nbsp;\u003C\/div\u003E\u003Cdiv\u003ESpeaker: Hongquan Xu, Department of Statistics and Data Science, University of California, Los Angeles\u003C\/div\u003E\u003Cdiv\u003E\u0026nbsp;\u003C\/div\u003E\u003Cdiv\u003EAbstract: \u0026nbsp;Space-filling designs are widely used in computer experiments. Inspired by the celebrated minimum aberration criterion for fractional factorial designs, we propose a minimum aberration-type criterion for assessing the space-filling properties of a broad class of designs including Latin hypercube designs, orthogonal arrays and strong orthogonal arrays. The new minimum aberration-type criterion covers the minimum aberration criterion and various generalizations as special cases. The generality of the new criterion comes with a huge computational cost. \u0026nbsp;The fast computation of the (generalized) minimum aberration criterion is facilitated through the famous MacWilliams identities -- a fundamental result in coding theory. There are no parallel results to handle complex design problems with stratifications. To address the computational issue, we introduce the concept of stratification pattern enumerator and show that the stratification pattern enumerator is a linear combination of the space-filling pattern. The stratification pattern enumerator is more general than the MacWilliams identities, and it can be used to compute the space-filling or stratification pattern in quadratic times, instead of exponential times by definition. \u0026nbsp;In addition, \u0026nbsp;we establish a lower bound \u0026nbsp;for the stratification pattern enumerator and present construction methods for designs that achieve the lower bound using multiplication tables over Galois fields. The constructed designs have good space-filling properties in low-dimensional projections and are robust under various criteria.\u0026nbsp;\u003C\/div\u003E\u003Cdiv\u003E\u0026nbsp;\u003C\/div\u003E\u003Cdiv\u003EReferences:\u0026nbsp;\u003C\/div\u003E\u003Cdiv\u003ETian, Y. and Xu, H. \u0026nbsp;(2022). A Minimum Aberration-Type Criterion for Selecting Space-Filling Designs. Biometrika,109(2), 489-501.\u003C\/div\u003E\u003Cdiv\u003ETian, Y. and Xu, H. (2024). Stratification Pattern Enumerator and its Applications. Journal of the Royal Statistical Society Series B: Statistical Methodology, 86(2), 364-385.\u003C\/div\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EAbstract: \u0026nbsp;Space-filling designs are widely used in computer experiments. Inspired by the celebrated minimum aberration criterion for fractional factorial designs, we propose a minimum aberration-type criterion for assessing the space-filling properties of a broad class of designs including Latin hypercube designs, orthogonal arrays and strong orthogonal arrays. The new minimum aberration-type criterion covers the minimum aberration criterion and various generalizations as special cases. The generality of the new criterion comes with a huge computational cost. \u0026nbsp;The fast computation of the (generalized) minimum aberration criterion is facilitated through the famous MacWilliams identities -- a fundamental result in coding theory. There are no parallel results to handle complex design problems with stratifications. To address the computational issue, we introduce the concept of stratification pattern enumerator and show that the stratification pattern enumerator is a linear combination of the space-filling pattern. The stratification pattern enumerator is more general than the MacWilliams identities, and it can be used to compute the space-filling or stratification pattern in quadratic times, instead of exponential times by definition. \u0026nbsp;In addition, \u0026nbsp;we establish a lower bound \u0026nbsp;for the stratification pattern enumerator and present construction methods for designs that achieve the lower bound using multiplication tables over Galois fields. The constructed designs have good space-filling properties in low-dimensional projections and are robust under various criteria.\u0026nbsp;\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Title: Minimum aberration-type criterion and stratification pattern enumerator for selecting space-filling designs"}],"uid":"36433","created_gmt":"2025-03-17 18:30:24","changed_gmt":"2025-03-17 18:33:09","author":"mrussell89","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2025-03-25T11:00:00-04:00","event_time_end":"2025-03-25T12:00:00-04:00","event_time_end_last":"2025-03-25T12:00:00-04:00","gmt_time_start":"2025-03-25 15:00:00","gmt_time_end":"2025-03-25 16:00:00","gmt_time_end_last":"2025-03-25 16:00:00","rrule":null,"timezone":"America\/New_York"},"location":"ISYE Main 228","extras":[],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"177814","name":"Postdoc"},{"id":"78771","name":"Public"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"681023":{"#nid":"681023","#data":{"type":"event","title":"ISyE Seminar - Michael Kosorok","body":[{"value":"\u003Cp\u003ETitle:\u0026nbsp;\u003C\/p\u003E\u003Cp\u003ELinear regression using Hilbert-space valued covariates with unknown reproducing kernel\u003C\/p\u003E\u003Cp\u003EAbstract:\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;In this talk we present a new method of linear regression using Hilbert-space valued covariates with unknown reproducing kernels. We develop a computationally efficient approach to estimation and derive asymptotic theory for the regression parameter estimates under mild assumptions. We demonstrate the approach in simulation studies as well as in a data analyses using two- and three-dimensional brain images as predictors. The is work is a collaboration with Xinyi Li and Margaret Hoch.\u003C\/p\u003E\u003Cp\u003EBio:\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EMichael R. Kosorok, PhD, is the W.R. Kenan, Jr. Distinguished Professor of Biostatistics, Professor of Statistics and Operations Research, and Director of the Center for Artificial Intelligence and Public Health at the University of North Carolina at Chapel Hill. His interests include biostatistics, artificial intelligence, empirical processes, and precision health. He is a fellow of the ASA, IMS and AAAS, and is past-president of IMS.\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EIn this talk we present a new method of linear regression using Hilbert-space valued covariates with unknown reproducing kernels. We develop a computationally efficient approach to estimation and derive asymptotic theory for the regression parameter estimates under mild assumptions. We demonstrate the approach in simulation studies as well as in a data analyses using two- and three-dimensional brain images as predictors. The is work is a collaboration with Xinyi Li and Margaret Hoch.\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Linear regression using Hilbert-space valued covariates with unknown reproducing kernel"}],"uid":"36374","created_gmt":"2025-03-07 20:36:09","changed_gmt":"2025-03-07 20:36:09","author":"mwelch39","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2025-03-14T11:30:00-04:00","event_time_end":"2025-03-14T12:30:00-04:00","event_time_end_last":"2025-03-14T12:30:00-04:00","gmt_time_start":"2025-03-14 15:30:00","gmt_time_end":"2025-03-14 16:30:00","gmt_time_end_last":"2025-03-14 16:30:00","rrule":null,"timezone":"America\/New_York"},"location":" ISYE Groseclose 402","extras":[],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"177814","name":"Postdoc"},{"id":"78771","name":"Public"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"680734":{"#nid":"680734","#data":{"type":"event","title":"SCL IRC Seminar: \u0022GenAI and\/or Optimization: Present and Future\u0022","body":[{"value":"\u003Cp\u003EThe \u003Ca href=\u0022https:\/\/www.scl.gatech.edu\u0022\u003ESupply Chain and Logistics Institute\u003C\/a\u003E and \u003Ca href=\u0022https:\/\/www.scl.gatech.edu\/outreach\/SCLO\u0022\u003ESupply Chain \u0026amp; Logistics Organization\u003C\/a\u003E Student Group is co-hosting a special event seminar featuring Ishai Menache from Microsoft.\u003C\/p\u003E\u003Ch2\u003EGenAI and\/or Optimization: Present and Future\u0026nbsp;\u003C\/h2\u003E\u003Ch3\u003Efeaturing Ishai Menache from Microsoft\u003C\/h3\u003E\u003Cp\u003E\u003Cstrong\u003EMarch 27, 2025\u0026nbsp;\u003C\/strong\u003E\u003Cbr\u003E12pm ET | Complimentary Lunch (first 75 attendees)\u003Cbr\u003E\u003Cstrong\u003E1-2pm ET | Seminar\u003C\/strong\u003E\u003Cbr\u003ECallaway Manufacturing Research Building (Auditorium 101)\u003Cbr\u003E\u0026nbsp;\u003C\/p\u003E\u003Ch3\u003ESESSION OVERVIEW\u003C\/h3\u003E\u003Cp\u003EIshai Menache will describe how LLMs and optimization techniques are leveraged in Microsoft\u2019s cloud supply chain to improve efficiency, provide insights to planners, and answer \u0022what-if\u0022 questions. More broadly, Menache will discuss ongoing research and future directions in the intersection of operations research and AI.\u003C\/p\u003E\u003Ch3\u003ESESSION SPEAKER\u003C\/h3\u003E\u003Cp\u003EIshai Menache is a Partner Researcher \u0026amp; Manager of the Machine Learning and Optimization group at Microsoft Research. With a PhD in Electrical Engineering from the Technion and postdoctoral experience at MIT, he has been with Microsoft since 2011. His research focuses on developing large-scale optimization frameworks that leverage machine learning and Generative AI technologies to improve supply chain operations.\u003C\/p\u003E\u003Ch3\u003ESESSION LOCATION\u003C\/h3\u003E\u003Cp\u003ECallaway Manufacturing Research Center (MARC)\u003Cbr\u003EMain Auditorium (1st Floor)\u003Cbr\u003EGeorgia Institute of Technology\u003Cbr\u003E813 Ferst Drive NW\u003Cbr\u003EAtlanta, GA 30332\u003C\/p\u003E\u003Cp\u003EGPS Coordinates: 33.777873, -84.401353\u003C\/p\u003E\u003Cp\u003E\u003Ca href=\u0022https:\/\/www.google.com\/maps\/place\/Callaway+Manufacturing+Research+Center+Building,+813+Ferst+Dr+NW,+Atlanta,+GA+30332\/@33.777616,-84.403925,17z\/data=!3m1!4b1!4m14!1m7!3m6!1s0x88f5048b89758177:0x537652470f144db0!2sGeorgia+Tech+Manufacturing+Institute!8m2!3d33.777968!4d-84.4013451!16s%2Fg%2F11b6v54690!3m5!1s0x88f5048b897494cb:0xc5afa41e74f3bc81!8m2!3d33.7776116!4d-84.4013501!16s%2Fg%2F12hpmc353?entry=ttu\u0026amp;g_ep=EgoyMDI1MDIyMy4xIKXMDSoJLDEwMjExNDUzSAFQAw%3D%3D\u0022\u003EView via Google Maps\u003C\/a\u003E\u003C\/p\u003E\u003Cp\u003E\u003Cem\u003E\u003Cstrong\u003EPlease note that we will be serving a free box lunch from 12-1pm (for the first 75 attendees) outside of the auditorium before the lecture at 1pm ET.\u003C\/strong\u003E\u003C\/em\u003E\u003C\/p\u003E\u003Ch3\u003E\u003Ca href=\u0022https:\/\/eforms.scl.gatech.edu\/mar25seminar\u0022\u003E\u003Cstrong\u003ERegister Online for our Special Event Seminar\u003C\/strong\u003E\u003C\/a\u003E\u003C\/h3\u003E\u003Cp\u003E\u003Cbr\u003EFor questions, please email \u003Ca href=\u0022mailto:event@scl.gatech.edu\u0022\u003Eevent@scl.gatech.edu\u003C\/a\u003E.\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EThe \u003Ca href=\u0022https:\/\/www.scl.gatech.edu\u0022\u003ESupply Chain and Logistics Institute\u003C\/a\u003E and \u003Ca href=\u0022https:\/\/www.scl.gatech.edu\/outreach\/SCLO\u0022\u003ESupply Chain \u0026amp; Logistics Organization\u003C\/a\u003E Student Group is co-hosting a special event seminar featuring Ishai Menache from Microsoft.\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Join the Supply Chain and Logistics Institute for a special event seminar featuring Ishai Menache from Microsoft."}],"uid":"27233","created_gmt":"2025-02-26 15:52:34","changed_gmt":"2025-03-03 13:01:29","author":"Andy Haleblian","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2025-03-27T12:00:00-04:00","event_time_end":"2025-03-27T14:00:00-04:00","event_time_end_last":"2025-03-27T14:00:00-04:00","gmt_time_start":"2025-03-27 16:00:00","gmt_time_end":"2025-03-27 18:00:00","gmt_time_end_last":"2025-03-27 18:00:00","rrule":null,"timezone":"America\/New_York"},"location":"Callaway Manufacturing Research Center (MARC) Main Auditorium (1st Floor)","extras":["free_food"],"hg_media":{"676433":{"id":"676433","type":"image","title":"GT-SCLIRC_Seminar_IshaiMenache_20250327.jpg","body":null,"created":"1740771430","gmt_created":"2025-02-28 19:37:10","changed":"1741006110","gmt_changed":"2025-03-03 12:48:30","alt":"SCL IRC Seminar: GenAI and\/or Optimization: Present and Future","file":{"fid":"260224","name":"GT-SCLIRC_Seminar_IshaiMenache_20250327.jpg","image_path":"\/sites\/default\/files\/2025\/03\/03\/GT-SCLIRC_Seminar_IshaiMenache_20250327.jpg","image_full_path":"http:\/\/hg.gatech.edu\/\/sites\/default\/files\/2025\/03\/03\/GT-SCLIRC_Seminar_IshaiMenache_20250327.jpg","mime":"image\/jpeg","size":470040,"path_740":"http:\/\/hg.gatech.edu\/sites\/default\/files\/styles\/740xx_scale\/public\/2025\/03\/03\/GT-SCLIRC_Seminar_IshaiMenache_20250327.jpg?itok=Sqw7XV7-"}}},"media_ids":["676433"],"related_links":[{"url":"https:\/\/eforms.scl.gatech.edu\/mar25seminar","title":"Register Online for our Special Event SCL IRC seminar"},{"url":"https:\/\/www.scl.gatech.edu\/sites\/default\/files\/downloads\/sclirc\/GT-SCLIRC_Seminar_IshaiMenache_20250327.pdf","title":"Download the event flyer"}],"groups":[{"id":"1250","name":"Center for Health and Humanitarian Systems (CHHS)"},{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"},{"id":"1243","name":"The Supply Chain and Logistics Institute (SCL)"}],"categories":[],"keywords":[{"id":"167074","name":"Supply Chain"},{"id":"233","name":"Logistics"},{"id":"122741","name":"physical internet"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"78771","name":"Public"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[{"value":"\u003Cp\u003Eevent@scl.gatech.edu\u003C\/p\u003E","format":"limited_html"}],"email":[],"slides":[],"orientation":[],"userdata":""}},"680487":{"#nid":"680487","#data":{"type":"event","title":"Celebrating Women in Industrial \u0026 Systems Engineering: Kickoff Breakfast Panel","body":[{"value":"\u003Cp\u003E\ud83c\udf1f \u003Cstrong\u003ECelebrating Women in Industrial \u0026amp; Systems Engineering\u003C\/strong\u003E! \ud83c\udf1f\u003Cbr\u003EThis March, the \u003Ca href=\u0022https:\/\/www.linkedin.com\/company\/georgiatechisye\/\u0022 target=\u0022_self\u0022\u003EGeorgia Tech H. Milton Stewart School of ISyE\u003C\/a\u003E \u0026nbsp;Center for Academics, Success, and Engagement (CASE) is proud to honor the incredible women in Industrial \u0026amp; Systems Engineering with a month full of inspiring events.\u003Cbr\u003E\u003Cbr\u003E\ud83c\udf89\u003Cstrong\u003EKickoff Breakfast Panel\u003C\/strong\u003E\u003Cbr\u003E\ud83d\udcc5 Friday, February 28 | \ud83d\udd58 9:00 AM - 11:00 AM\u003Cbr\u003E\ud83d\udccd GT Alumni House\u003Cbr\u003E\u003Cbr\u003EJoin us as we launch this celebration with an alumnae panel featuring:\u003Cbr\u003E\ud83d\udd39 Melody Mulaik, President at Revenue Cycle Coding Strategies LLC\u0026nbsp;\u003Cbr\u003E\u0026nbsp;\ud83d\udd39 Eleana L. Paddilla Acosta, Senior Product Manager at Oracle\u003Cbr\u003E\ud83d\udd39 Sheereen Brown, CEO of Between and Senior Business Analyst at The Task Force Global Health, Inc.\u003Cbr\u003E\ud83d\udd39 Aparajita Satapathy, Project Manager at Lockheed Martin\u003Cbr\u003E\ud83d\udd39 Danielle Donehew, \u0026nbsp;Executive Director at Women\u0027s Basketball Coaches Association\u0026nbsp;\u003Cbr\u003E\ud83d\udd39 Niv Persaud, Managing Director at Transition Planning \u0026amp; Guidance, LLC\u003Cbr\u003E\ud83d\udd39 Moderator: Dr. Dima Nazzal, Director, Academic Faculty at the Georgia Institute of Technology\u003Cbr\u003E\u003Cbr\u003E\u003Ca href=\u0022https:\/\/eforms.isye.gatech.edu\/form\/case-rsvp\u0022\u003ERSVP for the breakfast\u003C\/a\u003E.\u003C\/p\u003E\u003Cp\u003E\u003Cbr\u003E\ud83d\udcbc \u0022\u003Cstrong\u003EWomen in Consulting\u0022 Panel (PwC ISyE Alumnae)\u003C\/strong\u003E\u003Cbr\u003E\ud83d\udcc5 Tuesday, March 11 | \ud83d\udd5a 11:00 AM - 12:00 PM\u003Cbr\u003E\ud83d\udccd ISyE Main Atrium\u003Cbr\u003E\ud83d\udd39Presented by:\u0026nbsp;PwC ISyE Alumnae\u0026nbsp;\u003Cbr\u003E\u003Cbr\u003E\u003Ca href=\u0022https:\/\/eforms.isye.gatech.edu\/form\/case-rsvp?check_logged_in=1\u0022\u003ERSVP for the PwC seminar\u003C\/a\u003E.\u003C\/p\u003E\u003Cp\u003E\u003Cbr\u003E\ud83c\udfa2\u003Cstrong\u003E\u0022My Crazy IE Career Rollercoaster: Pivoting is What We Do?\u0022\u003C\/strong\u003E\u003Cbr\u003E\ud83d\udcc5 Tuesday, March 25 | \ud83d\udd5a 11:00 AM - 12:00 PM\u003Cbr\u003E\ud83d\udccd ISyE Main Atrium\u003Cbr\u003E\ud83d\udd39Presented by Dr. Fay Cobb Payton, Special Advisor on Innovation and Professor, Mathematics and Computer Science at Rutgers University and Partner Consultant\u003Cbr\u003E\u003Cbr\u003E\u003Ca href=\u0022https:\/\/eforms.isye.gatech.edu\/form\/case-wsie-seminar2\u0022\u003ERSVP for Fay Cobb Payton\u2019s seminar\u003C\/a\u003E.\u003C\/p\u003E\u003Cp\u003EThese events provide a fantastic opportunity to learn from and celebrate the achievements of women in ISyE. Mark your calendars and join us!\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EThis March, the Georgia Tech H. Milton Stewart School of ISyE \u0026nbsp;Center for Academics, Success, and Engagement (CASE) is proud to honor the incredible women in Industrial \u0026amp; Systems Engineering with a month full of inspiring events.\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"This March, the Georgia Tech H. Milton Stewart School of ISyE  Center for Academics, Success, and Engagement (CASE) is proud to honor the incredible women in Industrial \u0026 Systems Engineering with a month full of inspiring events."}],"uid":"36600","created_gmt":"2025-02-14 15:20:26","changed_gmt":"2025-02-25 19:20:41","author":"cmullins32","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2025-02-28T09:00:00-05:00","event_time_end":"2025-02-28T11:00:00-05:00","event_time_end_last":"2025-02-28T11:00:00-05:00","gmt_time_start":"2025-02-28 14:00:00","gmt_time_end":"2025-02-28 16:00:00","gmt_time_end_last":"2025-02-28 16:00:00","rrule":null,"timezone":"America\/New_York"},"location":"Georgia Tech Alumni House","extras":["free_food"],"groups":[{"id":"660354","name":"Center for Academics, Success, and Equity"},{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[],"invited_audience":[],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[{"value":"\u003Cp\u003ENicoly Myles, \u003Ca href=\u0022mailto:nicoly.myles@gatech.edu\u0022\u003Enicoly.myles@gatech.edu\u003C\/a\u003E\u003C\/p\u003E\u003Cp\u003ECarol English, \u003Ca href=\u0022mailto:carol.english@isye.gatech.edu\u0022\u003Ecarol.english@isye.gatech.edu\u003C\/a\u003E\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E","format":"limited_html"}],"email":[],"slides":[],"orientation":[],"userdata":""}},"680466":{"#nid":"680466","#data":{"type":"event","title":"SCL Lunch and Learn: \u201cSucceeding in the Modern Supply Chain\u0022","body":[{"value":"\u003Cp\u003E\u003Cstrong\u003EJoin us for a dynamic webinar exploring the challenges and opportunities of today\u2019s fast-evolving supply chains.\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EThursday, April 3, 2025 | 12-1pm ET\u003C\/strong\u003E\u003C\/p\u003E\u003Cdiv\u003E\u003Cdiv\u003E\u003Cdiv\u003E\u003Cdiv\u003E\u003Cdiv\u003E\u003Cdiv\u003ELearn why traditional decision-making tools no longer suffice in an era of constant disruption and uncertainty\u2014and discover the critical mindsets, strategies, and technologies shaping the future of supply chain management.\u003C\/div\u003E\u003C\/div\u003E\u003C\/div\u003E\u003C\/div\u003E\u003C\/div\u003E\u003Cdiv\u003E\u0026nbsp;\u003C\/div\u003E\u003C\/div\u003E\u003Cp\u003E\u003Ca href=\u0022https:\/\/www.scl.gatech.edu\/apr25-lnl\u0022\u003E\u003Cstrong\u003ERegister Online to Attend\u003C\/strong\u003E\u003C\/a\u003E\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EJoin us for a dynamic webinar exploring the challenges and opportunities of today\u2019s fast-evolving supply chains.\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Join us for a dynamic webinar exploring the challenges and opportunities of today\u2019s fast-evolving supply chains."}],"uid":"27233","created_gmt":"2025-02-13 18:23:27","changed_gmt":"2025-02-25 13:39:55","author":"Andy Haleblian","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2025-04-03T12:00:00-04:00","event_time_end":"2025-04-03T13:00:00-04:00","event_time_end_last":"2025-04-03T13:00:00-04:00","gmt_time_start":"2025-04-03 16:00:00","gmt_time_end":"2025-04-03 17:00:00","gmt_time_end_last":"2025-04-03 17:00:00","rrule":null,"timezone":"America\/New_York"},"location":"Online via Zoom","extras":[],"hg_media":{"676284":{"id":"676284","type":"image","title":"Succeeding in the Modern Supply Chain webinar","body":null,"created":"1739471371","gmt_created":"2025-02-13 18:29:31","changed":"1739471371","gmt_changed":"2025-02-13 18:29:31","alt":"Succeeding in the Modern Supply Chain webinar","file":{"fid":"260038","name":"MSCO.png","image_path":"\/sites\/default\/files\/2025\/02\/13\/MSCO.png","image_full_path":"http:\/\/hg.gatech.edu\/\/sites\/default\/files\/2025\/02\/13\/MSCO.png","mime":"image\/png","size":156221,"path_740":"http:\/\/hg.gatech.edu\/sites\/default\/files\/styles\/740xx_scale\/public\/2025\/02\/13\/MSCO.png?itok=2Cmo1Wp3"}}},"media_ids":["676284"],"related_links":[{"url":"https:\/\/www.scl.gatech.edu\/apr25-lnl","title":"Register Online to Attend the Webinar"},{"url":"https:\/\/www.scl.gatech.edu\/education\/professional-education","title":"Professional Education at the Georgia Tech Supply Chain and Logistics Institute"}],"groups":[{"id":"1250","name":"Center for Health and Humanitarian Systems (CHHS)"},{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"},{"id":"1243","name":"The Supply Chain and Logistics Institute (SCL)"}],"categories":[],"keywords":[{"id":"3615","name":"information session"},{"id":"186857","name":"go-gtmi"},{"id":"122741","name":"physical internet"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"10377","name":"Career\/Professional development"}],"invited_audience":[{"id":"78771","name":"Public"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[{"value":"\u003Cp\u003Ecourse@scl.gatech.edu\u003C\/p\u003E","format":"limited_html"}],"email":[],"slides":[],"orientation":[],"userdata":""}},"680645":{"#nid":"680645","#data":{"type":"event","title":"ISYE Statistics Seminar - Richard Samworth","body":[{"value":"\u003Cblockquote\u003E\u003Cdiv\u003E\u003Cdiv\u003E\u003Cdiv\u003ETitle: How should we do linear regression?\u003C\/div\u003E\u003Cdiv\u003EAbstract: In the context of linear regression, we construct a data-driven convex loss function with respect to which empirical risk minimisation yields optimal asymptotic variance in the downstream estimation of the regression coefficients. Our semiparametric approach targets the best decreasing approximation of the derivative of the log-density of the noise distribution. At the population level, this fitting process is a nonparametric extension of score matching, corresponding to a log-concave projection of the noise distribution with respect to the Fisher divergence. The procedure is computationally efficient, and we prove that our procedure attains the minimal asymptotic covariance among all convex M-estimators. As an example of a non-log-concave setting, for Cauchy errors, the optimal convex loss function is Huber-like, and our procedure yields an asymptotic efficiency greater than 0.87 relative to the oracle maximum likelihood estimator of the regression coefficients that uses knowledge of this error distribution; in this sense, we obtain robustness without sacrificing much efficiency.\u0026nbsp;\u003C\/div\u003E\u003C\/div\u003E\u003C\/div\u003E\u003C\/blockquote\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cblockquote\u003E\u003Cdiv\u003E\u003Cdiv\u003E\u003Cdiv\u003E\u003Cdiv\u003EAbstract: In the context of linear regression, we construct a data-driven convex loss fuAbstract: In the context of linear regression, we construct a data-driven convex loss function with respect to which empirical risk minimisation yields optimal asymptotic variance in the downstream estimation of the regression coefficients. Our semiparametric approach targets the best decreasing approximation of the derivative of the log-density of the noise distribution. At the population level, this fitting process is a nonparametric extension of score matching, corresponding to a log-concave projection of the noise distribution with respect to the Fisher divergence. The procedure is computationally efficient, and we prove that our procedure attains the minimal asymptotic covariance among all convex M-estimators. As an example of a non-log-concave setting, for Cauchy errors, the optimal convex loss function is Huber-like, and our procedure yields an asymptotic efficiency greater than 0.87 relative to the oracle maximum likelihood estimator of the regression coefficients that uses knowledge of this error distribution; in this sense, we obtain robustness without sacrificing much efficiency.\u0026nbsp;\u003Cbr\u003Enction with respect to which empirical risk minimisation yields optimal asymptotic variance in the downstream estimation of the regression coefficients. Our semiparametric approach targets the best decreasing approximation of the derivative of the log-density of the noise distribution. At the population level, this fitting process is a nonparametric extension of score matching, corresponding to a log-concave projection of the noise distribution with respect to the Fisher divergence. The procedure is computationally efficient, and we prove that our procedure attains the minimal asymptotic covariance among all convex M-estimators. As an example of a non-log-concave setting, for Cauchy errors, the optimal convex loss function is Huber-like, and our procedure yields an asymptotic efficiency greater than 0.87 relative to the oracle maximum likelihood estimator of the regression coefficients that uses knowledge of this error distribution; in this sense, we obtain robustness without sacrificing much efficiency.\u0026nbsp;\u003C\/div\u003E\u003Cdiv\u003E\u0026nbsp;\u003C\/div\u003E\u003C\/div\u003E\u003C\/div\u003E\u003C\/div\u003E\u003C\/blockquote\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Title: How should we do linear regression?"}],"uid":"36433","created_gmt":"2025-02-21 13:14:58","changed_gmt":"2025-02-21 13:17:04","author":"mrussell89","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2025-04-22T11:00:00-04:00","event_time_end":"2025-04-22T12:00:00-04:00","event_time_end_last":"2025-04-22T12:00:00-04:00","gmt_time_start":"2025-04-22 15:00:00","gmt_time_end":"2025-04-22 16:00:00","gmt_time_end_last":"2025-04-22 16:00:00","rrule":null,"timezone":"America\/New_York"},"location":"Groseclose 402","extras":[],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"177814","name":"Postdoc"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"677095":{"#nid":"677095","#data":{"type":"event","title":"ISyE Seminar - Jeff Hong","body":[{"value":"\u003Cp\u003ETitle:\u003C\/p\u003E\u003Cp\u003EThe (Surprising) Rate Optimality of Greedy Procedures for Large-Scale Ranking and Selection\u003C\/p\u003E\u003Cp\u003EAbstract:\u003C\/p\u003E\u003Cp\u003ERanking and selection (R\u0026amp;S) aims to select the best alternative with the largest mean performance from a finite set of alternatives. Recently, considerable attention has turned toward the large-scale R\u0026amp;S problem which involves a large number of alternatives. Ideal large-scale R\u0026amp;S procedures should be sample optimal; that is, the total sample size required to deliver an asymptotically nonzero probability of correct selection (PCS) grows at the minimal order (linear order) in the number of alternatives, k. Surprisingly, we discover that the na\u00efve greedy procedure, which keeps sampling the alternative with the largest running average, performs strikingly well and appears sample optimal. To understand this discovery, we develop a new boundary-crossing perspective and prove that the greedy procedure is sample optimal for the scenarios where the best mean maintains at least a positive constant away from all other means as k increases. We further show that the derived PCS lower bound is asymptotically tight for the slippage configuration of means with a common variance. For other scenarios, we consider the probability of good selection and find that the result depends on the growth behavior of the number of good alternatives: if it remains bounded as k increases, the sample optimality still holds; otherwise, the result may change. Moreover, we propose the explore-first greedy procedures by adding an exploration phase to the greedy procedure. The procedures are proven to be sample optimal and consistent under the same assumptions. Last, we numerically investigate the performance of our greedy procedures in solving large-scale R\u0026amp;S problems.\u003C\/p\u003E\u003Cp\u003EThis is a joint work with Zaile Li at INSEAD and Weiwei Fan at Tongji University. The paper is available at https:\/\/doi.org\/10.1287\/mnsc.2023.00694.\u003C\/p\u003E\u003Cp\u003EBio:\u003C\/p\u003E\u003Cp\u003EJeff Hong received his bachelor\u2019s degree from Tsinghua University and his Ph.D. from Northwestern University. He is currently a professor in the Department of Industrial and Systems Engineering at the University of Minnesota, Twin Cities. Previously, he held faculty positions at Fudan University, the City University of Hong Kong, and the Hong Kong University of Science and Technology. His research interests include stochastic simulation, stochastic optimization, risk management, and supply chain management. Jeff is the Simulation Department Editor of Naval Research Logistics and an Associate Editor for Management Science and ACM Transactions on Modeling and Computer Simulation. He served as the Simulation Area Editor for Operations Research from 2018 to 2023, and as President of the INFORMS Simulation Society from 2020 to 2022.\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003ERanking and selection (R\u0026amp;S) aims to select the best alternative with the largest mean performance from a finite set of alternatives. Recently, considerable attention has turned toward the large-scale R\u0026amp;S problem which involves a large number of alternatives. Ideal large-scale R\u0026amp;S procedures should be sample optimal; that is, the total sample size required to deliver an asymptotically nonzero probability of correct selection (PCS) grows at the minimal order (linear order) in the number of alternatives, k. Surprisingly, we discover that the na\u00efve greedy procedure, which keeps sampling the alternative with the largest running average, performs strikingly well and appears sample optimal. To understand this discovery, we develop a new boundary-crossing perspective and prove that the greedy procedure is sample optimal for the scenarios where the best mean maintains at least a positive constant away from all other means as k increases. We further show that the derived PCS lower bound is asymptotically tight for the slippage configuration of means with a common variance. For other scenarios, we consider the probability of good selection and find that the result depends on the growth behavior of the number of good alternatives: if it remains bounded as k increases, the sample optimality still holds; otherwise, the result may change. Moreover, we propose the explore-first greedy procedures by adding an exploration phase to the greedy procedure. The procedures are proven to be sample optimal and consistent under the same assumptions. Last, we numerically investigate the performance of our greedy procedures in solving large-scale R\u0026amp;S problems.\u003C\/p\u003E\u003Cp\u003EThis is a joint work with Zaile Li at INSEAD and Weiwei Fan at Tongji University. The paper is available at https:\/\/doi.org\/10.1287\/mnsc.2023.00694.\u003Cbr\u003E\u0026nbsp;\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"The (Surprising) Rate Optimality of Greedy Procedures for Large-Scale Ranking and Selection"}],"uid":"36374","created_gmt":"2024-09-25 15:43:15","changed_gmt":"2025-02-19 13:53:21","author":"mwelch39","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2024-10-04T11:30:00-04:00","event_time_end":"2024-10-04T12:30:00-04:00","event_time_end_last":"2024-10-04T12:30:00-04:00","gmt_time_start":"2024-10-04 15:30:00","gmt_time_end":"2024-10-04 16:30:00","gmt_time_end_last":"2024-10-04 16:30:00","rrule":null,"timezone":"America\/New_York"},"location":" ISYE Groseclose 402","extras":[],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"177814","name":"Postdoc"},{"id":"78771","name":"Public"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"680575":{"#nid":"680575","#data":{"type":"event","title":"ISyE Seminar - Laura Albert","body":[{"value":"\u003Cp\u003ETitle:\u003C\/p\u003E\u003Cp\u003ESmarter decisions for a secure world: opportunities and challenges for industrial engineering\u003C\/p\u003E\u003Cp\u003E\u003Cbr\u003EAbstract:\u003C\/p\u003E\u003Cp\u003EOur nation has undergone substantial transformation in the quarter century since the events of September 11, 2001, an era characterized by the need for security and resilience. During this time, the industrial engineering and operations research (IE\/OR) community rose to this challenge, making remarkable advances by addressing these vital needs. This seminar will explore these critical topics. Advancing IE\/OR through societally relevant applications has been a central theme of Dr. Laura Albert\u2019s academic research career. In this talk, she will discuss recent advances from her research group in the areas of cybersecurity and critical infrastructure protection. The talk will end with an outline of some of the \u201cgrand challenges\u201d in IE\/OR. Prof. Albert will introduce a roadmap for advancing IE\/OR by addressing these challenges and ensuring that IE\/OR remains at the forefront of solving global problems.\u003C\/p\u003E\u003Cp\u003E\u003Cbr\u003EBio:\u003C\/p\u003E\u003Cp\u003ELaura Albert is a Professor of Industrial \u0026amp; Systems Engineering at the University of Wisconsin-Madison, and she was also the 2023 President of the Institute for Operations Research and the Management Sciences (INFORMS). Professor Albert\u2019s research interests are in the field of operations research and analytics with application to homeland security, emergency response, and public sector problems. She has been recognized with the American Association for the Advancement of Science (AAAS) Fellow Award, Institute of Industrial and Systems Engineers (IISE) Fellow Award, the INFORMS Impact Prize, a National Science Foundation CAREER award, and a Fulbright Award. She is also an engineering ambassador who regularly promotes operations research locally and nationally through media appearances and on her blog entitled \u201cPunk Rock Operations Research.\u201d\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EOur nation has undergone substantial transformation in the quarter century since the events of September 11, 2001, an era characterized by the need for security and resilience. During this time, the industrial engineering and operations research (IE\/OR) community rose to this challenge, making remarkable advances by addressing these vital needs. This seminar will explore these critical topics. Advancing IE\/OR through societally relevant applications has been a central theme of Dr. Laura Albert\u2019s academic research career. In this talk, she will discuss recent advances from her research group in the areas of cybersecurity and critical infrastructure protection. The talk will end with an outline of some of the \u201cgrand challenges\u201d in IE\/OR. Prof. Albert will introduce a roadmap for advancing IE\/OR by addressing these challenges and ensuring that IE\/OR remains at the forefront of solving global problems.\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Smarter decisions for a secure world: opportunities and challenges for industrial engineering"}],"uid":"36374","created_gmt":"2025-02-19 13:44:37","changed_gmt":"2025-02-19 13:49:55","author":"mwelch39","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2025-02-28T11:30:00-05:00","event_time_end":"2025-02-28T12:30:00-05:00","event_time_end_last":"2025-02-28T12:30:00-05:00","gmt_time_start":"2025-02-28 16:30:00","gmt_time_end":"2025-02-28 17:30:00","gmt_time_end_last":"2025-02-28 17:30:00","rrule":null,"timezone":"America\/New_York"},"location":" ISYE Groseclose 402","extras":[],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"177814","name":"Postdoc"},{"id":"78771","name":"Public"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"680501":{"#nid":"680501","#data":{"type":"event","title":"ShapiroFest: Legacy of Professor Alexander Shapiro","body":[{"value":"\u003Cdiv\u003E\u003Cp lang=\u0022EN-US\u0022\u003EJoin us in celebrating the incredible legacy of\u0026nbsp;Professor Alexander Shapiro\u0026nbsp;at\u0026nbsp;ShapiroFest! \u0026nbsp;\u003C\/p\u003E\u003C\/div\u003E\u003Cdiv\u003E\u003Cp lang=\u0022EN-US\u0022\u003EThis workshop, dedicated to honoring Professor Shapiro\u0027s 75th birthday, will take place on March 17-18, 2025, at the Georgia Institute of Technology. The event will feature distinguished speakers, research talks on modern stochastic optimization, engaging discussions, and reflections on Professor Shapiro\u0027s transformative influence on stochastic programming and optimization.\u0026nbsp;\u003C\/p\u003E\u003C\/div\u003E\u003Cdiv\u003E\u003Cp lang=\u0022EN-US\u0022\u003EProfessor Shapiro\u0027s pioneering work\u0026nbsp;in stochastic programming has made significant contributions to the theoretical and methodological foundations of the field. His innovations include\u0026nbsp;risk-averse optimization,\u0026nbsp;distributionally robust Markov decision processes,\u0026nbsp;duality theory,\u0026nbsp;perturbation analysis,\u0026nbsp;sample average approximation, and\u0026nbsp;robust stochastic approximation. These advancements have expanded the scope and capabilities of stochastic programming, making it a critical tool in emerging fields such as\u0026nbsp;machine learning\u0026nbsp;and\u0026nbsp;artificial intelligence.\u0026nbsp;\u003C\/p\u003E\u003C\/div\u003E\u003Cdiv\u003E\u003Cp lang=\u0022EN-US\u0022\u003EAs the\u0026nbsp;A. Russell Chandler III Chair and Professor in the\u0026nbsp;H. Milton Stewart School of Industrial and Systems Engineering\u0026nbsp;at\u0026nbsp;Georgia Tech, Professor Shapiro has received numerous prestigious awards, including the\u0026nbsp;Khachiyan Prize, the\u0026nbsp;Dantzig Prize, and the\u0026nbsp;John von Neumann Theory Prize. His work has had a profound impact on the field, and ShapiroFest is a testament to his remarkable contributions.\u0026nbsp;\u003C\/p\u003E\u003C\/div\u003E\u003Cdiv\u003E\u003Cp lang=\u0022EN-US\u0022\u003EWe warmly invite researchers, practitioners, and students across all fields of science and engineering to join us in celebrating this milestone in Professor Shapiro\u0027s illustrious career.\u0026nbsp;\u003C\/p\u003E\u003C\/div\u003E\u003Cdiv\u003E\u003Cp lang=\u0022EN-US\u0022\u003EFor details about the program, speakers, and registration, please visit the \u003Ca href=\u0022https:\/\/sites.gatech.edu\/shapirofest\/)\u0022 rel=\u0022noreferrer noopener\u0022 target=\u0022_blank\u0022\u003Eworkshop website\u003C\/a\u003E. Registration for the workshop is free but required due to limited capacity.\u0026nbsp;\u003C\/p\u003E\u003C\/div\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EWe are thrilled to announce\u0026nbsp;ShapiroFest, a workshop dedicated to honoring the remarkable contributions of\u0026nbsp;Professor Alexander Shapiro\u0026nbsp;to the field of stochastic optimization on the occasion of his 75th birthday. This special event will take place on March 17-18, 2025, at the Georgia Institute of Technology.\u0026nbsp;\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Join us in celebrating the incredible legacy of Professor Alexander Shapiro at ShapiroFest! "}],"uid":"36284","created_gmt":"2025-02-14 18:33:43","changed_gmt":"2025-02-14 19:15:15","author":"chenriquez8","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2025-03-17T08:00:00-04:00","event_time_end":"2025-03-18T20:00:00-04:00","event_time_end_last":"2025-03-18T20:00:00-04:00","gmt_time_start":"2025-03-17 12:00:00","gmt_time_end":"2025-03-19 00:00:00","gmt_time_end_last":"2025-03-19 00:00:00","rrule":null,"timezone":"America\/New_York"},"location":"Georgia Tech ISyE","extras":[],"hg_media":{"676290":{"id":"676290","type":"image","title":"ShapiroFest 2025","body":null,"created":"1739560207","gmt_created":"2025-02-14 19:10:07","changed":"1739560207","gmt_changed":"2025-02-14 19:10:07","alt":"ShapiroFest 2025","file":{"fid":"260045","name":"Copy of CASE_WISyE Template-2.png","image_path":"\/sites\/default\/files\/2025\/02\/14\/Copy%20of%20CASE_WISyE%20Template-2.png","image_full_path":"http:\/\/hg.gatech.edu\/\/sites\/default\/files\/2025\/02\/14\/Copy%20of%20CASE_WISyE%20Template-2.png","mime":"image\/png","size":3609081,"path_740":"http:\/\/hg.gatech.edu\/sites\/default\/files\/styles\/740xx_scale\/public\/2025\/02\/14\/Copy%20of%20CASE_WISyE%20Template-2.png?itok=nvSgYofV"}}},"media_ids":["676290"],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"},{"id":"1243","name":"The Supply Chain and Logistics Institute (SCL)"}],"categories":[],"keywords":[{"id":"167738","name":"stochastic optimization"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"10377","name":"Career\/Professional development"},{"id":"1795","name":"Seminar\/Lecture\/Colloquium"},{"id":"26411","name":"Training\/Workshop"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"177814","name":"Postdoc"},{"id":"78771","name":"Public"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"680338":{"#nid":"680338","#data":{"type":"event","title":"SCL Course: Generative AI Application for Supply Chain Professionals (Onsite\/In-Person or Virtual\/Instructor-led)","body":[{"value":"\u003Ch3\u003E\u003Cstrong\u003ECourse Description\u003C\/strong\u003E\u003C\/h3\u003E\u003Cp\u003EThis course provides a deep dive into the ways in which artificial intelligence (AI) optimizes supply chain efficiency. Participants will explore generative AI fundamentals, prompt engineering, and practical applications such as automated inventory systems, predictive maintenance, and route optimization. The course also covers ethical AI use, good and bad use of generative AI (GenAI), and rapidly emerging use cases. By the end, professionals will be skilled in applying AI to enhance supply chain processes and drive success in their organizations.\u003C\/p\u003E\u003Ch3\u003E\u003Cstrong\u003EWho Should Attend\u003C\/strong\u003E\u003C\/h3\u003E\u003Cp\u003EThis course targets supply chain managers, data analysts, logistics professionals, procurement specialists, and business leaders aiming to harness GenAI for enhanced supply chain operations. It is ideal for those interested in GenAI-driven efficiency, strategic insights, and navigation of GenAI\u0027s role in transforming supply chain processes.\u003C\/p\u003E\u003Ch3\u003E\u003Cstrong\u003EHow You Will Benefit\u003C\/strong\u003E\u003C\/h3\u003E\u003Cul\u003E\u003Cli\u003EEnhance decision-making capabilities through GenAI-driven insights to optimize processes and boost efficiency.\u003C\/li\u003E\u003Cli\u003EAcquire practical skills in prompt engineering and the use of generative AI models.\u003C\/li\u003E\u003Cli\u003EExplore practical use cases that can be reapplied.\u003C\/li\u003E\u003Cli\u003ELearn about good and bad use of GenAI for individuals, teams, and organizations.\u003C\/li\u003E\u003Cli\u003EBecome better equipped to effectively harness GenAI capabilities in supply chain activities and planning.\u003C\/li\u003E\u003C\/ul\u003E\u003Ch3\u003E\u003Cstrong\u003EWhat Is Covered\u003C\/strong\u003E\u003C\/h3\u003E\u003Cul\u003E\u003Cli\u003EFoundational understanding of using GenAI in supply chain management\u003C\/li\u003E\u003Cli\u003EBasics of GenAI\u003C\/li\u003E\u003Cli\u003ECrafting effective AI prompts and their applications in optimizing warehouse layouts\u003C\/li\u003E\u003Cli\u003EPredictive maintenance and supplier selection\u003C\/li\u003E\u003Cli\u003EElimination of redundant tasks through AI\u003C\/li\u003E\u003Cli\u003EEthical considerations, risk assessments, and strategy for AI adoption\u003C\/li\u003E\u003Cli\u003EPractical strategies and real-world examples for implementing AI solutions effectively and making informed decisions\u003C\/li\u003E\u003C\/ul\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EParticipants will explore generative AI fundamentals, prompt engineering, and practical applications such as automated inventory systems, predictive maintenance, and route optimization.\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Participants will explore generative AI fundamentals, prompt engineering, and practical applications such as automated inventory systems, predictive maintenance, and route optimization."}],"uid":"27233","created_gmt":"2025-02-07 23:33:46","changed_gmt":"2025-02-07 23:51:14","author":"Andy Haleblian","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2025-10-06T20:00:00-04:00","event_time_end":"2025-10-08T16:00:00-04:00","event_time_end_last":"2025-10-08T16:00:00-04:00","gmt_time_start":"2025-10-07 00:00:00","gmt_time_end":"2025-10-08 20:00:00","gmt_time_end_last":"2025-10-08 20:00:00","rrule":null,"timezone":"America\/New_York"},"location":"Georgia Tech Savannah Campus OR Virtual\/Instructor-led","extras":[],"related_links":[{"url":"https:\/\/www.scl.gatech.edu\/education\/professional-education\/course\/gaiascp","title":"Course webpage within the SCL website"}],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"},{"id":"1243","name":"The Supply Chain and Logistics Institute (SCL)"}],"categories":[],"keywords":[{"id":"192390","name":"generative AI"},{"id":"170001","name":"Supply Chain Engineering"},{"id":"167074","name":"Supply Chain"},{"id":"122741","name":"physical internet"},{"id":"186857","name":"go-gtmi"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"10377","name":"Career\/Professional development"}],"invited_audience":[{"id":"78771","name":"Public"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[{"value":"\u003Cp\u003E\u003Ca href=\u0022mailto:info@scl.gatech.edu\u0022\u003Einfo@scl.gatech.edu\u003C\/a\u003E\u003C\/p\u003E","format":"limited_html"}],"email":[],"slides":[],"orientation":[],"userdata":""}},"680217":{"#nid":"680217","#data":{"type":"event","title":"SCL Course: Modern Supply Chain Overview (Onsite\/In-Person)","body":[{"value":"\u003Ch3\u003E\u003Cstrong\u003ECourse Description\u003C\/strong\u003E\u003C\/h3\u003E\u003Cp\u003EModern Supply Chain Overview (MoSCOw) covers the fundamental areas of supply chain engineering and management along with the ways in which they could evolve over time. An experimental activity in virtual reality is included, which lets course participants apply what they learn and gain concrete implementation experience.\u003C\/p\u003E\u003Ch3\u003E\u003Cstrong\u003EWho Should Attend\u003C\/strong\u003E\u003C\/h3\u003E\u003Cp\u003EThis course is intended for operational supply chain players (in both supply chain engineering and supply chain management) who wish to gain a better understanding of the systemic nature of supply chain ecosystems. This course is also aimed at consultants and advisors who wish to gain a more structured and comprehensive view of modern supply chains.\u003C\/p\u003E\u003Ch3\u003E\u003Cstrong\u003EHow You Will Benefit\u003C\/strong\u003E\u003C\/h3\u003E\u003Cul\u003E\u003Cli\u003EBecome familiar with the main supply chain concepts, features, components, relations, processes and current issues.\u003C\/li\u003E\u003Cli\u003EUnderstand the nature, causes, and options related to the demand, production, and procurement problems faced by supply chain managers and engineers.\u003C\/li\u003E\u003Cli\u003ELearn how to choose, deploy, and use legacy along with incoming methods, models, and techniques to create engineering solutions to these problems.\u003C\/li\u003E\u003Cli\u003ECreate a systemic understanding of supply chain systems, including the handling of scales (local, regional, national, and global) and horizons (operational, tactical, and strategical).\u003C\/li\u003E\u003C\/ul\u003E\u003Ch3\u003E\u003Cstrong\u003EWhat You Will Learn\u003C\/strong\u003E\u003C\/h3\u003E\u003Cul\u003E\u003Cli\u003EStructural and dynamic fundamentals of a supply chain (demand, production, procurement\/purchasing management, and engineering)\u003C\/li\u003E\u003Cli\u003ERisk-driven supply chain concepts and practices\u003C\/li\u003E\u003Cli\u003EData-driven supply chain concepts and practices\u003C\/li\u003E\u003Cli\u003EPerformance-driven supply chain concepts and practices\u003C\/li\u003E\u003Cli\u003EDemand-driven supply chain concepts and practices\u003C\/li\u003E\u003Cli\u003EHyperconnected supply chain concepts and practices\u003C\/li\u003E\u003C\/ul\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EModern Supply Chain Overview (MoSCOw) covers the fundamental areas of supply chain engineering and management along with the ways in which they could evolve over time. An experimental activity in virtual reality is included, which lets course participants apply what they learn and gain concrete implementation experience.\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Gain a systemic understanding of modern supply chain engineering and management, including core concepts, evolving trends, and practical application through a VR experience."}],"uid":"36698","created_gmt":"2025-02-05 17:43:46","changed_gmt":"2025-02-07 23:50:00","author":"dramirez65","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2025-04-21T08:00:00-04:00","event_time_end":"2025-04-23T16:00:00-04:00","event_time_end_last":"2025-04-23T16:00:00-04:00","gmt_time_start":"2025-04-21 12:00:00","gmt_time_end":"2025-04-23 20:00:00","gmt_time_end_last":"2025-04-23 20:00:00","rrule":null,"timezone":"America\/New_York"},"location":"Georgia Tech Global Learning Center","extras":[],"related_links":[{"url":"https:\/\/www.scl.gatech.edu\/education\/professional-education\/course\/msco","title":"Course webpage within the SCL website"}],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"},{"id":"1243","name":"The Supply Chain and Logistics Institute (SCL)"}],"categories":[],"keywords":[{"id":"170001","name":"Supply Chain Engineering"},{"id":"167074","name":"Supply Chain"},{"id":"122741","name":"physical internet"},{"id":"186857","name":"go-gtmi"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"10377","name":"Career\/Professional development"}],"invited_audience":[{"id":"78771","name":"Public"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[{"value":"\u003Cp\u003Einfo@scl.gatech.edu\u003C\/p\u003E","format":"limited_html"}],"email":[],"slides":[],"orientation":[],"userdata":""}},"680340":{"#nid":"680340","#data":{"type":"event","title":"SCL Course: Supply Chain Optimization and Prescriptive Analytics (Virtual\/Instructor-led)","body":[{"value":"\u003Ch3\u003E\u003Cstrong\u003ECourse Description\u003C\/strong\u003E\u003C\/h3\u003E\u003Cp\u003EThis course is the fourth in the 4-course Supply Chain Analytics Professional certificate program. It incorporates learning advanced analytics and mathematical optimization to find solutions for supply chain problems. You\u2019ll learn how to use linear programming, mixed integer programming, and heuristics to conduct prescriptive analytics related to production processes, distribution networks, and routing. The course serves as a capstone for the program by culminating in a hackathon where you\u2019ll design networks, inventory policies, and scenarios and then evaluate the outcomes via simulations.\u003C\/p\u003E\u003Cp\u003EThe online version of the course is comprised of (4) half-day online instructor-led LIVE group webinars and pre-work (e.g. installing and testing software on your computer, testing connectivity with LMS and meeting software, etc.) to be completed before the first day of the course.\u003C\/p\u003E\u003Ch3\u003E\u003Cstrong\u003EWho Should Attend\u003C\/strong\u003E\u003C\/h3\u003E\u003Cp\u003EExperienced business professionals who perform or want to perform analytics to improve their supply chain management processes. They want to tackle strategic goals and to perform leading edge analytics projects that address the full complexity of supply chains.\u003C\/p\u003E\u003Ch3\u003E\u003Cstrong\u003EHow You Will Benefit\u003C\/strong\u003E\u003C\/h3\u003E\u003Cul\u003E\u003Cli\u003EUse mathematical optimization to transform Supply Chain Management (SCM) processes.\u003C\/li\u003E\u003Cli\u003EApply LP, MIP, and heuristics to SCM, particularly in production planning, routing, and network design.\u003C\/li\u003E\u003Cli\u003EUtilize PowerBI and Python in optimization projects.\u003C\/li\u003E\u003Cli\u003EParticipate in a hackathon that pulls together everything learned throughout the certificate program.\u003C\/li\u003E\u003C\/ul\u003E\u003Ch3\u003E\u003Cstrong\u003EWhat Is Covered\u003C\/strong\u003E\u003C\/h3\u003E\u003Cul\u003E\u003Cli\u003ERole of mathematical optimization in addressing complex SCM challenges \u0026nbsp;\u003C\/li\u003E\u003Cli\u003EAppropriate application of linear programming (LP), mixed integer programming (MIP), and heuristics\u003C\/li\u003E\u003Cli\u003EEvaluation of production processes, distribution networks, and routes using optimization\u003C\/li\u003E\u003Cli\u003EAbility to pull together all content of the certificate program into a prescriptive analytics project\u003C\/li\u003E\u003Cli\u003EHands-on practice with these skills using data from the (fictional) Cardboard Company (CBC)\u003C\/li\u003E\u003C\/ul\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003ELearn advanced analytics and mathematical optimization to find solutions for supply chain problems.\u0026nbsp;The course also serves as a capstone for the Supply Chain Analytics Professional certificate program\u0026nbsp;by culminating in a hackathon where you\u2019ll design networks, inventory policies, and scenarios and then evaluate the outcomes via simulations.\u003C\/p\u003E\r\n","format":"limited_html"}],"field_summary_sentence":[{"value":"Learn advanced analytics and mathematical optimization to find solutions for supply chain problems."}],"uid":"27233","created_gmt":"2025-02-07 23:44:40","changed_gmt":"2025-02-07 23:45:28","author":"Andy Haleblian","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2025-12-01T13:00:00-05:00","event_time_end":"2025-12-04T17:00:00-05:00","event_time_end_last":"2025-12-04T17:00:00-05:00","gmt_time_start":"2025-12-01 18:00:00","gmt_time_end":"2025-12-04 22:00:00","gmt_time_end_last":"2025-12-04 22:00:00","rrule":null,"timezone":"America\/New_York"},"location":"Virtual\/Instructor-led","extras":[],"related_links":[{"url":"https:\/\/www.scl.gatech.edu\/education\/professional-education\/course\/scaoc","title":"Course webpage within the SCL website"}],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"},{"id":"1243","name":"The Supply Chain and Logistics Institute (SCL)"}],"categories":[],"keywords":[{"id":"7251","name":"analytics"},{"id":"167074","name":"Supply Chain"},{"id":"122741","name":"physical internet"},{"id":"186857","name":"go-gtmi"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"10377","name":"Career\/Professional development"}],"invited_audience":[{"id":"78771","name":"Public"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[{"value":"\u003Cp\u003E\u003Ca href=\u0022mailto:info@scl.gatech.edu\u0022\u003Einfo@scl.gatech.edu\u003C\/a\u003E\u003C\/p\u003E","format":"limited_html"}],"email":[],"slides":[],"orientation":[],"userdata":""}},"680337":{"#nid":"680337","#data":{"type":"event","title":"SCL Course: Essentials of Negotiations and Stakeholder Influence (Onsite\/In-Person or Virtual\/Instructor-led)","body":[{"value":"\u003Ch3\u003E\u003Cstrong\u003ECourse Description\u003C\/strong\u003E\u003C\/h3\u003E\u003Cp\u003EEssentials of Negotiations and Stakeholder Influence level-sets the participants\u0027 understanding of negotiation influence and strengthens preparation, planning and execution activities involved with both simple and complex negotiations. The program includes industry techniques and tools for traditional supplier negotiations, as well as tips for internal cross-functional leadership. Participants walk away with a standard industry and customized individual experience which includes their personal Negotiation Style \u201cDNA\u201d to help them embrace their own natural tendencies and strengths. The program includes mock negotiations to reinforce techniques and tactics immediately in a \u201cno judgement zone\u201d environment.\u003C\/p\u003E\u003Ch3\u003E\u003Cstrong\u003EWho Should Attend\u003C\/strong\u003E\u003C\/h3\u003E\u003Cp\u003EThis course is ideal for sourcing initiative leaders, project leaders, business unit leaders, operations managers, sales leaders and procurement \u0026amp; supply management-related professionals who are involved with supplier selection, contract development and supplier performance management.\u003C\/p\u003E\u003Ch3\u003E\u003Cstrong\u003EHow You Will Benefit\u003C\/strong\u003E\u003C\/h3\u003E\u003Cul\u003E\u003Cli\u003EIncrease emphasis negotiation conditioning and philosophy setting before and throughout the entire sourcing engagement process\u003C\/li\u003E\u003Cli\u003EEnhance your toolbox of industry standard negotiation prep tools like the SWOT and BATNA\u003C\/li\u003E\u003Cli\u003EBetter prepare for negotiations by leveraging knowledge of key negotiation terms and counter-offer tactics\u003C\/li\u003E\u003Cli\u003EImprove negotiation table techniques and soft skills to direct and redirect negotiation momentum\u003C\/li\u003E\u003Cli\u003EHeighten ability to successfully utilize your traditional \u0022comfort zone\u0022 approach in combination with your negotiation team\u2019s strengths by leveraging Personal Negotiation Styles\u003C\/li\u003E\u003C\/ul\u003E\u003Ch3\u003E\u003Cstrong\u003EWhat Is Covered\u003C\/strong\u003E\u003C\/h3\u003E\u003Cul\u003E\u003Cli\u003ENegotiation Conditioning Overview\u003C\/li\u003E\u003Cli\u003ENegotiation Preparation Tools\u003C\/li\u003E\u003Cli\u003ENegotiation Execution Techniques\u003C\/li\u003E\u003Cli\u003EStakeholder Engagement \u0026amp; Team Leadership\u003C\/li\u003E\u003Cli\u003ELive Negotiations Simulation \u0026amp; Feedback\u003C\/li\u003E\u003C\/ul\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EThis course\u0026nbsp;level-sets the participants\u0027 understanding of negotiation influence and strengthens preparation, planning and execution activities involved with both simple and complex negotiations. The program includes industry techniques and tools for traditional supplier negotiations, as well as tips for internal cross-functional leadership. Participants walk away with a standard industry and customized individual experience which includes their personal Negotiation Style \u201cDNA\u201d to help them embrace their own natural tendencies and strengths. The program includes mock negotiations to reinforce techniques and tactics immediately in a \u201cno judgement zone\u201d environment.\u003C\/p\u003E\r\n","format":"limited_html"}],"field_summary_sentence":[{"value":"Level-sets the participants\u0027 understanding of negotiation influence and strengthens preparation, planning and execution activities involved with both simple and complex negotiations."}],"uid":"27233","created_gmt":"2025-02-07 23:27:52","changed_gmt":"2025-02-07 23:29:21","author":"Andy Haleblian","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2025-09-25T08:00:00-04:00","event_time_end":"2025-09-26T17:00:00-04:00","event_time_end_last":"2025-09-26T17:00:00-04:00","gmt_time_start":"2025-09-25 12:00:00","gmt_time_end":"2025-09-26 21:00:00","gmt_time_end_last":"2025-09-26 21:00:00","rrule":null,"timezone":"America\/New_York"},"location":"Georgia Tech Savannah Campus OR Virtual\/Instructor-led","extras":[],"related_links":[{"url":"https:\/\/www.scl.gatech.edu\/epn","title":"Course webpage within the SCL website"}],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"},{"id":"1243","name":"The Supply Chain and Logistics Institute (SCL)"}],"categories":[],"keywords":[{"id":"167074","name":"Supply Chain"},{"id":"122741","name":"physical internet"},{"id":"110501","name":"purchasing"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"10377","name":"Career\/Professional development"}],"invited_audience":[{"id":"78771","name":"Public"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[{"value":"\u003Cp\u003E\u003Ca href=\u0022mailto:info@scl.gatech.edu\u0022\u003Einfo@scl.gatech.edu\u003C\/a\u003E\u003C\/p\u003E","format":"limited_html"}],"email":[],"slides":[],"orientation":[],"userdata":""}},"680336":{"#nid":"680336","#data":{"type":"event","title":"SCL Course: Contracting and Legal Oversight (Onsite\/In-Person or Virtual\/Instructor-led)","body":[{"value":"\u003Ch3\u003E\u003Cstrong\u003ECourse Description\u003C\/strong\u003E\u003C\/h3\u003E\u003Cp\u003EContracting and Legal Oversight provides participants with a holistic and integrated understanding of contract law, contract types, key industry standard contract terms, and contract structure to improve their confidence when creating or modifying contract documents. The program is geared to reinforce standards of excellence for professionals who are responsible for delivering contractual agreements and mitigating financial risk for their organization.\u003C\/p\u003E\u003Cp\u003EThe online version of the course is comprised of (3) instructor-led LIVE group webinars, homework, and pre-work (e.g. installing and testing software on your computer, testing connectivity with Canvas LMS and BlueJeans meeting software, etc.) to be completed before the first day of the course.\u003C\/p\u003E\u003Ch3\u003E\u003Cstrong\u003EWho Should Attend\u003C\/strong\u003E\u003C\/h3\u003E\u003Cp\u003EThis course is ideal for contract managers, procurement professionals, sourcing initiative leaders, project managers and all procurement \u0026amp; supply management-related professionals involved with bid contract development, contract execution or supplier performance management.\u003C\/p\u003E\u003Ch3\u003E\u003Cstrong\u003EHow You Will Benefit\u003C\/strong\u003E\u003C\/h3\u003E\u003Cul\u003E\u003Cli\u003EIncrease knowledge base in law of agency concepts to gain more confidence with contracting.\u003C\/li\u003E\u003Cli\u003EEnhance understanding of industry standard contract types and contract terms for more robust contract agreements.\u003C\/li\u003E\u003Cli\u003EBetter leverage sourcing category knowledge to modify existing contract elements for more holistic contract agreements.\u0026nbsp;\u003C\/li\u003E\u003Cli\u003EImprove internal contract execution communication for better results.\u003C\/li\u003E\u003Cli\u003EHeighten sense of executive financial impact and risk needs to gain leadership early support.\u003C\/li\u003E\u003C\/ul\u003E\u003Ch3\u003E\u003Cstrong\u003EWhat Is Covered\u003C\/strong\u003E\u003C\/h3\u003E\u003Cul\u003E\u003Cli\u003ELaw of Agency Overview\u003C\/li\u003E\u003Cli\u003EContract Agreement Types\u003C\/li\u003E\u003Cli\u003EDefining Key Terms\u003C\/li\u003E\u003Cli\u003EContract Structure \u0026amp; Drafting\u003C\/li\u003E\u003Cli\u003ERisk Mitigation \u0026amp; Communication\u003C\/li\u003E\u003C\/ul\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EThis course\u0026nbsp;provides participants with a holistic and integrated understanding of contract law, contract types, key industry standard contract terms, and contract structure to improve their confidence when creating or modifying contract documents. The program is geared to reinforce standards of excellence for professionals who are responsible for delivering contractual agreements and mitigating financial risk for their organization.\u003C\/p\u003E\r\n","format":"limited_html"}],"field_summary_sentence":[{"value":"This course provides participants with a holistic and integrated understanding of contract law, contract types, key industry standard contract terms, and contract structure to improve their confidence when creating or modifying contract documents."}],"uid":"27233","created_gmt":"2025-02-07 23:25:00","changed_gmt":"2025-02-07 23:27:11","author":"Andy Haleblian","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2025-09-23T12:00:00-04:00","event_time_end":"2025-09-24T17:00:00-04:00","event_time_end_last":"2025-09-24T17:00:00-04:00","gmt_time_start":"2025-09-23 16:00:00","gmt_time_end":"2025-09-24 21:00:00","gmt_time_end_last":"2025-09-24 21:00:00","rrule":null,"timezone":"America\/New_York"},"location":"Georgia Tech Savannah Campus OR Virtual\/Instructor-led","extras":[],"related_links":[{"url":"https:\/\/www.scl.gatech.edu\/clo","title":"Course webpage within the SCL website"}],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"},{"id":"1243","name":"The Supply Chain and Logistics Institute (SCL)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"10377","name":"Career\/Professional development"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"78771","name":"Public"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[{"value":"\u003Cp\u003E\u003Ca href=\u0022mailto:info@scl.gatech.edu\u0022\u003Einfo@scl.gatech.edu\u003C\/a\u003E\u003C\/p\u003E","format":"limited_html"}],"email":[],"slides":[],"orientation":[],"userdata":""}},"680335":{"#nid":"680335","#data":{"type":"event","title":"SCL Course: Category Management and Sourcing Leadership (Onsite\/In-Person or Virtual\/Instructor-led)","body":[{"value":"\u003Ch3\u003E\u003Cstrong\u003ECourse Description\u003C\/strong\u003E\u003C\/h3\u003E\u003Cp\u003ECategory Management and Sourcing Leadership is designed to deepen participants\u0027 knowledge base of core activities in the procurement \u0026amp; supply management function. The program covers the sourcing process, specifications gathering, common bid package alternatives, cross-functional collaboration and supplier evaluation \u0026amp; selection. Participants will walk away ready to develop bid packages more thoroughly to help drive sourcing decisions for their organizations. This \u0022hands on\u0022 delivery focuses on the professional serving as the main liaison between the buying organization and the selling organization in the company sourcing process.\u003C\/p\u003E\u003Ch3\u003E\u003Cstrong\u003EWho Should Attend\u003C\/strong\u003E\u003C\/h3\u003E\u003Cp\u003EThis course is ideal for sourcing initiative leaders, procurement professionals, project managers, finance analyst, contract managers and all procurement \u0026amp; supply management-related professionals involved with bid package development, bid package analysis, negotiations preparation, contracting and supplier selection activity.\u003C\/p\u003E\u003Ch3\u003E\u003Cstrong\u003EHow You Will Benefit\u003C\/strong\u003E\u003C\/h3\u003E\u003Cul\u003E\u003Cli\u003EIncrease knowledge base of solicitation options (i.e. RFI, RFP, RFQ) and which solicitation approach to apply based on your organization\u0027s needs\u003C\/li\u003E\u003Cli\u003EEnhance understanding of sourcing process and critical steps in the Bid Package development and delivery activities\u003C\/li\u003E\u003Cli\u003EBetter leverage and utilization of solicitation tools to drive successful development of bid packages\u003C\/li\u003E\u003Cli\u003EImprove set up and execution of supplier selection scorecards to aid in identifying best Total Cost of Ownership alternatives\u003C\/li\u003E\u003Cli\u003EHeighten understanding of executive communication to leverage leadership support throughout the organization\u003C\/li\u003E\u003C\/ul\u003E\u003Ch3\u003E\u003Cstrong\u003EWhat Is Covered\u003C\/strong\u003E\u003C\/h3\u003E\u003Cul\u003E\u003Cli\u003EBid Package Development Overview\u003C\/li\u003E\u003Cli\u003ESourcing Initiative Process Roadmap\u0026nbsp;\u003C\/li\u003E\u003Cli\u003EMarketplace Analysis Tools\u003C\/li\u003E\u003Cli\u003EBid Package Alternatives \u0026amp; Design\u003C\/li\u003E\u003Cli\u003ESupplier Selection \u0026amp; Communication\u003C\/li\u003E\u003C\/ul\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EThis course\u0026nbsp;is designed to deepen participants\u0027 knowledge base of core activities in the procurement \u0026amp; supply management function. The program covers the sourcing process, specifications gathering, common bid package alternatives, cross-functional collaboration and supplier evaluation \u0026amp; selection. Participants will walk away ready to develop bid packages more thoroughly to help drive sourcing decisions for their organizations. This \u0022hands on\u0022 delivery focuses on the professional serving as the main liaison between the buying organization and the selling organization in the company sourcing process.\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"This course is designed to deepen participants\u0027 knowledge base of core activities in the procurement \u0026 supply management function."}],"uid":"27233","created_gmt":"2025-02-07 23:21:28","changed_gmt":"2025-02-07 23:23:43","author":"Andy Haleblian","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2025-09-22T08:00:00-04:00","event_time_end":"2025-09-23T17:00:00-04:00","event_time_end_last":"2025-09-23T17:00:00-04:00","gmt_time_start":"2025-09-22 12:00:00","gmt_time_end":"2025-09-23 21:00:00","gmt_time_end_last":"2025-09-23 21:00:00","rrule":null,"timezone":"America\/New_York"},"location":"Georgia Tech Savannah Campus OR Virtual\/Instructor-led","extras":[],"related_links":[{"url":"https:\/\/www.scl.gatech.edu\/education\/professional-education\/course\/cmsl","title":"Course webpage within the SCL website"}],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"},{"id":"1243","name":"The Supply Chain and Logistics Institute (SCL)"}],"categories":[],"keywords":[{"id":"170001","name":"Supply Chain Engineering"},{"id":"167074","name":"Supply Chain"},{"id":"110501","name":"purchasing"},{"id":"122741","name":"physical internet"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"10377","name":"Career\/Professional development"}],"invited_audience":[{"id":"78771","name":"Public"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[{"value":"\u003Cp\u003Einfo@scl.gatech.edu\u003C\/p\u003E","format":"limited_html"}],"email":[],"slides":[],"orientation":[],"userdata":""}},"678581":{"#nid":"678581","#data":{"type":"event","title":"SCL Course: Generative AI Application for Supply Chain Professionals (Onsite\/In-Person or Virtual\/Instructor-led)","body":[{"value":"\u003Ch3\u003E\u003Cstrong\u003ECourse Description\u003C\/strong\u003E\u003C\/h3\u003E\u003Cp\u003EThis course provides a deep dive into the ways in which artificial intelligence (AI) optimizes supply chain efficiency. Participants will explore generative AI fundamentals, prompt engineering, and practical applications such as automated inventory systems, predictive maintenance, and route optimization. The course also covers ethical AI use, good and bad use of generative AI (GenAI), and rapidly emerging use cases. By the end, professionals will be skilled in applying AI to enhance supply chain processes and drive success in their organizations.\u003C\/p\u003E\u003Ch3\u003E\u003Cstrong\u003EWho Should Attend\u003C\/strong\u003E\u003C\/h3\u003E\u003Cp\u003EThis course targets supply chain managers, data analysts, logistics professionals, procurement specialists, and business leaders aiming to harness GenAI for enhanced supply chain operations. It is ideal for those interested in GenAI-driven efficiency, strategic insights, and navigation of GenAI\u0027s role in transforming supply chain processes.\u003C\/p\u003E\u003Ch3\u003E\u003Cstrong\u003EHow You Will Benefit\u003C\/strong\u003E\u003C\/h3\u003E\u003Cul\u003E\u003Cli\u003EEnhance decision-making capabilities through GenAI-driven insights to optimize processes and boost efficiency.\u003C\/li\u003E\u003Cli\u003EAcquire practical skills in prompt engineering and the use of generative AI models.\u003C\/li\u003E\u003Cli\u003EExplore practical use cases that can be reapplied.\u003C\/li\u003E\u003Cli\u003ELearn about good and bad use of GenAI for individuals, teams, and organizations.\u003C\/li\u003E\u003Cli\u003EBecome better equipped to effectively harness GenAI capabilities in supply chain activities and planning.\u003C\/li\u003E\u003C\/ul\u003E\u003Ch3\u003E\u003Cstrong\u003EWhat Is Covered\u003C\/strong\u003E\u003C\/h3\u003E\u003Cul\u003E\u003Cli\u003EFoundational understanding of using GenAI in supply chain management\u003C\/li\u003E\u003Cli\u003EBasics of GenAI\u003C\/li\u003E\u003Cli\u003ECrafting effective AI prompts and their applications in optimizing warehouse layouts\u003C\/li\u003E\u003Cli\u003EPredictive maintenance and supplier selection\u003C\/li\u003E\u003Cli\u003EElimination of redundant tasks through AI\u003C\/li\u003E\u003Cli\u003EEthical considerations, risk assessments, and strategy for AI adoption\u003C\/li\u003E\u003Cli\u003EPractical strategies and real-world examples for implementing AI solutions effectively and making informed decisions\u003C\/li\u003E\u003C\/ul\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EParticipants will explore generative AI fundamentals, prompt engineering, and practical applications such as automated inventory systems, predictive maintenance, and route optimization.\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Participants will explore generative AI fundamentals, prompt engineering, and practical applications such as automated inventory systems, predictive maintenance, and route optimization."}],"uid":"27233","created_gmt":"2024-11-21 13:38:19","changed_gmt":"2025-02-07 23:18:43","author":"Andy Haleblian","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2025-03-26T20:00:00-04:00","event_time_end":"2025-03-28T16:00:00-04:00","event_time_end_last":"2025-03-28T16:00:00-04:00","gmt_time_start":"2025-03-27 00:00:00","gmt_time_end":"2025-03-28 20:00:00","gmt_time_end_last":"2025-03-28 20:00:00","rrule":null,"timezone":"America\/New_York"},"location":"Georgia Tech Global Learning Center OR Virtual\/Instructor-led","extras":[],"related_links":[{"url":"https:\/\/www.scl.gatech.edu\/education\/professional-education\/course\/gaiascp","title":"Course webpage within the SCL website"}],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"},{"id":"1243","name":"The Supply Chain and Logistics Institute (SCL)"}],"categories":[],"keywords":[{"id":"192390","name":"generative AI"},{"id":"170001","name":"Supply Chain Engineering"},{"id":"167074","name":"Supply Chain"},{"id":"122741","name":"physical internet"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"10377","name":"Career\/Professional development"}],"invited_audience":[{"id":"78771","name":"Public"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[{"value":"\u003Cp\u003E\u003Ca href=\u0022mailto:info@scl.gatech.edu\u0022\u003Einfo@scl.gatech.edu\u003C\/a\u003E\u003C\/p\u003E","format":"limited_html"}],"email":[],"slides":[],"orientation":[],"userdata":""}},"678578":{"#nid":"678578","#data":{"type":"event","title":"SCL Course: Transforming Supply Chain Management and Performance Analysis (Virtual\/Instructor-led)","body":[{"value":"\u003Ch3\u003E\u003Cstrong\u003ECourse Description\u003C\/strong\u003E\u003C\/h3\u003E\u003Cp\u003EThis course is the first in the four-course Supply Chain Analytics Professional certificate program. It prepares you to apply leading-edge analytical methods and technology enablers across the supply chain. You\u2019ll learn the dynamics of supply chains, the most relevant planning challenges, and the roles of different types of analytics. Next, you\u2019ll learn about data cleansing, exploratory data analysis, and visualization. You\u2019ll use Python and PowerBI to analyze the causes of underperformance and to build dashboards to visualize supply chain data. You will leave knowing how to gather, analyze, and prepare your data through descriptive analytics before you dig into deeper applications.\u003C\/p\u003E\u003Cp\u003EThe online version of the course is comprised of (4) half-day instructor-led LIVE group webinars and pre-work (e.g. installing and testing software on your computer, testing connectivity with LMS and meeting software, etc.) to be completed before the first day of the course.\u003C\/p\u003E\u003Ch3\u003E\u003Cstrong\u003EWho Should Attend\u003C\/strong\u003E\u003C\/h3\u003E\u003Cp\u003EExperienced business professionals who perform or want to perform analytics to improve their supply chain management processes. They want to tackle strategic goals and to perform leading edge analytics projects that address the full complexity of supply chains.\u003C\/p\u003E\u003Ch3\u003E\u003Cstrong\u003EHow You Will Benefit\u003C\/strong\u003E\u003C\/h3\u003E\u003Cul\u003E\u003Cli\u003EUnderstand the most relevant planning challenges across the strategic, tactical, and operational levels of supply chains\u003C\/li\u003E\u003Cli\u003ELearn the difference between analytics types, the links between them, and how to best use them to improve\u0026nbsp;supply chain management (SCM)\u0026nbsp;processes\u003C\/li\u003E\u003Cli\u003EUse\u0026nbsp;Key Performance Indicators (KPIs)\u0026nbsp;to find causes of underperformance in supply chains and to plan for analytics projects that will address strategic SCM goals\u003C\/li\u003E\u003Cli\u003EUtilize Python and PowerBI to understand, visualize, and analyze data in order to prepare for deeper analytics\u003C\/li\u003E\u003C\/ul\u003E\u003Ch3\u003E\u003Cstrong\u003EWhat Is Covered\u003C\/strong\u003E\u003C\/h3\u003E\u003Cul\u003E\u003Cli\u003EThe role of analytics in SCM\u003C\/li\u003E\u003Cli\u003ETypes of analytics (descriptive, diagnostic, predictive, and prescriptive) and the relationships between them\u003C\/li\u003E\u003Cli\u003EPreprocessing (cleaning and integrating) data as it relates to SCM\u003C\/li\u003E\u003Cli\u003EConducting exploratory data analysis on supply chain data\u003C\/li\u003E\u003Cli\u003EBest practices for visualizing data and building dashboards\u003C\/li\u003E\u003Cli\u003EIdentifying and analyzing KPIs of SCM\u003C\/li\u003E\u003Cli\u003EHands-on practice with these skills using data from the (fictional) Cardboard Company (CBC)\u003C\/li\u003E\u003C\/ul\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003ELearn the dynamics of supply chains, the most relevant planning challenges, and the roles of different types of analytics. Next, you\u2019ll learn about data cleansing, exploratory data analysis, and visualization. You\u2019ll use Python and PowerBI to analyze the causes of underperformance and to build dashboards to visualize supply chain data. You will leave knowing how to gather, analyze, and prepare your data through descriptive analytics before you dig into deeper applications.\u003C\/p\u003E\r\n","format":"limited_html"}],"field_summary_sentence":[{"value":"Learn to apply leading-edge analytical methods and technology enablers across the supply chain"}],"uid":"27233","created_gmt":"2024-11-21 13:24:33","changed_gmt":"2025-02-07 23:05:45","author":"Andy Haleblian","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2025-02-17T13:00:00-05:00","event_time_end":"2025-02-20T17:00:00-05:00","event_time_end_last":"2025-02-20T17:00:00-05:00","gmt_time_start":"2025-02-17 18:00:00","gmt_time_end":"2025-02-20 22:00:00","gmt_time_end_last":"2025-02-20 22:00:00","rrule":null,"timezone":"America\/New_York"},"location":"Virtual\/Instructor-led","extras":[],"related_links":[{"url":"https:\/\/www.scl.gatech.edu\/education\/professional-education\/course\/scapa","title":"Course webpage within the SCL website"}],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"},{"id":"1243","name":"The Supply Chain and Logistics Institute (SCL)"}],"categories":[],"keywords":[{"id":"7251","name":"analytics"},{"id":"167074","name":"Supply Chain"},{"id":"122741","name":"physical internet"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"10377","name":"Career\/Professional development"}],"invited_audience":[{"id":"78771","name":"Public"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[{"value":"\u003Cp\u003E\u003Ca href=\u0022mailto:info@scl.gatech.edu\u0022\u003Einfo@scl.gatech.edu\u003C\/a\u003E\u003C\/p\u003E","format":"limited_html"}],"email":[],"slides":[],"orientation":[],"userdata":""}},"673937":{"#nid":"673937","#data":{"type":"event","title":"SCL Course: Category Management and Sourcing Leadership (Virtual\/Instructor-led)","body":[{"value":"\u003Ch3\u003E\u003Cstrong\u003ECourse Description\u003C\/strong\u003E\u003C\/h3\u003E\u003Cp\u003ECategory Management and Sourcing Leadership is designed to deepen participants\u0027 knowledge base of core activities in the procurement \u0026amp; supply management function. The program covers the sourcing process, specifications gathering, common bid package alternatives, cross-functional collaboration and supplier evaluation \u0026amp; selection. Participants will walk away ready to develop bid packages more thoroughly to help drive sourcing decisions for their organizations. This \u0022hands on\u0022 delivery focuses on the professional serving as the main liaison between the buying organization and the selling organization in the company sourcing process.\u003C\/p\u003E\u003Ch3\u003E\u003Cstrong\u003EWho Should Attend\u003C\/strong\u003E\u003C\/h3\u003E\u003Cp\u003EThis course is ideal for sourcing initiative leaders, procurement professionals, project managers, finance analyst, contract managers and all procurement \u0026amp; supply management-related professionals involved with bid package development, bid package analysis, negotiations preparation, contracting and supplier selection activity.\u003C\/p\u003E\u003Ch3\u003E\u003Cstrong\u003EHow You Will Benefit\u003C\/strong\u003E\u003C\/h3\u003E\u003Cul\u003E\u003Cli\u003EIncrease knowledge base of solicitation options (i.e. RFI, RFP, RFQ) and which solicitation approach to apply based on your organization\u0027s needs\u003C\/li\u003E\u003Cli\u003EEnhance understanding of sourcing process and critical steps in the Bid Package development and delivery activities\u003C\/li\u003E\u003Cli\u003EBetter leverage and utilization of solicitation tools to drive successful development of bid packages\u003C\/li\u003E\u003Cli\u003EImprove set up and execution of supplier selection scorecards to aid in identifying best Total Cost of Ownership alternatives\u003C\/li\u003E\u003Cli\u003EHeighten understanding of executive communication to leverage leadership support throughout the organization\u003C\/li\u003E\u003C\/ul\u003E\u003Ch3\u003E\u003Cstrong\u003EWhat Is Covered\u003C\/strong\u003E\u003C\/h3\u003E\u003Cul\u003E\u003Cli\u003EBid Package Development Overview\u003C\/li\u003E\u003Cli\u003ESourcing Initiative Process Roadmap\u0026nbsp;\u003C\/li\u003E\u003Cli\u003EMarketplace Analysis Tools\u003C\/li\u003E\u003Cli\u003EBid Package Alternatives \u0026amp; Design\u003C\/li\u003E\u003Cli\u003ESupplier Selection \u0026amp; Communication\u003C\/li\u003E\u003C\/ul\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EThis course\u0026nbsp;is designed to deepen participants\u0027 knowledge base of core activities in the procurement \u0026amp; supply management function. The program covers the sourcing process, specifications gathering, common bid package alternatives, cross-functional collaboration and supplier evaluation \u0026amp; selection. Participants will walk away ready to develop bid packages more thoroughly to help drive sourcing decisions for their organizations. This \u0022hands on\u0022 delivery focuses on the professional serving as the main liaison between the buying organization and the selling organization in the company sourcing process.\u003C\/p\u003E\r\n","format":"limited_html"}],"field_summary_sentence":[{"value":"This course is designed to deepen participants\u0027 knowledge base of core activities in the procurement \u0026 supply management function."}],"uid":"27233","created_gmt":"2024-04-03 11:34:30","changed_gmt":"2025-02-07 23:05:40","author":"Andy Haleblian","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2025-02-24T13:00:00-05:00","event_time_end":"2025-02-27T14:30:00-05:00","event_time_end_last":"2025-02-27T14:30:00-05:00","gmt_time_start":"2025-02-24 18:00:00","gmt_time_end":"2025-02-27 19:30:00","gmt_time_end_last":"2025-02-27 19:30:00","rrule":null,"timezone":"America\/New_York"},"location":"Virtual\/Instructor-led","extras":[],"related_links":[{"url":"https:\/\/www.scl.gatech.edu\/cmsl","title":"Course webpage within the SCL website"}],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"},{"id":"1243","name":"The Supply Chain and Logistics Institute (SCL)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"10377","name":"Career\/Professional development"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"78771","name":"Public"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[{"value":"\u003Cp\u003EEMAIL: \u003Ca href=\u0022mailto:info@scl.gatech.edu\u0022\u003Einfo@scl.gatech.edu\u003C\/a\u003E or CALL: (404) 385-3501 between 9:00a.m. and 4:00p.m., Eastern time.\u003C\/p\u003E","format":"limited_html"}],"email":[],"slides":[],"orientation":[],"userdata":""}},"673938":{"#nid":"673938","#data":{"type":"event","title":"SCL Course: Contracting and Legal Oversight (Virtual\/Instructor-led)","body":[{"value":"\u003Ch3\u003E\u003Cstrong\u003ECourse Description\u003C\/strong\u003E\u003C\/h3\u003E\u003Cp\u003EContracting and Legal Oversight provides participants with a holistic and integrated understanding of contract law, contract types, key industry standard contract terms, and contract structure to improve their confidence when creating or modifying contract documents. The program is geared to reinforce standards of excellence for professionals who are responsible for delivering contractual agreements and mitigating financial risk for their organization.\u003C\/p\u003E\u003Cp\u003EThe online version of the course is comprised of (3) instructor-led LIVE group webinars, homework, and pre-work (e.g. installing and testing software on your computer, testing connectivity with Canvas LMS and BlueJeans meeting software, etc.) to be completed before the first day of the course.\u003C\/p\u003E\u003Ch3\u003E\u003Cstrong\u003EWho Should Attend\u003C\/strong\u003E\u003C\/h3\u003E\u003Cp\u003EThis course is ideal for contract managers, procurement professionals, sourcing initiative leaders, project managers and all procurement \u0026amp; supply management-related professionals involved with bid contract development, contract execution or supplier performance management.\u003C\/p\u003E\u003Ch3\u003E\u003Cstrong\u003EHow You Will Benefit\u003C\/strong\u003E\u003C\/h3\u003E\u003Cul\u003E\u003Cli\u003EIncrease knowledge base in law of agency concepts to gain more confidence with contracting.\u003C\/li\u003E\u003Cli\u003EEnhance understanding of industry standard contract types and contract terms for more robust contract agreements.\u003C\/li\u003E\u003Cli\u003EBetter leverage sourcing category knowledge to modify existing contract elements for more holistic contract agreements.\u0026nbsp;\u003C\/li\u003E\u003Cli\u003EImprove internal contract execution communication for better results.\u003C\/li\u003E\u003Cli\u003EHeighten sense of executive financial impact and risk needs to gain leadership early support.\u003C\/li\u003E\u003C\/ul\u003E\u003Ch3\u003E\u003Cstrong\u003EWhat Is Covered\u003C\/strong\u003E\u003C\/h3\u003E\u003Cul\u003E\u003Cli\u003ELaw of Agency Overview\u003C\/li\u003E\u003Cli\u003EContract Agreement Types\u003C\/li\u003E\u003Cli\u003EDefining Key Terms\u003C\/li\u003E\u003Cli\u003EContract Structure \u0026amp; Drafting\u003C\/li\u003E\u003Cli\u003ERisk Mitigation \u0026amp; Communication\u003C\/li\u003E\u003C\/ul\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EThis course\u0026nbsp;provides participants with a holistic and integrated understanding of contract law, contract types, key industry standard contract terms, and contract structure to improve their confidence when creating or modifying contract documents. The program is geared to reinforce standards of excellence for professionals who are responsible for delivering contractual agreements and mitigating financial risk for their organization.\u003C\/p\u003E\r\n","format":"limited_html"}],"field_summary_sentence":[{"value":"This course provides participants with a holistic and integrated understanding of contract law, contract types, key industry standard contract terms, and contract structure to improve their confidence when creating or modifying contract documents."}],"uid":"27233","created_gmt":"2024-04-03 11:38:04","changed_gmt":"2025-02-07 23:05:34","author":"Andy Haleblian","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2025-02-27T15:00:00-05:00","event_time_end":"2025-03-05T17:00:00-05:00","event_time_end_last":"2025-03-05T17:00:00-05:00","gmt_time_start":"2025-02-27 20:00:00","gmt_time_end":"2025-03-05 22:00:00","gmt_time_end_last":"2025-03-05 22:00:00","rrule":null,"timezone":"America\/New_York"},"location":"Virtual\/Instructor-led","extras":[],"related_links":[{"url":"https:\/\/www.scl.gatech.edu\/clo","title":"Course webpage within the SCL website"}],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"},{"id":"1243","name":"The Supply Chain and Logistics Institute (SCL)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"10377","name":"Career\/Professional development"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"78771","name":"Public"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[{"value":"\u003Cp\u003E\u003Ca href=\u0022mailto:info@scl.gatech.edu\u0022\u003Einfo@scl.gatech.edu\u003C\/a\u003E\u003C\/p\u003E","format":"limited_html"}],"email":[],"slides":[],"orientation":[],"userdata":""}},"673939":{"#nid":"673939","#data":{"type":"event","title":"SCL Course: Essentials of Negotiations and Stakeholder Influence (Virtual\/Instructor-led)","body":[{"value":"\u003Ch3\u003E\u003Cstrong\u003ECourse Description\u003C\/strong\u003E\u003C\/h3\u003E\u003Cp\u003EEssentials of Negotiations and Stakeholder Influence level-sets the participants\u0027 understanding of negotiation influence and strengthens preparation, planning and execution activities involved with both simple and complex negotiations. The program includes industry techniques and tools for traditional supplier negotiations, as well as tips for internal cross-functional leadership. Participants walk away with a standard industry and customized individual experience which includes their personal Negotiation Style \u201cDNA\u201d to help them embrace their own natural tendencies and strengths. The program includes mock negotiations to reinforce techniques and tactics immediately in a \u201cno judgement zone\u201d environment.\u003C\/p\u003E\u003Ch3\u003E\u003Cstrong\u003EWho Should Attend\u003C\/strong\u003E\u003C\/h3\u003E\u003Cp\u003EThis course is ideal for sourcing initiative leaders, project leaders, business unit leaders, operations managers, sales leaders and procurement \u0026amp; supply management-related professionals who are involved with supplier selection, contract development and supplier performance management.\u003C\/p\u003E\u003Ch3\u003E\u003Cstrong\u003EHow You Will Benefit\u003C\/strong\u003E\u003C\/h3\u003E\u003Cul\u003E\u003Cli\u003EIncrease emphasis negotiation conditioning and philosophy setting before and throughout the entire sourcing engagement process\u003C\/li\u003E\u003Cli\u003EEnhance your toolbox of industry standard negotiation prep tools like the SWOT and BATNA\u003C\/li\u003E\u003Cli\u003EBetter prepare for negotiations by leveraging knowledge of key negotiation terms and counter-offer tactics\u003C\/li\u003E\u003Cli\u003EImprove negotiation table techniques and soft skills to direct and redirect negotiation momentum\u003C\/li\u003E\u003Cli\u003EHeighten ability to successfully utilize your traditional \u0022comfort zone\u0022 approach in combination with your negotiation team\u2019s strengths by leveraging Personal Negotiation Styles\u003C\/li\u003E\u003C\/ul\u003E\u003Ch3\u003E\u003Cstrong\u003EWhat Is Covered\u003C\/strong\u003E\u003C\/h3\u003E\u003Cul\u003E\u003Cli\u003ENegotiation Conditioning Overview\u003C\/li\u003E\u003Cli\u003ENegotiation Preparation Tools\u003C\/li\u003E\u003Cli\u003ENegotiation Execution Techniques\u003C\/li\u003E\u003Cli\u003EStakeholder Engagement \u0026amp; Team Leadership\u003C\/li\u003E\u003Cli\u003ELive Negotiations Simulation \u0026amp; Feedback\u003C\/li\u003E\u003C\/ul\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EThis course\u0026nbsp;level-sets the participants\u0027 understanding of negotiation influence and strengthens preparation, planning and execution activities involved with both simple and complex negotiations. The program includes industry techniques and tools for traditional supplier negotiations, as well as tips for internal cross-functional leadership. Participants walk away with a standard industry and customized individual experience which includes their personal Negotiation Style \u201cDNA\u201d to help them embrace their own natural tendencies and strengths. The program includes mock negotiations to reinforce techniques and tactics immediately in a \u201cno judgement zone\u201d environment.\u003C\/p\u003E\r\n","format":"limited_html"}],"field_summary_sentence":[{"value":"Level-sets the participants\u0027 understanding of negotiation influence and strengthens preparation, planning and execution activities involved with both simple and complex negotiations."}],"uid":"27233","created_gmt":"2024-04-03 11:40:52","changed_gmt":"2025-02-07 23:05:12","author":"Andy Haleblian","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2025-03-06T13:00:00-05:00","event_time_end":"2025-03-12T16:00:00-04:00","event_time_end_last":"2025-03-12T16:00:00-04:00","gmt_time_start":"2025-03-06 18:00:00","gmt_time_end":"2025-03-12 20:00:00","gmt_time_end_last":"2025-03-12 20:00:00","rrule":null,"timezone":"America\/New_York"},"location":"Virtual\/Instructor-led","extras":[],"related_links":[{"url":"https:\/\/www.scl.gatech.edu\/epn","title":"Course webpage within the SCL website"}],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"},{"id":"1243","name":"The Supply Chain and Logistics Institute (SCL)"}],"categories":[],"keywords":[{"id":"167074","name":"Supply Chain"},{"id":"122741","name":"physical internet"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"10377","name":"Career\/Professional development"}],"invited_audience":[{"id":"78771","name":"Public"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[{"value":"\u003Cp\u003E\u003Ca href=\u0022mailto:info@scl.gatech.edu\u0022\u003Einfo@scl.gatech.edu\u003C\/a\u003E\u003C\/p\u003E","format":"limited_html"}],"email":[],"slides":[],"orientation":[],"userdata":""}},"680223":{"#nid":"680223","#data":{"type":"event","title":"SCL Course: Creating Business Value with Statistical Analysis (Virtual\/Instructor-led)","body":[{"value":"\u003Ch3\u003E\u003Cstrong\u003ECourse Description\u003C\/strong\u003E\u003C\/h3\u003E\u003Cp\u003EThis course is the second in the four-course Supply Chain Analytics Professional certificate program. It emphasizes operational performance metrics to align supply chain management with strategic business goals. You\u2019ll learn several statistics concepts (e.g. variance analysis, hypothesis testing, forecasting methods) along with inventory management models. You\u2019ll use diagnostic analytics with PowerBI and Python to conduct demand and service profiling, undertake root cause analysis, and use time series forecasting in inventory management.\u003C\/p\u003E\u003Cp\u003EThe online version of the course is comprised of (4) half-day online instructor-led LIVE group webinars and pre-work (e.g. installing and testing software on your computer, testing connectivity with LMS and meeting software, etc.) to be completed before the first day of the course.\u003C\/p\u003E\u003Ch3\u003E\u003Cstrong\u003EWho Should Attend\u003C\/strong\u003E\u003C\/h3\u003E\u003Cp\u003EExperienced business professionals who perform or want to perform analytics to improve their supply chain management processes. They want to tackle strategic goals and to perform leading edge analytics projects that address the full complexity of supply chains.\u003C\/p\u003E\u003Ch3\u003E\u003Cstrong\u003EHow You Will Benefit\u003C\/strong\u003E\u003C\/h3\u003E\u003Cul\u003E\u003Cli\u003EUnderstand why and how to align Supply Chain Management (SCM) strategy with business strategy\u003C\/li\u003E\u003Cli\u003ELearn statistics techniques as they relate to SCM\u003C\/li\u003E\u003Cli\u003EUnderstand inventory management models and how to apply statistics techniques to them\u003C\/li\u003E\u003Cli\u003ECreate time series forecasts based on SCM data\u003C\/li\u003E\u003Cli\u003EUtilize Python and PowerBI to perform statistical analyses, create time series forecasts and visualize results\u003C\/li\u003E\u003C\/ul\u003E\u003Ch3\u003E\u003Cstrong\u003EWhat Is Covered\u003C\/strong\u003E\u003C\/h3\u003E\u003Cul\u003E\u003Cli\u003EThe importance of aligning SCM and business strategy\u003C\/li\u003E\u003Cli\u003EHow to ask the right business questions as they relate to SCM\u003C\/li\u003E\u003Cli\u003EHow to use statistics to identify issues, compare data, and forecast decision outcomes\u003C\/li\u003E\u003Cli\u003EStatistical\u0026nbsp;concepts including variance analysis and hypothesis testing\u003C\/li\u003E\u003Cli\u003EInventory management models\u003C\/li\u003E\u003Cli\u003EApplying statistics to inventory management models\u003C\/li\u003E\u003Cli\u003EForecasting techniques including time series forecasting\u003C\/li\u003E\u003Cli\u003EHands-on practice with these skills using data from the (fictional) Cardboard Company (CBC)\u003C\/li\u003E\u003C\/ul\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003ELearn statistics concepts (e.g. variance analysis, hypothesis testing, forecasting methods) and inventory management models to improve operational performance metrics and align supply chain management with strategic business goals.\u003C\/p\u003E\r\n","format":"limited_html"}],"field_summary_sentence":[{"value":"Learn statistics concepts (e.g. variance analysis, hypothesis testing, forecasting methods) and inventory management models."}],"uid":"27233","created_gmt":"2025-02-05 19:23:23","changed_gmt":"2025-02-07 23:05:06","author":"Andy Haleblian","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2025-03-24T08:00:00-04:00","event_time_end":"2025-03-27T12:00:00-04:00","event_time_end_last":"2025-03-27T12:00:00-04:00","gmt_time_start":"2025-03-24 12:00:00","gmt_time_end":"2025-03-27 16:00:00","gmt_time_end_last":"2025-03-27 16:00:00","rrule":null,"timezone":"America\/New_York"},"location":"Virtual\/Instructor-led","extras":[],"related_links":[{"url":"https:\/\/www.scl.gatech.edu\/education\/professional-education\/course\/scabv","title":"Course detail within the SCL website"}],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"},{"id":"1243","name":"The Supply Chain and Logistics Institute (SCL)"}],"categories":[],"keywords":[{"id":"7251","name":"analytics"},{"id":"167074","name":"Supply Chain"},{"id":"122741","name":"physical internet"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"10377","name":"Career\/Professional development"}],"invited_audience":[{"id":"78771","name":"Public"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[{"value":"\u003Cp\u003E\u003Ca href=\u0022mailto:info@scl.gatech.edu\u0022\u003Einfo@scl.gatech.edu\u003C\/a\u003E\u003C\/p\u003E","format":"limited_html"}],"email":[],"slides":[],"orientation":[],"userdata":""}},"680311":{"#nid":"680311","#data":{"type":"event","title":"ShapiroFest Workshop","body":[{"value":"\u003Cp\u003EStochastic programming addresses optimization problems involving random parameters, which arise in many fields of science and engineering, including telecommunications, transportation, energy, medicine, and finance. Professor Alexander Shapiro has made fundamental contributions to the theoretical and methodological foundations of stochastic programming. His pioneering work includes novel modeling approaches, such as risk-averse optimization and distributionally robust Markov decision processes; advancements in duality theory and perturbation analysis; and development of solution techniques like sample average approximation and robust stochastic approximation. These innovations have significantly expanded the scope and capabilities of stochastic programming, enabling it to tackle a broader range of practical and theoretical challenges. Building on his foundational contributions, stochastic programming has become a critical tool in emerging fields such as machine learning and artificial intelligence. This workshop honors Professor Shapiro\u2019s profound influence on the field and celebrates his remarkable contributions.\u003C\/p\u003E\u003Cp\u003EAlexander Shapiro is the A. Russell Chandler III Chair and Professor in the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Tech. Dr. Shapiro\u2019s research interests are focused on stochastic programming, risk analysis, simulation based optimization, and multivariate statistical analysis. In 2013 he was awarded Khachiyan Prize of INFORMS for lifetime achievements in optimization, and in 2018 he was a recipient of the Dantzig Prize awarded by the Mathematical Optimization Society and Society for Industrial and Applied Mathematics. In 2020 he was elected to the National Academy of Engineering. In 2021 he was a recipient of John von Neumann Theory Prize awarded by the Institute for Operations Research and the Management Sciences (INFORMS). Dr. Shapiro served on the editorial boards of a number of professional journals. He was an area editor (optimization) of Operations Research and the editor-in-chief of Mathematical Programming, Series A.\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EThis workshop honors Professor Shapiro\u2019s profound influence on the field and celebrates his remarkable contributions.\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"A Celebration of Alexander Shapiro\u2019s Legacy in Stochastic Optimization"}],"uid":"36433","created_gmt":"2025-02-07 18:45:38","changed_gmt":"2025-02-07 18:47:21","author":"mrussell89","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2025-03-17T00:00:00-04:00","event_time_end":"2025-03-18T23:59:59-04:00","event_time_end_last":"2025-03-18T23:59:59-04:00","gmt_time_start":"2025-03-17 04:00:00","gmt_time_end":"2025-03-19 03:59:59","gmt_time_end_last":"2025-03-19 03:59:59","rrule":null,"timezone":"America\/New_York"},"location":"IC 211","extras":["free_food"],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[],"invited_audience":[],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"680007":{"#nid":"680007","#data":{"type":"event","title":"ISyE Seminar - Soroosh Shafiee","body":[{"value":"\u003Ch3\u003E\u003Cstrong\u003ETitle:\u003C\/strong\u003E\u003C\/h3\u003E\u003Cp\u003ELearning with Local and Global Adversarial Corruptions\u003C\/p\u003E\u003Ch3\u003E\u003Cstrong\u003EAbstract:\u003C\/strong\u003E\u0026nbsp;\u003C\/h3\u003E\u003Cp\u003EWe study learning in an adversarial setting, where an epsilon fraction of samples from a distribution P are globally corrupted (arbitrarily modified), and the remaining perturbations have an average magnitude bounded by rho (local corruptions). With access to n such corrupted samples, we aim to develop a computationally efficient approach that achieves the optimal minimax excess risk. Our approach combines a data-driven cleaning module with a distributionally robust optimization (DRO) framework. We demonstrate that if the data cleaning module is minimax optimal with respect to the Wasserstein loss, solving an optimal transport-based DRO problem ensures a minimax optimal decision. We further provide tractable reformulations for both modules. Specifically, we introduce an optimal filtering algorithm to clean corrupted data by identifying and removing outliers. For the DRO module, we reformulate the problem as a two-player zero-sum game, deriving finite convex formulations. We show that the minimax theorem applies to this game, and Nash equilibria exist. Finally, we present a principled approach for constructing adversarial examples.\u003C\/p\u003E\u003Ch3\u003E\u003Cstrong\u003EBio:\u003C\/strong\u003E\u0026nbsp;\u003C\/h3\u003E\u003Cp\u003ESoroosh Shafiee is an assistant professor in the School of Operations Research and Information Engineering at Cornell University. Before that, he held positions as a postdoctoral researcher at both the Tepper School of Business at Carnegie Mellon University and the Automatic Control Laboratory at ETH Zurich. He held a B.Sc. and M.Sc. degree in Electrical Engineering from the University of Tehran and a Ph.D. degree in Operations Research from EPFL. His primary research interests revolve around data-driven optimization, low-complexity decision-making and optimal transport.\u0026nbsp;\u003Cbr\u003E\u003Cbr\u003E\u0026nbsp;\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Ch3\u003E\u003Cstrong\u003EAbstract:\u003C\/strong\u003E\u0026nbsp;\u003C\/h3\u003E\u003Cp\u003EWe study learning in an adversarial setting, where an epsilon fraction of samples from a distribution P are globally corrupted (arbitrarily modified), and the remaining perturbations have an average magnitude bounded by rho (local corruptions). With access to n such corrupted samples, we aim to develop a computationally efficient approach that achieves the optimal minimax excess risk. Our approach combines a data-driven cleaning module with a distributionally robust optimization (DRO) framework. We demonstrate that if the data cleaning module is minimax optimal with respect to the Wasserstein loss, solving an optimal transport-based DRO problem ensures a minimax optimal decision. We further provide tractable reformulations for both modules. Specifically, we introduce an optimal filtering algorithm to clean corrupted data by identifying and removing outliers. For the DRO module, we reformulate the problem as a two-player zero-sum game, deriving finite convex formulations. We show that the minimax theorem applies to this game, and Nash equilibria exist. Finally, we present a principled approach for constructing adversarial examples.\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Learning with Local and Global Adversarial Corruptions"}],"uid":"34977","created_gmt":"2025-01-28 12:50:35","changed_gmt":"2025-01-28 12:57:00","author":"Julie Smith","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2025-02-18T11:00:00-05:00","event_time_end":"2025-02-18T12:00:00-05:00","event_time_end_last":"2025-02-18T12:00:00-05:00","gmt_time_start":"2025-02-18 16:00:00","gmt_time_end":"2025-02-18 17:00:00","gmt_time_end_last":"2025-02-18 17:00:00","rrule":null,"timezone":"America\/New_York"},"location":"ISyE Main 228","extras":[],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"177814","name":"Postdoc"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"679949":{"#nid":"679949","#data":{"type":"event","title":"ISYE Statistics Seminar - Zhaohui Qin","body":[{"value":"\u003Cp\u003ETitle:\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EA novel association study framework powered by machine learning\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EAbstract:\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EGenome-wide association studies (GWASs) have been widely applied to discover genetic variants associated with a diverse array of traits. So far, almost all GWASs conducted in neuroimaging genetics are performed on image-derived quantitative features, which are univariate. In this study, we proposed and implemented a novel machine learning strategy for systematically identifying variants that lead to detectable discrepancies on the full-frame brain images. When applied to data collected by the Alzheimer\u2019s Disease Neuroimaging Initiative (ADNI) consortium, we are able to identify novel variants that show strong association with brain phenotypes.\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EAbstract\u003C\/p\u003E\u003Cp\u003EGenome-wide association studies (GWASs) have been widely applied to discover genetic variants associated with a diverse array of traits. So far, almost all GWASs conducted in neuroimaging genetics are performed on image-derived quantitative features, which are univariate. In this study, we proposed and implemented a novel machine learning strategy for systematically identifying variants that lead to detectable discrepancies on the full-frame brain images. When applied to data collected by the Alzheimer\u2019s Disease Neuroimaging Initiative (ADNI) consortium, we are able to identify novel variants that show strong association with brain phenotypes.\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"A novel association study framework powered by machine learning"}],"uid":"36433","created_gmt":"2025-01-24 18:24:47","changed_gmt":"2025-01-24 18:27:09","author":"mrussell89","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2025-04-15T11:00:00-04:00","event_time_end":"2025-04-15T12:00:00-04:00","event_time_end_last":"2025-04-15T12:00:00-04:00","gmt_time_start":"2025-04-15 15:00:00","gmt_time_end":"2025-04-15 16:00:00","gmt_time_end_last":"2025-04-15 16:00:00","rrule":null,"timezone":"America\/New_York"},"location":"Groseclose 402","extras":[],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"177814","name":"Postdoc"},{"id":"78771","name":"Public"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"679849":{"#nid":"679849","#data":{"type":"event","title":"ISyE Seminar - Elad Romanov","body":[{"value":"\u003Ch3\u003E\u003Cstrong\u003ETitle:\u0026nbsp;\u003C\/strong\u003E\u003C\/h3\u003E\u003Cp\u003EOn Principal Component Regression in High Dimension\u003C\/p\u003E\u003Ch3\u003E\u003Cstrong\u003EAbstract:\u003C\/strong\u003E\u003C\/h3\u003E\u003Cp\u003EPrincipal component regression (PCR) is a classical two-step approach to linear regression, where one first reduces the data dimension by projecting onto its leading principal components, and then performs ordinary least squares regression. We study PCR in an asymptotic high-dimensional regression setting, where the number of data points is proportional to the dimension. Our main deliverables are asymptotically exact limiting formulas for the estimation and prediction risks, which depend in a nuanced way on the eigenvalues of the population covariance, the alignment between the population principal components and the true signal, and the number of selected components.\u003C\/p\u003E\u003Cp\u003EA key challenge in the high-dimensional regime is that the sample covariance matrix is an inconsistent estimate of its population counterpart, and thus sample principal components may fail to capture potential latent low-dimensional structure in the data. We demonstrate this point through several case studies, including that of a spiked covariance matrix. The analysis of (random design) linear regression in high dimension typically builds on powerful results from random matrix theory, such as the Marchenko\u2013Pastur law and deterministic equivalents for the resolvent of a sample covariance matrix. However, these standard tools alone are not sufficient for analyzing the prediction risk of PCR. To that end, we leverage and develop somewhat less standard techniques, which, to our knowledge, have not seen wide use in the statistics literature to date: multi-resolvent traces and their associated eigenvector overlap measures.\u003C\/p\u003E\u003Cp\u003EBased on joint work with Alden Green (Stanford).\u003C\/p\u003E\u003Cp\u003E\u003Ca href=\u0022https:\/\/arxiv.org\/abs\/2405.11676\u0022\u003Ehttps:\/\/arxiv.org\/abs\/2405.11676\u003C\/a\u003E\u003C\/p\u003E\u003Ch3\u003E\u003Cstrong\u003EBio:\u003C\/strong\u003E\u003C\/h3\u003E\u003Cp\u003EElad Romanov is a postdoctoral researcher in the Department of Statistics, Stanford, where he is hosted by Prof. David Donoho. Prior to that, he completed his PhD in the School of Computer Science, the Hebrew University of Jerusalem, where he was advised by Profs. Or Ordentlich and Matan Gavish. His research interests broadly span high-dimensional statistics, information theory and signal processing, and the mathematics of data science.\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Ch3\u003E\u003Cstrong\u003EAbstract:\u003C\/strong\u003E\u003C\/h3\u003E\u003Cp\u003EPrincipal component regression (PCR) is a classical two-step approach to linear regression, where one first reduces the data dimension by projecting onto its leading principal components, and then performs ordinary least squares regression. We study PCR in an asymptotic high-dimensional regression setting, where the number of data points is proportional to the dimension. Our main deliverables are asymptotically exact limiting formulas for the estimation and prediction risks, which depend in a nuanced way on the eigenvalues of the population covariance, the alignment between the population principal components and the true signal, and the number of selected components.\u003C\/p\u003E\u003Cp\u003EA key challenge in the high-dimensional regime is that the sample covariance matrix is an inconsistent estimate of its population counterpart, and thus sample principal components may fail to capture potential latent low-dimensional structure in the data. We demonstrate this point through several case studies, including that of a spiked covariance matrix. The analysis of (random design) linear regression in high dimension typically builds on powerful results from random matrix theory, such as the Marchenko\u2013Pastur law and deterministic equivalents for the resolvent of a sample covariance matrix. However, these standard tools alone are not sufficient for analyzing the prediction risk of PCR. To that end, we leverage and develop somewhat less standard techniques, which, to our knowledge, have not seen wide use in the statistics literature to date: multi-resolvent traces and their associated eigenvector overlap measures.\u003C\/p\u003E\u003Cp\u003EBased on joint work with Alden Green (Stanford).\u003C\/p\u003E\u003Cp\u003E\u003Ca href=\u0022https:\/\/arxiv.org\/abs\/2405.11676\u0022\u003Ehttps:\/\/arxiv.org\/abs\/2405.11676\u003C\/a\u003E\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"On Principal Component Regression in High Dimension"}],"uid":"34977","created_gmt":"2025-01-23 13:04:12","changed_gmt":"2025-01-23 13:06:05","author":"Julie Smith","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2025-02-11T11:00:00-05:00","event_time_end":"2025-02-11T12:00:00-05:00","event_time_end_last":"2025-02-11T12:00:00-05:00","gmt_time_start":"2025-02-11 16:00:00","gmt_time_end":"2025-02-11 17:00:00","gmt_time_end_last":"2025-02-11 17:00:00","rrule":null,"timezone":"America\/New_York"},"location":"ISyE Main 228","extras":[],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"177814","name":"Postdoc"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"679688":{"#nid":"679688","#data":{"type":"event","title":"ISyE Seminar - Philip Ernst","body":[{"value":"\u003Ch3\u003E\u003Cstrong\u003ETitle\u003C\/strong\u003E:\u0026nbsp;\u003C\/h3\u003E\u003Cp\u003EYule\u2019s \u201cnonsense correlation\u201d: Moments and density.\u003C\/p\u003E\u003Ch3\u003E\u003Cstrong\u003EAbstract\u003C\/strong\u003E:\u0026nbsp;\u003C\/h3\u003E\u003Cp\u003EIn 1926, G. Udny Yule considered the following problem: given two independent and identically distributed random walks independent from each other, what is the distribution of their empirical correlation coefficient? Yule empirically observed the distribution of this statistic to be heavily dispersed and frequently large in absolute value, leading him to call it \u201cnonsense correlation.\u0027\u0027 This unexpected finding led to his formulation of two concrete questions, each of which would remain open for more than ninety years: (i) Find (analytically) the variance of this empirical correlation coefficient and (ii): Find (analytically) the higher order moments and the density of this empirical correlation coefficient. After giving a brief overview of the solution to question (i) in Ernst et al. (\u003Cem\u003EThe Annals of Statistics\u003C\/em\u003E, 2017), we turn to the recent work of Ernst et al. (\u003Cem\u003EBernoulli,\u003C\/em\u003E\u0026nbsp;2025), which closed question (ii) by explicitly calculating all moments of the empirical correlation coefficient (up to order 16). This leads, for the first time, to an approximation to the density of Yule\u0027s nonsense correlation. The methodology of Ernst et al. (2025) further enables explicit calculations of the moments of the empirical correlation coefficient when the two independent Wiener processes are replaced by two correlated Wiener processes, two independent Ornstein-Uhlenbeck processes, and two independent Brownian bridges. We also succeed in proving a Central Limit Theorem for the case of two independent Ornstein-Uhlenbeck processes. This shows that Yule\u0027s \u201cnonsense correlation\u201d is indeed not \u201cnonsense\u201d for stochastic processes which admit stationary distributions. The talk concludes with a discussion of some concrete applications of our work to the study of weather and climate extremes. The latter is part of our ongoing collaboration with the U.S. Office of Naval Research (2018-present).\u003C\/p\u003E\u003Ch3\u003E\u003Cstrong\u003EBio:\u0026nbsp;\u003C\/strong\u003E\u003C\/h3\u003E\u003Cp\u003EPhilip Ernst is Chair (and Full Professor) in Statistics and Royal Society Wolfson Fellow at Imperial College London. He was previously an Assistant Professor (2014-2018), an Associate Professor (2019-2022), and a Full Professor (2022-2023), all at Rice University\u2019s Department of Statistics. His research lies at the interface of applied probability and operations research. His work has been funded by the U.S. Office of Naval Research (ONR), the U.S. Army Research Office (ARO), the National Science Foundation (NSF), The Royal Society, and The British Academy. Ernst is the recipient of numerous international and national research awards, including: a 2026 Institute of Mathematical Statistics (IMS) Medallion Award \u0026amp; Lecture, a 2023 Henri Lebesgue Chair, a 2023 British Academy\/Wolfson Fellowship, a 2022 Committee of Presidents of Statistical Societies (COPSS) Emerging Leader Award, the 2020 (inaugural) INFORMS Donald P. Gaver, Jr. Early Career Award for Excellence in Operations Research, a 2018 U.S. Army Research Office (ARO) Young Investigator Award, and the 2018 Institute of Mathematical Statistics (IMS) Tweedie New Researcher Award. Ernst is also highly invested in teaching; he won seven teaching awards in the eight years he was employed at Rice University (including the George R. Brown Prize for Excellence in Teaching, Rice University\u2019s most prestigious teaching award). He currently serves as an associate editor for six journals: \u003Cem\u003EJournal of Stochastic Analysis\u003C\/em\u003E, \u003Cem\u003EJournal of the American Statistical Association: Theory and Methods,\u003C\/em\u003E\u0026nbsp;\u003Cem\u003EMathematics of Operations Research\u003C\/em\u003E, \u003Cem\u003EStatistics and Probability Letters\u003C\/em\u003E, \u003Cem\u003EStochastics\u003C\/em\u003E, and \u003Cem\u003EThe American Statistician\u003C\/em\u003E. He is also an elected member of IMS Council (2024-2027).\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Ch3\u003EAbstract:\u0026nbsp;\u003C\/h3\u003E\u003Cp\u003EIn 1926, G. Udny Yule considered the following problem: given two independent and identically distributed random walks independent from each other, what is the distribution of their empirical correlation coefficient? Yule empirically observed the distribution of this statistic to be heavily dispersed and frequently large in absolute value, leading him to call it \u201cnonsense correlation.\u0027\u0027 This unexpected finding led to his formulation of two concrete questions, each of which would remain open for more than ninety years: (i) Find (analytically) the variance of this empirical correlation coefficient and (ii): Find (analytically) the higher order moments and the density of this empirical correlation coefficient. After giving a brief overview of the solution to question (i) in Ernst et al. (\u003Cem\u003EThe Annals of Statistics\u003C\/em\u003E, 2017), we turn to the recent work of Ernst et al. (\u003Cem\u003EBernoulli,\u003C\/em\u003E\u0026nbsp;2025), which closed question (ii) by explicitly calculating all moments of the empirical correlation coefficient (up to order 16). This leads, for the first time, to an approximation to the density of Yule\u0027s nonsense correlation. The methodology of Ernst et al. (2025) further enables explicit calculations of the moments of the empirical correlation coefficient when the two independent Wiener processes are replaced by two correlated Wiener processes, two independent Ornstein-Uhlenbeck processes, and two independent Brownian bridges. We also succeed in proving a Central Limit Theorem for the case of two independent Ornstein-Uhlenbeck processes. This shows that Yule\u0027s \u201cnonsense correlation\u201d is indeed not \u201cnonsense\u201d for stochastic processes which admit stationary distributions. The talk concludes with a discussion of some concrete applications of our work to the study of weather and climate extremes. The latter is part of our ongoing collaboration with the U.S. Office of Naval Research (2018-present).\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Yule\u2019s \u201cnonsense correlation\u201d: Moments and density."}],"uid":"34977","created_gmt":"2025-01-17 14:00:02","changed_gmt":"2025-01-17 14:02:50","author":"Julie Smith","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2025-02-03T11:00:00-05:00","event_time_end":"2025-02-03T12:00:00-05:00","event_time_end_last":"2025-02-03T12:00:00-05:00","gmt_time_start":"2025-02-03 16:00:00","gmt_time_end":"2025-02-03 17:00:00","gmt_time_end_last":"2025-02-03 17:00:00","rrule":null,"timezone":"America\/New_York"},"location":"ISyE Groseclose 402","extras":[],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"177814","name":"Postdoc"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"679243":{"#nid":"679243","#data":{"type":"event","title":"SCL Lunch and Learn: \u201cBuilding Resilience: Strategies for Effective Supply Chain Risk Management\u0022","body":[{"value":"\u003Cp\u003E\u003Cstrong\u003EJoin us for an in-depth webinar on the essential strategies for identifying, assessing, and mitigating risks within your supply chain.\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EThursday, March 6, 2025 | 12-1pm ET\u003C\/strong\u003E\u003C\/p\u003E\u003Cdiv\u003E\u003Cdiv\u003E\u003Cdiv\u003E\u003Cdiv\u003E\u003Cdiv\u003E\u003Cdiv\u003EWe\u0027ll cover key topics such as managing disruptions, understanding the impact of geopolitical events, and developing robust contingency plans to ensure supply chain continuity. Gain valuable insights into how to proactively protect your operations from unforeseen challenges and keep your supply chain resilient in today\u2019s dynamic environment.\u003C\/div\u003E\u003C\/div\u003E\u003C\/div\u003E\u003C\/div\u003E\u003C\/div\u003E\u003Cdiv\u003E\u0026nbsp;\u003C\/div\u003E\u003C\/div\u003E\u003Cp\u003E\u003Ca href=\u0022https:\/\/www.scl.gatech.edu\/mar25-lnl\u0022\u003E\u003Cstrong\u003ERegister Online to Attend\u003C\/strong\u003E\u003C\/a\u003E\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EJoin us for an in-depth webinar on the essential strategies for identifying, assessing, and mitigating risks within your supply chain.\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Join us for an in-depth webinar on the essential strategies for identifying, assessing, and mitigating risks within your supply chain."}],"uid":"27233","created_gmt":"2025-01-08 00:21:34","changed_gmt":"2025-01-08 00:39:53","author":"Andy Haleblian","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2025-03-06T12:00:00-05:00","event_time_end":"2025-03-06T13:00:00-05:00","event_time_end_last":"2025-03-06T13:00:00-05:00","gmt_time_start":"2025-03-06 17:00:00","gmt_time_end":"2025-03-06 18:00:00","gmt_time_end_last":"2025-03-06 18:00:00","rrule":null,"timezone":"America\/New_York"},"location":"Online via Zoom","extras":[],"hg_media":{"675963":{"id":"675963","type":"image","title":"SCL Lunch and Learn: \u0022Building Resilience: Strategies for Effective Supply Chain Risk Management\u0022","body":null,"created":"1736296492","gmt_created":"2025-01-08 00:34:52","changed":"1736296492","gmt_changed":"2025-01-08 00:34:52","alt":"Building Resilience: Strategies for Effective Supply Chain Risk Management","file":{"fid":"259650","name":"SCRM.png","image_path":"\/sites\/default\/files\/2025\/01\/07\/SCRM.png","image_full_path":"http:\/\/hg.gatech.edu\/\/sites\/default\/files\/2025\/01\/07\/SCRM.png","mime":"image\/png","size":174316,"path_740":"http:\/\/hg.gatech.edu\/sites\/default\/files\/styles\/740xx_scale\/public\/2025\/01\/07\/SCRM.png?itok=Gcu9A0cx"}}},"media_ids":["675963"],"related_links":[{"url":"https:\/\/www.scl.gatech.edu\/mar25-lnl","title":"Register Online to Attend the Webinar"},{"url":"https:\/\/www.scl.gatech.edu\/education\/professional-education","title":"Professional Education at the Georgia Tech Supply Chain and Logistics Institute"}],"groups":[{"id":"1250","name":"Center for Health and Humanitarian Systems (CHHS)"},{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"},{"id":"1243","name":"The Supply Chain and Logistics Institute (SCL)"}],"categories":[],"keywords":[{"id":"3615","name":"information session"},{"id":"186857","name":"go-gtmi"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"10377","name":"Career\/Professional development"}],"invited_audience":[{"id":"78771","name":"Public"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[{"value":"\u003Cp\u003Ecourse@scl.gatech.edu\u003C\/p\u003E","format":"limited_html"}],"email":[],"slides":[],"orientation":[],"userdata":""}},"679231":{"#nid":"679231","#data":{"type":"event","title":"ISyE Seminar - Timothy Chan","body":[{"value":"\u003Cp\u003ETitle:\u003Cbr\u003EGot (Optimal) Milk? Pooling Donations in Human Milk Banks with Machine Learning and Optimization\u003Cbr\u003E\u003Cem\u003ETimothy C. Y. Chan , Rafid Mahmood , Deborah L. O\u2019Connor , Debbie Stone, Sharon Unger , Rachel K. Wong, Ian Yihang Zhu\u003C\/em\u003E\u003C\/p\u003E\u003Cp\u003EAbstract:\u003Cbr\u003EProblem definition: Human donor milk provides critical nutrition for millions of infants who are born preterm each year. Donor milk is collected, processed, and distributed by milk banks. The macronutrient content of donor milk is directly linked to infant brain development and can vary substantially across donations, which is why multiple donations are typically pooled together to create a final product. Approximately half of all milk banks in North America do not have the resources to measure the macronutrient content of donor milk, which means pooling is done heuristically. For these milk banks, an approach is needed to optimize pooling decisions. Methodology\/results: We propose a data-driven framework combining machine learning and optimization to predict macronutrient content of donations and then optimally combine them in pools, respectively. In collaboration with our partner milk bank, we collect a data set of milk to train our predictive models. We rigorously simulate milk bank practices to fine-tune our optimization models and evaluate operational scenarios such as changes in donation habits during the COVID-19 pandemic. Finally, we conduct a year-long trial implementation, where we observe the current nurse-led pooling practices followed by our intervention. Pools created by our approach meet clinical macronutrient targets approximately 31% more often than the baseline, although taking 60% less recipe creation time. Managerial implications: This is the first paper in the broader blending literature that combines machine learning and optimization. We demonstrate that such pipelines are feasible to implement in a healthcare setting and can yield significant improvements over current practices. Our insights can guide practitioners in any application area seeking to implement machine learning and optimization-based decision support.\u003C\/p\u003E\u003Cp\u003EBio:\u003Cbr\u003ETimothy Chan is the Associate Vice-President and Vice-Provost, Strategic Initiatives at the University of Toronto, the Canada Research Chair in Novel Optimization and Analytics in Health, a Professor in the department of Mechanical and Industrial Engineering, and a Senior Fellow of Massey College. He was previously Director of the Centre for Healthcare Engineering, Director of the Centre for Analytics and AI Engineering, and Associate Director, Research and Thematic Programming, of the Data Sciences Institute. His primary research interests are in operations research, optimization, and applied machine learning, with applications in healthcare, medicine, sustainability, and sports.\u003C\/p\u003E\u003Cp\u003EProfessor Chan received his B.Sc. in Applied Mathematics from the University of British Columbia (2002), and his Ph.D. in Operations Research from the Massachusetts Institute of Technology (2007). Before coming to Toronto, he was an Associate in the Chicago office of McKinsey and Company (2007-2009), a global management consulting firm. During that time, he advised leading companies in the fields of medical device technology, travel and hospitality, telecommunications, and energy on issues of strategy, organization, technology and operations.\u003C\/p\u003E\u003Cp\u003EProfessor Chan currently holds editorial roles in seven academic journals, including Operations Research, Management Science, and M\u0026amp;SOM. He has served in a variety of leadership and service roles at INFORMS and CORS, including as President of the INFORMS Health Application Society. He has over 120 publications in refereed journals, and is co-author of an upcoming book entitled \u201cIntroduction to Markov Decision Processes\u201d. He has graduated over 50 graduate students and postdoctoral fellows, and takes great pride in cultivating a healthy, inclusive, and productive lab environment.\u003C\/p\u003E\u003Cp\u003EProfessor Chan has received numerous awards and honours for his research, teaching and service. Recent highlights include the President\u2019s Teaching Award from the University of Toronto in 2024, 1st place in the research paper competition at the MIT Sloan Sports Analytics Conference in 2024, the INFORMS Prize for Teaching OR\/MS Practice in 2023, the Pierskalla Best Paper Award from INFORMS Health Applications Society in 2023, 1st place in the INFORMS Case Competition in 2022, and the CORS Eldon Gunn Service Award in 2022. His research has been featured by the CBC, CTV News, Global News, Reuters, CNN, the Globe and Mail, the Toronto Star, Boston Globe, ESPN, Canadian Business Magazine, and World Economic Forum.\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EProblem definition: Human donor milk provides critical nutrition for millions of infants who are born preterm each year. Donor milk is collected, processed, and distributed by milk banks. The macronutrient content of donor milk is directly linked to infant brain development and can vary substantially across donations, which is why multiple donations are typically pooled together to create a final product. Approximately half of all milk banks in North America do not have the resources to measure the macronutrient content of donor milk, which means pooling is done heuristically. For these milk banks, an approach is needed to optimize pooling decisions. Methodology\/results: We propose a data-driven framework combining machine learning and optimization to predict macronutrient content of donations and then optimally combine them in pools, respectively. In collaboration with our partner milk bank, we collect a data set of milk to train our predictive models. We rigorously simulate milk bank practices to fine-tune our optimization models and evaluate operational scenarios such as changes in donation habits during the COVID-19 pandemic. Finally, we conduct a year-long trial implementation, where we observe the current nurse-led pooling practices followed by our intervention. Pools created by our approach meet clinical macronutrient targets approximately 31% more often than the baseline, although taking 60% less recipe creation time. Managerial implications: This is the first paper in the broader blending literature that combines machine learning and optimization. We demonstrate that such pipelines are feasible to implement in a healthcare setting and can yield significant improvements over current practices. Our insights can guide practitioners in any application area seeking to implement machine learning and optimization-based decision support.\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Got (Optimal) Milk? Pooling Donations in Human Milk Banks with Machine Learning and Optimization"}],"uid":"36374","created_gmt":"2025-01-07 18:54:17","changed_gmt":"2025-01-07 19:00:14","author":"mwelch39","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2025-02-21T11:30:00-05:00","event_time_end":"2025-02-21T12:30:00-05:00","event_time_end_last":"2025-02-21T12:30:00-05:00","gmt_time_start":"2025-02-21 16:30:00","gmt_time_end":"2025-02-21 17:30:00","gmt_time_end_last":"2025-02-21 17:30:00","rrule":null,"timezone":"America\/New_York"},"location":" ISYE Groseclose 402","extras":[],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"177814","name":"Postdoc"},{"id":"78771","name":"Public"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"679180":{"#nid":"679180","#data":{"type":"event","title":"ISyE Seminar - Yuchen Wu","body":[{"value":"\u003Ch3\u003ETitle:\u0026nbsp;\u003C\/h3\u003E\u003Cp\u003EModern Sampling Paradigms: from Posterior Sampling to Generative AI\u003C\/p\u003E\u003Ch3\u003E\u003Cstrong\u003EAbstract:\u0026nbsp;\u003C\/strong\u003E\u003C\/h3\u003E\u003Cp\u003ESampling from a target distribution is a recurring theme in statistics and generative artificial intelligence (AI). In statistics, posterior sampling offers a flexible inferential framework, enabling uncertainty quantification, probabilistic prediction, as well as the estimation of intractable quantities. In generative AI, sampling aims to generate unseen instances that emulate a target population, such as the natural distributions of texts, images, and molecules.\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EIn this talk, I will present my works on designing provably efficient sampling algorithms, addressing challenges in both statistics and generative AI. (1) In the first part, I will focus on posterior sampling for Bayes sparse regression. In general, such posteriors are high-dimensional and contain many modes, making them challenging to sample from. To address this, we develop a novel sampling algorithm based on decomposing the target posterior into a log-concave mixture of simple distributions, reducing sampling from a complex distribution to sampling from a tractable log-concave one. We establish provable guarantees for our method in a challenging regime that was previously intractable. (2) In the second part, I will describe a training-free acceleration method for diffusion models, which are deep generative models that underpin cutting-edge applications such as AlphaFold, DALL-E and Sora. Our approach is simple to implement, wraps around any pre-trained diffusion model, and comes with a provable convergence rate that strengthens prior theoretical results. We demonstrate the effectiveness of our method on several real-world image generation tasks.\u0026nbsp;\u003C\/p\u003E\u003Cp\u003ELastly, I will outline my vision for bridging the fields of statistics and generative AI, exploring how insights from one domain can drive progress in the other.\u003C\/p\u003E\u003Ch3\u003E\u003Cstrong\u003EBio:\u003C\/strong\u003E\u0026nbsp;\u003C\/h3\u003E\u003Cp\u003EYuchen Wu is a departmental postdoctoral researcher in the\u0026nbsp;Department of Statistics and Data Science\u0026nbsp;at the Wharton School, University of Pennsylvania.\u0026nbsp;She earned her Ph.D. in 2023 from Stanford University, where she was advised by Professor Andrea Montanari. Her research lies broadly at the intersection of statistics and machine learning, featuring generative AI, high-dimensional statistics, Bayesian inference, algorithm design, and data-driven decision making.\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Ch3\u003EAbstract:\u0026nbsp;\u003C\/h3\u003E\u003Cp\u003ESampling from a target distribution is a recurring theme in statistics and generative artificial intelligence (AI). In statistics, posterior sampling offers a flexible inferential framework, enabling uncertainty quantification, probabilistic prediction, as well as the estimation of intractable quantities. In generative AI, sampling aims to generate unseen instances that emulate a target population, such as the natural distributions of texts, images, and molecules.\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EIn this talk, I will present my works on designing provably efficient sampling algorithms, addressing challenges in both statistics and generative AI. (1) In the first part, I will focus on posterior sampling for Bayes sparse regression. In general, such posteriors are high-dimensional and contain many modes, making them challenging to sample from. To address this, we develop a novel sampling algorithm based on decomposing the target posterior into a log-concave mixture of simple distributions, reducing sampling from a complex distribution to sampling from a tractable log-concave one. We establish provable guarantees for our method in a challenging regime that was previously intractable. (2) In the second part, I will describe a training-free acceleration method for diffusion models, which are deep generative models that underpin cutting-edge applications such as AlphaFold, DALL-E and Sora. Our approach is simple to implement, wraps around any pre-trained diffusion model, and comes with a provable convergence rate that strengthens prior theoretical results. We demonstrate the effectiveness of our method on several real-world image generation tasks.\u0026nbsp;\u003C\/p\u003E\u003Cp\u003ELastly, I will outline my vision for bridging the fields of statistics and generative AI, exploring how insights from one domain can drive progress in the other.\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Modern Sampling Paradigms: from Posterior Sampling to Generative AI"}],"uid":"34977","created_gmt":"2025-01-06 15:28:11","changed_gmt":"2025-01-06 15:29:48","author":"Julie Smith","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2025-02-13T11:00:00-05:00","event_time_end":"2025-02-13T12:00:00-05:00","event_time_end_last":"2025-02-13T12:00:00-05:00","gmt_time_start":"2025-02-13 16:00:00","gmt_time_end":"2025-02-13 17:00:00","gmt_time_end_last":"2025-02-13 17:00:00","rrule":null,"timezone":"America\/New_York"},"location":"ISyE Groseclose 402","extras":[],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"177814","name":"Postdoc"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"679176":{"#nid":"679176","#data":{"type":"event","title":"ISyE Seminar - Yuetian Luo","body":[{"value":"\u003Ch3\u003ETitle:\u003C\/h3\u003E\u003Cp\u003EChallenges and Opportunities in Assumption-free and Robust Inference\u003C\/p\u003E\u003Ch3\u003EAbstract:\u0026nbsp;\u003C\/h3\u003E\u003Cp\u003EWith the growing application of data science to complex high-stakes tasks, ensuring the reliability of statistical inference methods has become increasingly critical. This talk considers two key challenges to achieving this goal: model misspecification and data corruption, highlighting their associated difficulties and potential solutions. In the first part, we investigate the problem of distribution-free algorithm risk evaluation, uncovering fundamental limitations for answering these questions with limited amounts of data. To navigate the challenge, we will also discuss how incorporating an assumption about algorithmic stability might help. The second part focuses on constructing robust confidence intervals in the presence of arbitrary data contamination. We show that when the proportion of contamination is unknown, uncertainty quantification incurs a substantial cost, resulting in optimal robust confidence intervals that must be significantly wider.\u003C\/p\u003E\u003Ch3\u003EShort bio:\u003C\/h3\u003E\u003Cp\u003EYuetian Luo is a postdoctoral scholar in the Data Science Institute at the University of Chicago advised by Professor Rina Foygel Barber. He received his Ph.D. in Statistics from the University of Wisconsin-Madison under the supervision of Professor Anru Zhang. His research interests lie broadly in distribution-free inference, computational complexity of statistical inference, tensor learning, robust statistics, and non-convex optimization.\u0026nbsp;\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Ch3\u003EAbstract:\u0026nbsp;\u003C\/h3\u003E\u003Cp\u003EWith the growing application of data science to complex high-stakes tasks, ensuring the reliability of statistical inference methods has become increasingly critical. This talk considers two key challenges to achieving this goal: model misspecification and data corruption, highlighting their associated difficulties and potential solutions. In the first part, we investigate the problem of distribution-free algorithm risk evaluation, uncovering fundamental limitations for answering these questions with limited amounts of data. To navigate the challenge, we will also discuss how incorporating an assumption about algorithmic stability might help. The second part focuses on constructing robust confidence intervals in the presence of arbitrary data contamination. We show that when the proportion of contamination is unknown, uncertainty quantification incurs a substantial cost, resulting in optimal robust confidence intervals that must be significantly wider.\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Challenges and Opportunities in Assumption-free and Robust Inference"}],"uid":"34977","created_gmt":"2025-01-06 15:23:31","changed_gmt":"2025-01-06 15:24:46","author":"Julie Smith","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2025-02-06T11:00:00-05:00","event_time_end":"2025-02-06T12:00:00-05:00","event_time_end_last":"2025-02-06T12:00:00-05:00","gmt_time_start":"2025-02-06 16:00:00","gmt_time_end":"2025-02-06 17:00:00","gmt_time_end_last":"2025-02-06 17:00:00","rrule":null,"timezone":"America\/New_York"},"location":"ISyE Groseclose 402","extras":[],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"177814","name":"Postdoc"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"679175":{"#nid":"679175","#data":{"type":"event","title":"ISyE Seminar - Shixin Wang","body":[{"value":"\u003Ch3\u003ETitle:\u0026nbsp;\u003C\/h3\u003E\u003Cp\u003ESimple menus in robust screening\u003C\/p\u003E\u003Ch3\u003EAbstract:\u0026nbsp;\u003C\/h3\u003E\u003Cp\u003EThis talk investigates the design and effectiveness of simple selling mechanisms when a seller has only partial information about a buyer\u2019s valuation distribution, obtained through market research or price experimentation. While robust screening offers stronger guarantees compared to deterministic pricing, it often involves complex menus with infinitely many options, posing implementation challenges. Our research introduces simple mechanisms with finite menus that balance performance guarantees with practical implementation. Using a unified framework for various ambiguity sets\u2014including support, mean, and quantile\u2014we derive optimal mechanisms and performance ratios for different menu sizes. Our findings reveal that modest menu sizes can closely approximate the benefits of optimal infinite-menu mechanisms. Remarkably, even a two-option menu significantly outperforms deterministic pricing.\u003C\/p\u003E\u003Cp\u003EWe extend our results to multi-item mechanism design, where optimal mechanisms are complicated even with full knowledge of buyers\u2019 valuation distributions. To address this challenge, we propose \u201csemi-separable mechanisms,\u201d where each item\u0027s allocation and payment rules depend only on its valuation and joint distributional information, but not on the valuations of other items. We prove that semi-separable mechanisms achieve the optimal performance ratio among all incentive-compatible and individually rational mechanisms when only marginal support information is available. Additionally, our framework accommodates settings where sellers possess aggregate valuation information for product bundles, further enhancing its practical applicability.\u003C\/p\u003E\u003Ch3\u003EBio:\u0026nbsp;\u003C\/h3\u003E\u003Cp\u003EShixin Wang is an Assistant Professor in the Department of Decisions, Operations and Technology at The Chinese University of Hong Kong. Before joining CUHK, she earned her Ph.D. in Operations Management from NYU Stern School of Business and a bachelor\u2019s degree in Industrial Engineering from Tsinghua University. Her research focuses on developing simple, robust pricing policies in revenue management and designing sparse, reliable networks for supply chain and service systems. Her work has been recognized as a finalist in the INFORMS JFIG Paper Competition and the INFORMS Service Science Best Cluster Paper Award. Her research has been supported by funding from Hong Kong Research Grants Council (RGC).\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Ch3\u003EAbstract:\u0026nbsp;\u003C\/h3\u003E\u003Cp\u003EThis talk investigates the design and effectiveness of simple selling mechanisms when a seller has only partial information about a buyer\u2019s valuation distribution, obtained through market research or price experimentation. While robust screening offers stronger guarantees compared to deterministic pricing, it often involves complex menus with infinitely many options, posing implementation challenges. Our research introduces simple mechanisms with finite menus that balance performance guarantees with practical implementation. Using a unified framework for various ambiguity sets\u2014including support, mean, and quantile\u2014we derive optimal mechanisms and performance ratios for different menu sizes. Our findings reveal that modest menu sizes can closely approximate the benefits of optimal infinite-menu mechanisms. Remarkably, even a two-option menu significantly outperforms deterministic pricing.\u003C\/p\u003E\u003Cp\u003EWe extend our results to multi-item mechanism design, where optimal mechanisms are complicated even with full knowledge of buyers\u2019 valuation distributions. To address this challenge, we propose \u201csemi-separable mechanisms,\u201d where each item\u0027s allocation and payment rules depend only on its valuation and joint distributional information, but not on the valuations of other items. We prove that semi-separable mechanisms achieve the optimal performance ratio among all incentive-compatible and individually rational mechanisms when only marginal support information is available. Additionally, our framework accommodates settings where sellers possess aggregate valuation information for product bundles, further enhancing its practical applicability.\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Simple menus in robust screening"}],"uid":"34977","created_gmt":"2025-01-06 15:18:40","changed_gmt":"2025-01-06 15:20:21","author":"Julie Smith","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2025-01-30T11:00:00-05:00","event_time_end":"2025-01-30T12:00:00-05:00","event_time_end_last":"2025-01-30T12:00:00-05:00","gmt_time_start":"2025-01-30 16:00:00","gmt_time_end":"2025-01-30 17:00:00","gmt_time_end_last":"2025-01-30 17:00:00","rrule":null,"timezone":"America\/New_York"},"location":"ISyE Groseclose 402","extras":[],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"177814","name":"Postdoc"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"679048":{"#nid":"679048","#data":{"type":"event","title":"ISyE Seminar - J. Carlos Mart\u00ednez Mori ","body":[{"value":"\u003Ch3\u003ETitle:\u003Cstrong\u003E\u0026nbsp;\u003C\/strong\u003E\u003C\/h3\u003E\u003Cp\u003ECooperation and the Design of Public Goods\u003C\/p\u003E\u003Ch3\u003EAbstract:\u003C\/h3\u003E\u003Cp\u003EPublic transit systems face two, often conflicting design goals: ridership and coverage. The ridership goal involves serving as many people as possible, typically in high-density urban centers. Conversely, the coverage goal treats transit as a social service and measures its success by how good a service it provides to those who badly need it, including those in low-density suburban areas. And while the social significance of the coverage goal has only grown after decades of increasingly suburbanized poverty, a transit agency that takes this too far is likely to face fierce pressure from those who contribute to its resources (e.g., in the form of taxes and fare collection) but do not benefit from such a service plan. This tension speaks to a fundamental difficulty with designing public goods, including but not limited to transit, that take on a social service mission while trying to maintain broad popular support.\u003C\/p\u003E\u003Cp\u003EIn this talk, I will approach the design of public goods from the perspective of cooperative game theory. I will introduce non-transferable utility (NTU) linear production (LP) games, which combine the essential game-theoretic elements of public goods with the modeling flexibility of linear programming. I will show that under mild and interpretable conditions, designs that maintain popular support are possible. However, this result is existential: I will show that testing whether a particular design maintains popular support is co-NP-complete. I will also demonstrate how, while one can in principle write a mixed-integer linear programming formulation for the set of popular designs, this approach is vastly impractical even for simple instances, and that natural approaches to obtain a polyhedral relaxation through cutting plane methods can be insufficient. This motivates further research on optimizing over this complicated yet well-structured set. Lastly, I will tie this theory back to transit with a data-driven implementation that illustrates the impact of maintaining popular support on the distribution of quality of service for coverage-oriented transit designs.\u003C\/p\u003E\u003Ch3\u003EBio:\u003C\/h3\u003E\u003Cp\u003EJuan Carlos Mart\u00ednez Mori is a Schmidt Science Fellow and a President\u2019s Postdoctoral Fellow with the H. Milton Stewart School of Industrial and Systems Engineering at the Georgia Institute of Technology. Prior to his current appointment, he was a Postdoctoral Fellow at the Simons Laufer Mathematical Sciences Institute (SLMath, formerly MSRI) as part of their thematic program on Algorithms, Fairness, and Equity. He earned his PhD in Applied Mathematics from Cornell University in 2023 and his BSc in Civil Engineering and minor in Computer Science from the University of Illinois at Urbana-Champaign in 2017.\u003C\/p\u003E\u003Cp\u003EHis primary research interests span transportation, optimization, and game theory, with additional prior work in enumerative combinatorics and sports analytics. \u0026nbsp;As a frequent transit rider, he is interested in mathematics that support more accessible and convenient public transportation.\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Ch3\u003EAbstract:\u003C\/h3\u003E\u003Cp\u003EPublic transit systems face two, often conflicting design goals: ridership and coverage. The ridership goal involves serving as many people as possible, typically in high-density urban centers. Conversely, the coverage goal treats transit as a social service and measures its success by how good a service it provides to those who badly need it, including those in low-density suburban areas. And while the social significance of the coverage goal has only grown after decades of increasingly suburbanized poverty, a transit agency that takes this too far is likely to face fierce pressure from those who contribute to its resources (e.g., in the form of taxes and fare collection) but do not benefit from such a service plan. This tension speaks to a fundamental difficulty with designing public goods, including but not limited to transit, that take on a social service mission while trying to maintain broad popular support.\u003C\/p\u003E\u003Cp\u003EIn this talk, I will approach the design of public goods from the perspective of cooperative game theory. I will introduce non-transferable utility (NTU) linear production (LP) games, which combine the essential game-theoretic elements of public goods with the modeling flexibility of linear programming. I will show that under mild and interpretable conditions, designs that maintain popular support are possible. However, this result is existential: I will show that testing whether a particular design maintains popular support is co-NP-complete. I will also demonstrate how, while one can in principle write a mixed-integer linear programming formulation for the set of popular designs, this approach is vastly impractical even for simple instances, and that natural approaches to obtain a polyhedral relaxation through cutting plane methods can be insufficient. This motivates further research on optimizing over this complicated yet well-structured set. Lastly, I will tie this theory back to transit with a data-driven implementation that illustrates the impact of maintaining popular support on the distribution of quality of service for coverage-oriented transit designs.\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Cooperation and the Design of Public Goods"}],"uid":"34977","created_gmt":"2024-12-31 13:14:18","changed_gmt":"2024-12-31 13:17:25","author":"Julie Smith","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2025-01-28T11:00:00-05:00","event_time_end":"2025-01-28T12:00:00-05:00","event_time_end_last":"2025-01-28T12:00:00-05:00","gmt_time_start":"2025-01-28 16:00:00","gmt_time_end":"2025-01-28 17:00:00","gmt_time_end_last":"2025-01-28 17:00:00","rrule":null,"timezone":"America\/New_York"},"location":"ISyE Groseclose 402","extras":[],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"177814","name":"Postdoc"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"679003":{"#nid":"679003","#data":{"type":"event","title":"ISyE Seminar - Hui Zou","body":[{"value":"\u003Ch3\u003ETitle:\u0026nbsp;\u003C\/h3\u003E\u003Cp\u003EHigh-dimensional Clustering\u0026nbsp;via A Latent Transformation Mixture Model\u0026nbsp;\u003C\/p\u003E\u003Ch3\u003EAbstract:\u0026nbsp;\u003C\/h3\u003E\u003Cp\u003ECluster analysis is a fundamental task in machine learning. Several clustering algorithms have been extended to handle high-dimensional data by incorporating a sparsity constraint in the estimation of a mixture of Gaussian models. Though it makes some neat theoretical analysis possible, this type of approach is arguably restrictive for many applications. In this work we propose a novel latent transformation mixture model for clustering. The use of unspecified transformation makes the model much more flexible than the classical model-based clustering. Under the assumption that the optimal clustering admits a sparsity structure, we develop a new clustering algorithm named CESME for high-dimensional clustering. We offer a comprehensive analysis of CESME including identifiability, initialization, algorithmic convergence, and statistical guarantees on clustering. Extensive numerical study and real data analysis show that CESME outperforms the existing high-dimensional clustering algorithms in the literature.\u0026nbsp;\u003C\/p\u003E\u003Ch3\u003EBio:\u003C\/h3\u003E\u003Cp\u003EDr. Hui Zou is currently the Dr. Lynn Y.S. Lin Professor at the University of Minnesota. He earned his Ph.D. in Statistics from Stanford University in 2005. His primary research interests include statistical learning, high-dimensional models, statistical computing, and the application of modern statistical methods in business, health, and engineering. Dr. Zou is an elected fellow of the American Association for the Advancement of Science (AAAS), the Institute of Mathematical Statistics (IMS), and the American Statistical Association (ASA). He has published over 100 research articles, many of which are highly cited, including three that have been listed among the most-cited papers of all time in\u0026nbsp;\u003Cem\u003EJRSSB\u003C\/em\u003E,\u0026nbsp;\u003Cem\u003EJASA\u003C\/em\u003E, and\u0026nbsp;\u003Cem\u003EJCGS\u003C\/em\u003E. One of his papers in the\u0026nbsp;\u003Cem\u003EAnnals of Statistics\u003C\/em\u003E was selected as the Best Paper in Applied Mathematics at the 8th International Congress of Chinese Mathematicians (ICCM 2019). Dr. Zou has also mentored 15 PhD students, two of whom received the COPSS Leadership Academy Award for Emerging Leaders in Statistics.\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Ch3\u003EAbstract:\u003C\/h3\u003E\u003Cp\u003ECluster analysis is a fundamental task in machine learning. Several clustering algorithms have been extended to handle high-dimensional data by incorporating a sparsity constraint in the estimation of a mixture of Gaussian models. Though it makes some neat theoretical analysis possible, this type of approach is arguably restrictive for many applications. In this work we propose a novel latent transformation mixture model for clustering. The use of unspecified transformation makes the model much more flexible than the classical model-based clustering. Under the assumption that the optimal clustering admits a sparsity structure, we develop a new clustering algorithm named CESME for high-dimensional clustering. We offer a comprehensive analysis of CESME including identifiability, initialization, algorithmic convergence, and statistical guarantees on clustering. Extensive numerical study and real data analysis show that CESME outperforms the existing high-dimensional clustering algorithms in the literature.\u0026nbsp;\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"High-dimensional Clustering via A Latent Transformation Mixture Model "}],"uid":"34977","created_gmt":"2024-12-19 19:00:25","changed_gmt":"2024-12-19 19:03:10","author":"Julie Smith","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2025-01-16T11:00:00-05:00","event_time_end":"2025-01-16T12:00:00-05:00","event_time_end_last":"2025-01-16T12:00:00-05:00","gmt_time_start":"2025-01-16 16:00:00","gmt_time_end":"2025-01-16 17:00:00","gmt_time_end_last":"2025-01-16 17:00:00","rrule":null,"timezone":"America\/New_York"},"location":"ISyE Groseclose 402","extras":[],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"177814","name":"Postdoc"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"678979":{"#nid":"678979","#data":{"type":"event","title":"ISyE Seminar - Fan Li","body":[{"value":"\u003Ch3\u003ETitle:\u0026nbsp;\u003C\/h3\u003E\u003Cp\u003ECovariate adjustment in randomized experiments with missing outcomes and covariates\u003C\/p\u003E\u003Ch3\u003EAbstract:\u003C\/h3\u003E\u003Cp\u003ECovariate adjustment can improve precision in analyzing randomized experiments. With fully observed data, regression adjustment and inverse probability weighting are asymptotically equivalent in improving efficiency over unadjusted analysis. When some outcomes are missing, we consider combining these two adjustment methods with inverse probability of observation weighting for handling missing outcomes, and show that the equivalence between the two methods breaks down. Regression adjustment no longer ensures efficiency gain over unadjusted analysis unless the true outcome model is linear in covariates or the outcomes are missing completely at random. Propensity score weighting, in contrast, still guarantees efficiency over unadjusted analysis, and including more covariates in adjustment never harms asymptotic efficiency.\u0026nbsp; Moreover, we establish the value of using partially observed covariates to secure additional efficiency by the missingness indicator method, which imputes all missing covariates by zero and uses the union of the completed covariates and corresponding missingness indicators as the new, fully observed covariates.\u0026nbsp; Based on these findings, we recommend using regression adjustment in combination with the missingness indicator method if the linear outcome model or missing complete at random assumption is plausible and using propensity score weighting with the missingness indicator method otherwise.\u003C\/p\u003E\u003Ch3\u003EBio:\u0026nbsp;\u003C\/h3\u003E\u003Cp\u003EFan Li is a professor in the Department of Statistical Science at Duke University, with a secondary appointment at Biostatistics and Bioinformatics. Her primary research interest in causal inference, and have been working at the intersection between causal inference, machine learning, and population health. She also works on Bayesian analysis and missing data problems. She is the editor for Social Science, Biostatistics and Policy of the Annals of Applied Statistics, and an elected fellow of the American Statistical Association and the Institute of Mathematical Statistics (IMS).\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Ch3\u003EAbstract:\u0026nbsp;\u003C\/h3\u003E\u003Cp\u003ECovariate adjustment can improve precision in analyzing randomized experiments. With fully observed data, regression adjustment and inverse probability weighting are asymptotically equivalent in improving efficiency over unadjusted analysis. When some outcomes are missing, we consider combining these two adjustment methods with inverse probability of observation weighting for handling missing outcomes, and show that the equivalence between the two methods breaks down. Regression adjustment no longer ensures efficiency gain over unadjusted analysis unless the true outcome model is linear in covariates or the outcomes are missing completely at random. Propensity score weighting, in contrast, still guarantees efficiency over unadjusted analysis, and including more covariates in adjustment never harms asymptotic efficiency.\u0026nbsp; Moreover, we establish the value of using partially observed covariates to secure additional efficiency by the missingness indicator method, which imputes all missing covariates by zero and uses the union of the completed covariates and corresponding missingness indicators as the new, fully observed covariates.\u0026nbsp; Based on these findings, we recommend using regression adjustment in combination with the missingness indicator method if the linear outcome model or missing complete at random assumption is plausible and using propensity score weighting with the missingness indicator method ot herwise.\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Covariate adjustment in randomized experiments with missing outcomes and covariates"}],"uid":"34977","created_gmt":"2024-12-18 16:24:17","changed_gmt":"2024-12-18 16:26:33","author":"Julie Smith","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2025-01-14T11:00:00-05:00","event_time_end":"2025-01-14T12:00:00-05:00","event_time_end_last":"2025-01-14T12:00:00-05:00","gmt_time_start":"2025-01-14 16:00:00","gmt_time_end":"2025-01-14 17:00:00","gmt_time_end_last":"2025-01-14 17:00:00","rrule":null,"timezone":"America\/New_York"},"location":"ISyE Groseclose 402","extras":[],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"177814","name":"Postdoc"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"678920":{"#nid":"678920","#data":{"type":"event","title":"ISyE Seminar - Souvik Dhara","body":[{"value":"\u003Ch3\u003E\u003Cstrong\u003ETitle:\u003C\/strong\u003E\u0026nbsp;\u003C\/h3\u003E\u003Cp\u003EPropagation of Shocks on Networks: Can Local Information Predict Survival?\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Ch3\u003E\u003Cstrong\u003EAbstract:\u003C\/strong\u003E\u0026nbsp;\u003C\/h3\u003E\u003Cp\u003EComplex systems are often fragile, where minor disruptions can cascade into dramatic collapses. Epidemics serve as a prime example of this phenomenon, while the 2008 financial crisis highlights how a domino effect, originating from the small subprime mortgage sector, can trigger global repercussions. Similarly, a massive power outage in eastern North America was seemingly set off by a localized fault. The mathematical theory underlying these phenomena is both elegant and foundational, profoundly shaping the field of Network Science since its inception. In this talk, I will present a unifying mathematical model for network fragility and cascading dynamics and explore its deep connections to the theory of local-weak convergence, pioneered by Benjamini-Schramm and Aldous-Steele.\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Ch3\u003E\u003Cstrong\u003EBio:\u003C\/strong\u003E\u0026nbsp;\u003C\/h3\u003E\u003Cp\u003EDr. Souvik Dhara is an Assistant Professor at the Edwardson School of Industrial Engineering at Purdue University. He previously held positions as a Schramm Fellow, with a joint appointment at MIT Mathematics and Microsoft Research, a Simons-Berkeley Fellow at the Simons Institute for the Theory of Computing, UC Berkeley, and a Postdoctoral Research Associate at Brown University. Dr. Dhara earned his Ph.D. from the Department of Mathematics and Computer Science at Eindhoven University of Technology. In recognition of his doctoral work, he was awarded the Stieltjes Prize at the Dutch Mathematical Congress 2019. Dr. Dhara\u2019s research lies at the intersection of applied probability and network science, with a primary focus on developing theoretical foundations for stochastic processes and algorithms on large-scale networks. His interests include cascades on networks, graph representation learning, and different notions of graph limits.\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Ch3\u003E\u003Cstrong\u003EAbstract:\u003C\/strong\u003E\u0026nbsp;\u003C\/h3\u003E\u003Cp\u003EComplex systems are often fragile, where minor disruptions can cascade into dramatic collapses. Epidemics serve as a prime example of this phenomenon, while the 2008 financial crisis highlights how a domino effect, originating from the small subprime mortgage sector, can trigger global repercussions. Similarly, a massive power outage in eastern North America was seemingly set off by a localized fault. The mathematical theory underlying these phenomena is both elegant and foundational, profoundly shaping the field of Network Science since its inception. In this talk, I will present a unifying mathematical model for network fragility and cascading dynamics and explore its deep connections to the theory of local-weak convergence, pioneered by Benjamini-Schramm and Aldous-Steele.\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Propagation of Shocks on Networks: Can Local Information Predict Survival?"}],"uid":"34977","created_gmt":"2024-12-17 13:26:41","changed_gmt":"2024-12-17 13:29:07","author":"Julie Smith","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2025-01-21T11:00:00-05:00","event_time_end":"2025-01-21T12:00:00-05:00","event_time_end_last":"2025-01-21T12:00:00-05:00","gmt_time_start":"2025-01-21 16:00:00","gmt_time_end":"2025-01-21 17:00:00","gmt_time_end_last":"2025-01-21 17:00:00","rrule":null,"timezone":"America\/New_York"},"location":"ISyE Groseclose 402","extras":[],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"177814","name":"Postdoc"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"678804":{"#nid":"678804","#data":{"type":"event","title":"ISyE Seminar - Andrew Lowy","body":[{"value":"\u003Ch3\u003E\u003Cstrong\u003ETitle:\u0026nbsp;\u003C\/strong\u003E\u003C\/h3\u003E\u003Cp\u003EFoundations of Private Optimization for Modern Machine Learning\u003C\/p\u003E\u003Ch3\u003E\u003Cstrong\u003EAbstract:\u0026nbsp;\u003C\/strong\u003E\u003C\/h3\u003E\u003Cp\u003EHow can we develop optimization algorithms for training machine learning models that preserve the privacy of individuals\u0027 training data? In this talk, I will present my work addressing this challenge through differential privacy (DP). Differential privacy offers a rigorous, quantifiable standard of privacy that limits potential leakage of training data. I will explore the fundamental limits of performance for differentially private optimization in modern machine learning, particularly within federated learning settings, and present scalable, efficient algorithms that achieve optimal accuracy under DP constraints. Additionally, these algorithms demonstrate strong empirical performance.\u003C\/p\u003E\u003Ch3\u003E\u003Cstrong\u003EBio:\u003C\/strong\u003E\u0026nbsp;\u003C\/h3\u003E\u003Cp\u003EAndrew Lowy is\u0026nbsp;a postdoctoral Research Associate at University of Wisconsin-Madison, advised by Stephen J. Wright. He received his PhD in Applied Math at University\u0026nbsp;of Southern California under the supervision of Meisam Razaviyayn, where he was awarded the 2023 Center for Applied Mathematical Sciences (CAMS) Graduate Student Prize for outstanding research. His work has been published in leading venues in optimization, machine learning, and privacy, including SIOPT, NeurIPS, ICML, ICLR, ALT, AISTATS, ACM CCS, and the Journal of Privacy and Confidentiality. Prior to his doctoral studies, he completed his undergraduate studies at Princeton University and Columbia University.\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EAndrew\u2019s research focuses on optimization for private, fair, and robust machine learning. His primary area of expertise is in differentially private optimization, with an emphasis on understanding fundamental limits and developing scalable algorithms that attain these limits.\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Ch3\u003E\u003Cstrong\u003EAbstract:\u0026nbsp;\u003C\/strong\u003E\u003C\/h3\u003E\u003Cp\u003EHow can we develop optimization algorithms for training machine learning models that preserve the privacy of individuals\u0027 training data? In this talk, I will present my work addressing this challenge through differential privacy (DP). Differential privacy offers a rigorous, quantifiable standard of privacy that limits potential leakage of training data. I will explore the fundamental limits of performance for differentially private optimization in modern machine learning, particularly within federated learning settings, and present scalable, efficient algorithms that achieve optimal accuracy under DP constraints. Additionally, these algorithms demonstrate strong empirical performance.\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Foundations of Private Optimization for Modern Machine Learning"}],"uid":"34977","created_gmt":"2024-12-10 19:19:16","changed_gmt":"2024-12-10 19:22:26","author":"Julie Smith","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2025-01-09T11:00:00-05:00","event_time_end":"2025-01-09T12:00:00-05:00","event_time_end_last":"2025-01-09T12:00:00-05:00","gmt_time_start":"2025-01-09 16:00:00","gmt_time_end":"2025-01-09 17:00:00","gmt_time_end_last":"2025-01-09 17:00:00","rrule":null,"timezone":"America\/New_York"},"location":"ISyE Groseclose 402","extras":[],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"177814","name":"Postdoc"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"678761":{"#nid":"678761","#data":{"type":"event","title":"ISyE Seminar - Jackie Cha","body":[{"value":"\u003Ch3\u003ETitle:\u003C\/h3\u003E\u003Cp\u003ESurgical Human-Robot Collaborations: Transforming Training, Skills, and Safety\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Ch3\u003EAbstract:\u0026nbsp;\u003C\/h3\u003E\u003Cp\u003EThe introduction of surgical robotic systems in the operating room has changed the paradigm of how surgical team members physically and cognitively interact with both technology and one another. This shift has highlighted the need to reconsider nontechnical skills \u2013 task relevant cognitive and interpersonal skills among surgical team members \u2013 and to adapt surgical training accordingly. This presentation will highlight the impacts of robotic technology, both surgical systems and wearables, on skills training and clinician safety. It will also introduce potential interventions such as adaptive training programs and exoskeletons, aimed at improving surgical human-robot collaborations and team dynamics to promote surgical safety and performance.\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Ch3\u003EBio:\u0026nbsp;\u003C\/h3\u003E\u003Cp\u003EJackie Cha, PhD CPE is an Assistant Professor in the Department of Industrial Engineering and faculty with the Biomedical Data Science and Informatics Program at Clemson University and Faculty Scholar in the Clemson University School of Health Research. She obtained her PhD in Industrial Engineering from Purdue University, and MSE and BSE in Biomedical Engineering from the University of Michigan. Her research focuses on measuring physical human-robot interactions, particularly in healthcare (surgical) environments, to improve team performance, safety, and system efficiency. Her research has been funded by several sponsors such as the National Science Foundation (NSF) (including the NSF CAREER award), National Institutes of Health (NIH), and the Agency of Healthcare Research and Quality (AHRQ).\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Ch3\u003EAbstract:\u0026nbsp;\u003C\/h3\u003E\u003Cp\u003EThe introduction of surgical robotic systems in the operating room has changed the paradigm of how surgical team members physically and cognitively interact with both technology and one another. This shift has highlighted the need to reconsider nontechnical skills \u2013 task relevant cognitive and interpersonal skills among surgical team members \u2013 and to adapt surgical training accordingly. This presentation will highlight the impacts of robotic technology, both surgical systems and wearables, on skills training and clinician safety. It will also introduce potential interventions such as adaptive training programs and exoskeletons, aimed at improving surgical human-robot collaborations and team dynamics to promote surgical safety and performance.\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Surgical Human-Robot Collaborations: Transforming Training, Skills, and Safety"}],"uid":"34977","created_gmt":"2024-12-05 12:37:00","changed_gmt":"2024-12-05 12:40:18","author":"Julie Smith","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2025-01-07T11:00:00-05:00","event_time_end":"2025-01-07T12:00:00-05:00","event_time_end_last":"2025-01-07T12:00:00-05:00","gmt_time_start":"2025-01-07 16:00:00","gmt_time_end":"2025-01-07 17:00:00","gmt_time_end_last":"2025-01-07 17:00:00","rrule":null,"timezone":"America\/New_York"},"location":"ISyE Groseclose 402","extras":[],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"177814","name":"Postdoc"},{"id":"78771","name":"Public"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"678691":{"#nid":"678691","#data":{"type":"event","title":"ISyE Seminar - Shengbo Wang ","body":[{"value":"\u003Ch3\u003E\u003Cstrong\u003ETitle:\u0026nbsp;\u003C\/strong\u003E\u003C\/h3\u003E\u003Cp\u003EEfficient Gradient Estimation for Overparameterized Stochastic Differential Equations\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003E\u0026nbsp;\u003C\/strong\u003E\u003C\/p\u003E\u003Ch3\u003E\u003Cstrong\u003EAbstract:\u003C\/strong\u003E\u0026nbsp;\u003C\/h3\u003E\u003Cp\u003EOverparameterized stochastic differential equation (SDE) models have achieved remarkable success in various complex environments, such as PDE-constrained optimization, stochastic control and reinforcement learning, financial engineering, neural SDEs, and generative AI. These models often feature system evolution coefficients that are parameterized by a high-dimensional vector \u03b8 in R^n, aiming to optimize expectations of the SDE, such as a value function, through stochastic gradient ascent. Consequently, designing efficient gradient estimators for which the computational complexity scales well with n is of significant interest. We introduce a novel unbiased stochastic gradient estimator--the generator gradient estimator--for which the computation time remains stable in n. In addition to establishing the validity of our methodology for general SDEs with jumps, we also perform numerical experiments testing our estimator in controlling a multi-class queue where the control policy is parameterized by high-dimensional neural networks. The results show a significant improvement in efficiency compared to previous methods: our estimator achieves near-constant computation times, increasingly outperforms its counterparts as n increases. These empirical findings highlight the potential of our proposed methodology for optimizing SDEs in contemporary applications.\u003C\/p\u003E\u003Cp\u003EThis is a joint work with Jose Blanchet and Peter Glynn.\u003C\/p\u003E\u003Ch3\u003E\u003Cstrong\u003EBio:\u003C\/strong\u003E\u0026nbsp;\u003C\/h3\u003E\u003Cp\u003EShengbo Wang is a fifth-year Ph.D. candidate in Operations Research at Stanford University\u2019s Department of Management Science and Engineering, co-advised by Prof. Peter Glynn and Prof. Jose Blanchet. His research interests span a broad spectrum within applied probability, including stochastic modeling, reinforcement learning, distributionally robust control, and simulation methods for machine learning. He focuses on developing tractable probabilistic models and designing algorithms for data-driven dynamic decision-making under uncertainty, specifically addressing reliability and scalability challenges in modern managerial and engineering applications. Prior to his PhD studies, he earned his B.S. in Operations Research and Information Engineering from Cornell University.\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Ch3\u003E\u003Cstrong\u003EAbstract:\u003C\/strong\u003E\u0026nbsp;\u003C\/h3\u003E\u003Cp\u003EOverparameterized stochastic differential equation (SDE) models have achieved remarkable success in various complex environments, such as PDE-constrained optimization, stochastic control and reinforcement learning, financial engineering, neural SDEs, and generative AI. These models often feature system evolution coefficients that are parameterized by a high-dimensional vector \u03b8 in R^n, aiming to optimize expectations of the SDE, such as a value function, through stochastic gradient ascent. Consequently, designing efficient gradient estimators for which the computational complexity scales well with n is of significant interest. We introduce a novel unbiased stochastic gradient estimator--the generator gradient estimator--for which the computation time remains stable in n. In addition to establishing the validity of our methodology for general SDEs with jumps, we also perform numerical experiments testing our estimator in controlling a multi-class queue where the control policy is parameterized by high-dimensional neural networks. The results show a significant improvement in efficiency compared to previous methods: our estimator achieves near-constant computation times, increasingly outperforms its counterparts as n increases. These empirical findings highlight the potential of our proposed methodology for optimizing SDEs in contemporary applications.\u003C\/p\u003E\u003Cp\u003EThis is a joint work with Jose Blanchet and Peter Glynn.\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Efficient Gradient Estimation for Overparameterized Stochastic Differential Equations"}],"uid":"34977","created_gmt":"2024-11-27 13:19:55","changed_gmt":"2024-11-27 13:22:18","author":"Julie Smith","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2025-01-23T11:00:00-05:00","event_time_end":"2025-01-23T12:00:00-05:00","event_time_end_last":"2025-01-23T12:00:00-05:00","gmt_time_start":"2025-01-23 16:00:00","gmt_time_end":"2025-01-23 17:00:00","gmt_time_end_last":"2025-01-23 17:00:00","rrule":null,"timezone":"America\/New_York"},"location":"ISyE Groseclose 402","extras":[],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"177814","name":"Postdoc"},{"id":"78771","name":"Public"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"678690":{"#nid":"678690","#data":{"type":"event","title":"ISyE Seminar - Aysu Ozel","body":[{"value":"\u003Ch3\u003ETitle:\u0026nbsp;\u003C\/h3\u003E\u003Cp\u003EAdvances in School District Design: Addressing Inequities and Planning for the Future\u003C\/p\u003E\u003Ch3\u003EAbstract:\u003C\/h3\u003E\u003Cp\u003EFor decades, public school districts in the United States have faced complex decisions related to school district design. School desegregation in 1954 led to a series of operations research approaches to support these decisions. In this talk, we present a new modeling framework for the school district design problem, developed in partnership with a local school district, to facilitate an iterative community co-design process to address historic inequities in access to education and improve student assignment. This process led to recommendations and policy outcomes, including the school board\u2019s approval of a new school and revised school attendance boundaries. At the core of our approach is a novel formulation that consolidates multiple assignment decisions, capturing their interactions through composite variables. The compact nature of this formulation makes it amenable to important extensions. Notably, we consider time-expanded versions of the model that allow districts to plan for multiple years. Importantly, these time-expanded district design models consider continuity in students\u2019 educational experiences over time periods that may include the opening and closing of schools. This model can also incorporate uncertainty in future student enrollment over the planning horizon. Testing these new models is challenging given the lack of shareable data sets due to sensitive student information. To address this, we developed a method to create context-rich data sets for school operations models and methods relying only on publicly available data.\u003C\/p\u003E\u003Ch3\u003EBio:\u003C\/h3\u003E\u003Cp\u003EAysu Ozel is a Ph.D. candidate in the Department of Industrial Engineering and Management Sciences at Northwestern University. Her dissertation research focuses on developing models and solution approaches to support decision-making for school district design. Her work has been recognized as the Winner of the 2023 INFORMS Doing Good with Good OR Student Paper Competition and a Finalist for the 2023 INFORMS DEI Best Student Paper Award. She is a Dissertation Year Fellow of Northwestern University Transportation Center and a Terminal Year Fellow of McCormick School of Engineering. Before her doctoral program, Aysu earned her M.S. and B.S. degrees in Industrial Engineering from Bilkent University.\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Ch3\u003EAbstract:\u003C\/h3\u003E\u003Cp\u003EFor decades, public school districts in the United States have faced complex decisions related to school district design. School desegregation in 1954 led to a series of operations research approaches to support these decisions. In this talk, we present a new modeling framework for the school district design problem, developed in partnership with a local school district, to facilitate an iterative community co-design process to address historic inequities in access to education and improve student assignment. This process led to recommendations and policy outcomes, including the school board\u2019s approval of a new school and revised school attendance boundaries. At the core of our approach is a novel formulation that consolidates multiple assignment decisions, capturing their interactions through composite variables. The compact nature of this formulation makes it amenable to important extensions. Notably, we consider time-expanded versions of the model that allow districts to plan for multiple years. Importantly, these time-expanded district design models consider continuity in students\u2019 educational experiences over time periods that may include the opening and closing of schools. This model can also incorporate uncertainty in future student enrollment over the planning horizon. Testing these new models is challenging given the lack of shareable data sets due to sensitive student information. To address this, we developed a method to create context-rich data sets for school operations models and methods relying only on publicly available data.\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Advances in School District Design: Addressing Inequities and Planning for the Future"}],"uid":"34977","created_gmt":"2024-11-27 12:52:31","changed_gmt":"2024-11-27 12:57:54","author":"Julie Smith","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2024-12-10T11:00:00-05:00","event_time_end":"2024-12-10T12:00:00-05:00","event_time_end_last":"2024-12-10T12:00:00-05:00","gmt_time_start":"2024-12-10 16:00:00","gmt_time_end":"2024-12-10 17:00:00","gmt_time_end_last":"2024-12-10 17:00:00","rrule":null,"timezone":"America\/New_York"},"location":"ISyE Groseclose 402","extras":[],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"177814","name":"Postdoc"},{"id":"78771","name":"Public"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"678577":{"#nid":"678577","#data":{"type":"event","title":"SCL Lunch and Learn: \u201cMastering Strategic Sourcing: Balancing Cost, Quality, and Risk\u0022","body":[{"value":"\u003Cp\u003E\u003Cstrong\u003EJoin us for an insightful webinar that delves into the complexities of strategic sourcing.\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EThursday, February 6, 2025 | 12-1pm ET\u003C\/strong\u003E\u003C\/p\u003E\u003Cdiv\u003E\u003Cdiv\u003E\u003Cdiv\u003E\u003Cdiv\u003E\u003Cdiv\u003E\u003Cdiv\u003EWe\u0027ll explore how to strike the right balance between cost efficiency, quality standards, and effective risk management. Learn about the latest trends shaping the sourcing landscape, the critical role of strong supplier relationships, and strategies for navigating the challenges of global supply chain disruptions. Don\u0027t miss this opportunity to gain actionable insights that can help optimize your sourcing strategy in an ever-evolving market.\u003C\/div\u003E\u003C\/div\u003E\u003C\/div\u003E\u003C\/div\u003E\u003C\/div\u003E\u003Cdiv\u003E\u0026nbsp;\u003C\/div\u003E\u003C\/div\u003E\u003Cp\u003E\u003Ca href=\u0022https:\/\/www.scl.gatech.edu\/feb25-lnl\u0022\u003E\u003Cstrong\u003ERegister Online to Attend\u003C\/strong\u003E\u003C\/a\u003E\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EJoin us for an insightful webinar that delves into the complexities of strategic sourcing.\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Join us for an insightful webinar that delves into the complexities of strategic sourcing."}],"uid":"27233","created_gmt":"2024-11-21 02:12:35","changed_gmt":"2024-11-21 02:42:45","author":"Andy Haleblian","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2025-02-06T12:00:00-05:00","event_time_end":"2025-02-06T13:00:00-05:00","event_time_end_last":"2025-02-06T13:00:00-05:00","gmt_time_start":"2025-02-06 17:00:00","gmt_time_end":"2025-02-06 18:00:00","gmt_time_end_last":"2025-02-06 18:00:00","rrule":null,"timezone":"America\/New_York"},"location":"Online via Zoom","extras":[],"hg_media":{"675688":{"id":"675688","type":"image","title":"SCL Lunch and Learn: \u201cMastering Strategic Sourcing: Balancing Cost, Quality, and Risk\u0022","body":null,"created":"1732155487","gmt_created":"2024-11-21 02:18:07","changed":"1732155487","gmt_changed":"2024-11-21 02:18:07","alt":"SCL Lunch and Learn: \u201cMastering Strategic Sourcing: Balancing Cost, Quality, and Risk\u0022","file":{"fid":"259340","name":"PSM.png","image_path":"\/sites\/default\/files\/2024\/11\/20\/PSM.png","image_full_path":"http:\/\/hg.gatech.edu\/\/sites\/default\/files\/2024\/11\/20\/PSM.png","mime":"image\/png","size":165743,"path_740":"http:\/\/hg.gatech.edu\/sites\/default\/files\/styles\/740xx_scale\/public\/2024\/11\/20\/PSM.png?itok=aIdmPpr1"}}},"media_ids":["675688"],"related_links":[{"url":"https:\/\/www.scl.gatech.edu\/feb25-lnl","title":"Register Online to Attend the Webinar"},{"url":"https:\/\/pe.gatech.edu\/certificates\/procurement-and-supply-management-certificate","title":"About our Procurement and Supply Management Leadership Certificate"}],"groups":[{"id":"1250","name":"Center for Health and Humanitarian Systems (CHHS)"},{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"},{"id":"1243","name":"The Supply Chain and Logistics Institute (SCL)"}],"categories":[],"keywords":[{"id":"3615","name":"information session"},{"id":"186857","name":"go-gtmi"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"10377","name":"Career\/Professional development"}],"invited_audience":[{"id":"78771","name":"Public"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[{"value":"\u003Cp\u003Ecourse@scl.gatech.edu\u003C\/p\u003E","format":"limited_html"}],"email":[],"slides":[],"orientation":[],"userdata":""}},"678515":{"#nid":"678515","#data":{"type":"event","title":"SCL Lunch and Learn: \u201cGenerative AI for Supply Chain\u200b\u0022","body":[{"value":"\u003Cp\u003E\u003Cstrong\u003EJoin us for a deep dive into how artificial intelligence (AI) is optimizing supply chain efficiency.\u0026nbsp;\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EThursday, January 2, 2025 | 12-1pm ET\u003C\/strong\u003E\u003C\/p\u003E\u003Cdiv\u003E\u003Cdiv\u003E\u003Cdiv\u003E\u003Cdiv\u003E\u003Cdiv\u003E\u003Cdiv\u003EWe\u0027ll touch on generative AI fundamentals, prompt engineering, and practical applications like automated inventory, predictive maintenance, and route optimization. We will also touch on ethical AI use, best practices for generative AI, and emerging supply chain use cases.\u003C\/div\u003E\u003C\/div\u003E\u003C\/div\u003E\u003C\/div\u003E\u003C\/div\u003E\u003Cdiv\u003E\u0026nbsp;\u003C\/div\u003E\u003C\/div\u003E\u003Cp\u003E\u003Ca href=\u0022https:\/\/gatech.zoom.us\/webinar\/register\/2717316132305\/WN_LD1BtdBCTjG7-apdDdeV6A\u0022\u003E\u003Cstrong\u003ERegister Online to Attend\u003C\/strong\u003E\u003C\/a\u003E\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EJoin us for a deep dive into how artificial intelligence (AI) is optimizing supply chain efficiency.\u0026nbsp;\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Join us for a deep dive into how artificial intelligence (AI) is optimizing supply chain efficiency. "}],"uid":"27233","created_gmt":"2024-11-18 21:08:38","changed_gmt":"2024-11-21 02:28:49","author":"Andy Haleblian","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2025-01-02T12:00:00-05:00","event_time_end":"2025-01-02T13:00:00-05:00","event_time_end_last":"2025-01-02T13:00:00-05:00","gmt_time_start":"2025-01-02 17:00:00","gmt_time_end":"2025-01-02 18:00:00","gmt_time_end_last":"2025-01-02 18:00:00","rrule":null,"timezone":"America\/New_York"},"location":"Online via Zoom","extras":[],"hg_media":{"675659":{"id":"675659","type":"image","title":"SCL Lunch and Learn: \u201cGenerative AI for Supply Chain\u200b\u0022","body":null,"created":"1731966273","gmt_created":"2024-11-18 21:44:33","changed":"1731966353","gmt_changed":"2024-11-18 21:45:53","alt":"SCL Lunch and Learn: \u201cGenerative AI for Supply Chain\u200b\u0022","file":{"fid":"259307","name":"LunchAndLearn-GenAI.png","image_path":"\/sites\/default\/files\/2024\/11\/18\/LunchAndLearn-GenAI.png","image_full_path":"http:\/\/hg.gatech.edu\/\/sites\/default\/files\/2024\/11\/18\/LunchAndLearn-GenAI.png","mime":"image\/png","size":159462,"path_740":"http:\/\/hg.gatech.edu\/sites\/default\/files\/styles\/740xx_scale\/public\/2024\/11\/18\/LunchAndLearn-GenAI.png?itok=FtDCO0L0"}}},"media_ids":["675659"],"related_links":[{"url":"https:\/\/www.scl.gatech.edu\/jan25-lnl","title":"Register Online to Attend the Webinar"},{"url":"https:\/\/pe.gatech.edu\/courses\/generative-ai-application-supply-chain-professionals","title":"About our Generative AI Application for Supply Chain Professionals course"}],"groups":[{"id":"1250","name":"Center for Health and Humanitarian Systems (CHHS)"},{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"},{"id":"1243","name":"The Supply Chain and Logistics Institute (SCL)"}],"categories":[],"keywords":[{"id":"3615","name":"information session"},{"id":"186857","name":"go-gtmi"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"10377","name":"Career\/Professional development"}],"invited_audience":[{"id":"78771","name":"Public"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[{"value":"\u003Cp\u003Ecourse@scl.gatech.edu\u003C\/p\u003E","format":"limited_html"}],"email":[],"slides":[],"orientation":[],"userdata":""}},"678522":{"#nid":"678522","#data":{"type":"event","title":"ISyE Seminar - Calum MacRury","body":[{"value":"\u003Ch3\u003ETitle:\u003C\/h3\u003E\u003Cp\u003EOnline Contention Resolution Schemes for the Matching Polytope of Graphs\u0027\u0027\u003C\/p\u003E\u003Ch3\u003EAbstract:\u003C\/h3\u003E\u003Cp\u003EOnline Contention Resolution Schemes (OCRS\u0027s) represent a modern tool for selecting a subset of elements, subject to resource constraints, when the elements are presented to the algorithm sequentially. OCRS\u0027s have led to some of the best-known competitive ratio guarantees for online resource allocation problems, with the added benefit of treating different online decisions---accept\/reject, probing, pricing---in a unified manner. We analyze OCRS\u0027s for resource constraints defined by graph matchings, a fundamental structure in combinatorial optimization. We improve the state of the art both in terms of algorithmic guarantees and impossibility results. Our algorithms directly improve the best-known competitive ratios for online accept\/reject, probing, and pricing problems on graphs. This includes the prophet matching problem for both edge and vertex arrival models, as well as for matching in a gig-economy. Our techniques are also relevant to more complicated resource constraints, and we attain new results for network revenue management and online assortment optimization.\u0026nbsp;\u003C\/p\u003E\u003Ch3\u003EBio:\u003C\/h3\u003E\u003Cp\u003ECalum MacRury is a Postdoctoral Research Scholar in the Decision, Risk, and Operations Division at Columbia Business School, where he is supported by\u003Cbr\u003Ean NSERC Postdoctoral Fellowship (PDF). He works on online algorithms, and more generally, decision making under uncertainty. He is particularly interested in stochastic optimization, including prophet inequalities and probing problems when the resource constraints are described by graph matchings. Calum received his PhD from the Department of Computer Science at the University of Toronto in 2023.\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Ch3\u003EAbstract:\u003C\/h3\u003E\u003Cp\u003EOnline Contention Resolution Schemes (OCRS\u0027s) represent a modern tool for selecting a subset of elements, subject to resource constraints, when the elements are presented to the algorithm sequentially. OCRS\u0027s have led to some of the best-known competitive ratio guarantees for online resource allocation problems, with the added benefit of treating different online decisions---accept\/reject, probing, pricing---in a unified manner. We analyze OCRS\u0027s for resource constraints defined by graph matchings, a fundamental structure in combinatorial optimization. We improve the state of the art both in terms of algorithmic guarantees and impossibility results. Our algorithms directly improve the best-known competitive ratios for online accept\/reject, probing, and pricing problems on graphs. This includes the prophet matching problem for both edge and vertex arrival models, as well as for matching in a gig-economy. Our techniques are also relevant to more complicated resource constraints, and we attain new results for network revenue management and online assortment optimization.\u0026nbsp;\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Online Contention Resolution Schemes for the Matching Polytope of Graphs\u0027"}],"uid":"34977","created_gmt":"2024-11-19 14:35:54","changed_gmt":"2024-11-19 14:38:53","author":"Julie Smith","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2024-12-03T11:00:00-05:00","event_time_end":"2024-12-03T12:00:00-05:00","event_time_end_last":"2024-12-03T12:00:00-05:00","gmt_time_start":"2024-12-03 16:00:00","gmt_time_end":"2024-12-03 17:00:00","gmt_time_end_last":"2024-12-03 17:00:00","rrule":null,"timezone":"America\/New_York"},"location":"ISyE Groseclose 402","extras":[],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"177814","name":"Postdoc"},{"id":"78771","name":"Public"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"678520":{"#nid":"678520","#data":{"type":"event","title":"ISyE Seminar - Yu Ma","body":[{"value":"\u003Ch3\u003E\u003Cstrong\u003ETitle\u003C\/strong\u003E:\u0026nbsp;\u003C\/h3\u003E\u003Cp\u003EIntegrative Artificial Intelligence for Healthcare\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Ch3\u003E\u003Cstrong\u003EAbstract\u003C\/strong\u003E:\u0026nbsp;\u003C\/h3\u003E\u003Cp\u003EDeveloping integrated artificial intelligence frameworks is crucial for hospital operations and medical diagnostics. However, despite the opportunities, there remain challenges on 1) how to effectively learn, share, and combine information from diverse sources into a single setting and 2) how to design models that are practically implementable. In the first part, we propose and evaluate a unified Holistic AI in Medicine (HAIM) framework to facilitate the generation and testing of AI systems that integrate data from multiple modality sources, including tabular, time-series, language, and vision data. Our approach leverages recently developed open-source, pre-trained large models. We show that this framework can consistently and robustly produce models that outperform similar single-modality approaches across various healthcare tasks by 6\u201333%. In the second part, we introduce Multimodal Multitask Machine Learning for Healthcare (M3H), an explainable framework that consolidates learning for multiple tasks, including supervised binary\/multiclass classification, regression, and unsupervised clustering in a single model to better exploit the interactions and dependencies among tasks. It introduces a novel attention mechanism inspired by healthcare considerations that designs a token-based query computation and scaling function that encourages self-exploitation and cross-exploration. The work, in addition, proposes a new explainability metric of the task space to better quantify the dynamics of task learning interdependencies and to automatically detect patterns among task relations. M3H encompasses a wide range of medical tasks and consistently outperforms traditional single-task models by an average of 11.6% across 44 medical tasks. The modular design of both frameworks ensures their generalizability in data processing, task definition, and rapid model prototyping. Combined, HAIM and M3H offer methodological and practical solutions to design integrative artificial intelligence to impact practice.\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Ch3\u003E\u003Cstrong\u003EBio:\u003C\/strong\u003E\u003C\/h3\u003E\u003Cp\u003EYu Ma is a final year PhD student at the MIT Operations Research Center, where she is advised by Prof. Dimitris Bertsimas. Her research focuses on the use of AI methodologies to solve significant problems in healthcare service and policy making. Driven by a commitment to creating real-world impact, she has collaborated with six healthcare institutions and implemented three of her works in practice at Hartford Healthcare, the largest hospital system in Connecticut. In tackling these challenges, her works combine tools from machine learning, optimization, and analytics. She is recognized by the MIT School of Engineering as a Takeda Fellow. In the summer of 2023, she was an applied scientist in eBay\u2019s Recommendation team. Prior to PhD, she obtained a B.A. degree from UC Berkeley in Applied Mathematics.\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Ch3\u003E\u003Cstrong\u003EAbstract\u003C\/strong\u003E:\u0026nbsp;\u003C\/h3\u003E\u003Cp\u003EDeveloping integrated artificial intelligence frameworks is crucial for hospital operations and medical diagnostics. However, despite the opportunities, there remain challenges on 1) how to effectively learn, share, and combine information from diverse sources into a single setting and 2) how to design models that are practically implementable. In the first part, we propose and evaluate a unified Holistic AI in Medicine (HAIM) framework to facilitate the generation and testing of AI systems that integrate data from multiple modality sources, including tabular, time-series, language, and vision data. Our approach leverages recently developed open-source, pre-trained large models. We show that this framework can consistently and robustly produce models that outperform similar single-modality approaches across various healthcare tasks by 6\u201333%. In the second part, we introduce Multimodal Multitask Machine Learning for Healthcare (M3H), an explainable framework that consolidates learning for multiple tasks, including supervised binary\/multiclass classification, regression, and unsupervised clustering in a single model to better exploit the interactions and dependencies among tasks. It introduces a novel attention mechanism inspired by healthcare considerations that designs a token-based query computation and scaling function that encourages self-exploitation and cross-exploration. The work, in addition, proposes a new explainability metric of the task space to better quantify the dynamics of task learning interdependencies and to automatically detect patterns among task relations. M3H encompasses a wide range of medical tasks and consistently outperforms traditional single-task models by an average of 11.6% across 44 medical tasks. The modular design of both frameworks ensures their generalizability in data processing, task definition, and rapid model prototyping. Combined, HAIM and M3H offer methodological and practical solutions to design integrative artificial intelligence to impact practice.\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Integrative Artificial Intelligence for Healthcare"}],"uid":"34977","created_gmt":"2024-11-19 13:16:05","changed_gmt":"2024-11-19 13:17:56","author":"Julie Smith","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2024-12-05T11:00:00-05:00","event_time_end":"2024-12-05T12:00:00-05:00","event_time_end_last":"2024-12-05T12:00:00-05:00","gmt_time_start":"2024-12-05 16:00:00","gmt_time_end":"2024-12-05 17:00:00","gmt_time_end_last":"2024-12-05 17:00:00","rrule":null,"timezone":"America\/New_York"},"location":"ISyE Groseclose 402","extras":[],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"177814","name":"Postdoc"},{"id":"78771","name":"Public"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"678387":{"#nid":"678387","#data":{"type":"event","title":"ISyE Seminar - Susan R Hunter","body":[{"value":"\u003Cp\u003ETitle:\u0026nbsp;\u003C\/p\u003E\u003Cp\u003ETwo-Stage Stochastic Multi-Objective Linear Programming\u003C\/p\u003E\u003Cp\u003EAbstract:\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EWe consider a two-stage stochastic multi-objective linear program (TSSMOLP) which is a natural multi-objective generalization of the well-studied two-stage stochastic linear program. The second-stage recourse decision is governed by an uncertain multi-objective linear program whose solution maps to an uncertain second-stage nondominated set. The TSSMOLP then comprises the objective function, which is the Minkowsi sum of a linear term plus the expected value of the second-stage nondominated set, and the constraints, which are linear. Since the second-stage nondominated set is a random set, its expected value is defined through the selection expectation. The global Pareto set is defined as the collection of nondominated points in the image space of the TSSMOLP. We discuss properties of TSSMOLPs and the multifunctions that arise therein, as well as the implications of these properties for the development of TSSMOLP solution methods. We illustrate the TSSMOLP and its properties through an example in disaster relief planning.\u003C\/p\u003E\u003Cp\u003EThis work is joint work with Akshita Gupta, Edwardson School of Industrial Engineering, Purdue University.\u003C\/p\u003E\u003Cp\u003EBio:\u0026nbsp;\u003C\/p\u003E\u003Cp\u003ESusan R. Hunter is an associate professor in the Edwardson School of Industrial Engineering at Purdue University. Her research interests include theoretical and algorithmic aspects of stochastic optimization in the presence of multiple performance measures with emphasis on asymptotics, computation, and application. In 2016, she received an NSF CAREER Award to work on multi-objective simulation optimization; that is, multi-objective optimization in which the objective functions can only be observed with stochastic error as the output of a black-box Monte Carlo simulation oracle. Her published works have been recognized by the INFORMS Computing Society in 2011, by IISE Transactions in 2017, and by The Operational Research Society in 2021. She currently serves as Program Chair for the 2024 Winter Simulation Conference, on the Organizing Committee for SIAM Conference on Optimization (OP26), as Vice President \/ President Elect of the INFORMS Simulation Society, and as an associate editor for Operations Research, Journal of Optimization Theory and Applications, and Flexible Services and Manufacturing Journal.\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EWe consider a two-stage stochastic multi-objective linear program (TSSMOLP) which is a natural multi-objective generalization of the well-studied two-stage stochastic linear program. The second-stage recourse decision is governed by an uncertain multi-objective linear program whose solution maps to an uncertain second-stage nondominated set. The TSSMOLP then comprises the objective function, which is the Minkowsi sum of a linear term plus the expected value of the second-stage nondominated set, and the constraints, which are linear. Since the second-stage nondominated set is a random set, its expected value is defined through the selection expectation. The global Pareto set is defined as the collection of nondominated points in the image space of the TSSMOLP. We discuss properties of TSSMOLPs and the multifunctions that arise therein, as well as the implications of these properties for the development of TSSMOLP solution methods. We illustrate the TSSMOLP and its properties through an example in disaster relief planning.\u0026nbsp;\u003Cbr\u003E\u0026nbsp;\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Two-Stage Stochastic Multi-Objective Linear Programming"}],"uid":"36374","created_gmt":"2024-11-13 10:00:31","changed_gmt":"2024-11-13 10:02:33","author":"mwelch39","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2024-11-22T11:30:00-05:00","event_time_end":"2024-11-22T12:30:00-05:00","event_time_end_last":"2024-11-22T12:30:00-05:00","gmt_time_start":"2024-11-22 16:30:00","gmt_time_end":"2024-11-22 17:30:00","gmt_time_end_last":"2024-11-22 17:30:00","rrule":null,"timezone":"America\/New_York"},"location":" ISYE Groseclose 402","extras":[],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"177814","name":"Postdoc"},{"id":"78771","name":"Public"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"678379":{"#nid":"678379","#data":{"type":"event","title":"ISyE Seminar - Feng Zhu","body":[{"value":"\u003Ch3\u003E\u003Cstrong\u003ETitle:\u003C\/strong\u003E\u0026nbsp;\u003C\/h3\u003E\u003Cp\u003EManaging Tail Risk in Online Learning: When Safety Meets Efficiency\u003C\/p\u003E\u003Ch3\u003E\u003Cstrong\u003EAbstract:\u003C\/strong\u003E\u0026nbsp;\u003C\/h3\u003E\u003Cp\u003EOnline learning is a fast-growing research area in sequential decision-making. While previous work mostly focuses on achieving efficiency by minimizing regret expectation, controlling the regret tail risk to ensure safety is essential in applications such as revenue management, clinical trials, and financial investment, but has not been well studied. This work tries to provide a detailed characterization of regret distribution in online learning under the safety concern of managing tail risk.\u003C\/p\u003E\u003Cp\u003EIn Part I, we aim to design policies that enjoy both optimal regret expectation and light-tailed regret distribution. We first find that any policy that obtains the optimal instance-dependent regret expectation could incur a heavy-tailed regret tail risk. We then design a novel policy that enjoys the optimal worst-case regret expectation and has the optimal worst-case regret tail risk with an optimal exponential decaying rate for any regret threshold. Numerical experiments show that our new policy design leads to similar efficiency and much better safety compared to celebrated policies. Our policy design also bears an interesting connection with Monte Carlo Tree Search (MCTS) used in AlphaGo. In Part II, we study the optimal trade-off between expectation and tail risk for regret distribution. We fully characterize the interplay among three desired properties for policy design: worst-case optimality, instance-dependent consistency, and light-tailed risk. Our results reveal several insights on how to design policies that balance efficiency and safety. All our results are extended to the stochastic linear bandit setting.\u003C\/p\u003E\u003Ch3\u003E\u003Cstrong\u003EBio:\u003C\/strong\u003E\u0026nbsp;\u003C\/h3\u003E\u003Cp\u003EFeng Zhu is a 5th-year PhD student at MIT, advised by Prof. David Simchi-Levi. His research interests lie broadly in sequential decision-making under uncertainty, including online learning and online matching, with applications to online experimentation, supply chain management and revenue management. His research goal is to develop data-driven decision-making paradigms that ensure the safety \u0026amp; resiliency of modern operational systems, with a keen focus on managing (hidden) risk in various decision-making environments. Prior to MIT, he majored in Mathematics \u0026amp; Statistics and minored in Economics at Peking University.\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Ch3\u003E\u003Cstrong\u003EAbstract:\u003C\/strong\u003E\u0026nbsp;\u003C\/h3\u003E\u003Cp\u003EOnline learning is a fast-growing research area in sequential decision-making. While previous work mostly focuses on achieving efficiency by minimizing regret expectation, controlling the regret tail risk to ensure safety is essential in applications such as revenue management, clinical trials, and financial investment, but has not been well studied. This work tries to provide a detailed characterization of regret distribution in online learning under the safety concern of managing tail risk.\u003C\/p\u003E\u003Cp\u003EIn Part I, we aim to design policies that enjoy both optimal regret expectation and light-tailed regret distribution. We first find that any policy that obtains the optimal instance-dependent regret expectation could incur a heavy-tailed regret tail risk. We then design a novel policy that enjoys the optimal worst-case regret expectation and has the optimal worst-case regret tail risk with an optimal exponential decaying rate for any regret threshold. Numerical experiments show that our new policy design leads to similar efficiency and much better safety compared to celebrated policies. Our policy design also bears an interesting connection with Monte Carlo Tree Search (MCTS) used in AlphaGo. In Part II, we study the optimal trade-off between expectation and tail risk for regret distribution. We fully characterize the interplay among three desired properties for policy design: worst-case optimality, instance-dependent consistency, and light-tailed risk. Our results reveal several insights on how to design policies that balance efficiency and safety. All our results are extended to the stochastic linear bandit setting.\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Managing Tail Risk in Online Learning: When Safety Meets Efficiency"}],"uid":"34977","created_gmt":"2024-11-12 20:27:17","changed_gmt":"2024-11-12 20:29:18","author":"Julie Smith","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2024-12-12T11:00:00-05:00","event_time_end":"2024-12-12T12:00:00-05:00","event_time_end_last":"2024-12-12T12:00:00-05:00","gmt_time_start":"2024-12-12 16:00:00","gmt_time_end":"2024-12-12 17:00:00","gmt_time_end_last":"2024-12-12 17:00:00","rrule":null,"timezone":"America\/New_York"},"location":"ISyE Groseclose 402","extras":[],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"177814","name":"Postdoc"},{"id":"78771","name":"Public"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"678231":{"#nid":"678231","#data":{"type":"event","title":"ISyE Seminar - Rudy Zhou ","body":[{"value":"\u003Ch3\u003E\u003Cstrong\u003ETitle:\u0026nbsp;\u003C\/strong\u003E\u003C\/h3\u003E\u003Cp\u003EOptimization under Uncertainty: Scheduling with Failover\u003C\/p\u003E\u003Ch3\u003E\u003Cstrong\u003EAbstract:\u0026nbsp;\u003C\/strong\u003E\u003C\/h3\u003E\u003Cp\u003EModern decision-making pipelines almost always rely on ML\/AI tools to quantify uncertainty via probability distributions. This naturally leads to stochastic models, which we would like to optimize over to drive better decisions. I will discuss my work in developing fundamental algorithmic techniques for stochastic combinatorial optimization to drive algorithm design in both theory and practice.\u003Cbr\u003E\u003Cbr\u003EI will introduce a novel resource allocation problem arising in cloud data centers: demands arrive online and need to be assigned to devices subject to capacity constraints to maximize utilization. Further, to be robust against failure, our assignment must remain feasible even in failover scenarios \u2013 when some device fails. These novel failover constraints introduce new trade-offs not present in classic assignment problems.\u0026nbsp; We design asymptotically optimal online algorithms for this problem in both the worst- and average-case (where demands are drawn i.i.d. from an unknown distribution). Along the way, we will see a lesser-known probabilistic tool: stochastic monotone matchings. Preliminary experiments on real cloud workloads show the potential of our algorithms to generate more power-efficient allocations, which can save millions and significantly reduce the environmental impact of cloud computing.\u003C\/p\u003E\u003Ch3\u003E\u003Cstrong\u003EBio:\u0026nbsp;\u003C\/strong\u003E\u003C\/h3\u003E\u003Cp\u003ERudy is currently a postdoc at Carnegie Mellon, where he recently graduated with a PhD in the ACO (Algorithms, Combinatorics, and Optimization) program. He is broady interested in algorithm design - especially for discrete and stochastic optimization problems. His work draws on ideas from approximation algorithms, probability theory, and convex optimization to design improved algorithms and general-purpose technical tools for fundamental optimization problems. On the application side, he is interested in realizing the potential impact of theoretical algorithms in practice in areas such as data center scheduling and naval logistics.\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Ch3\u003E\u003Cstrong\u003EAbstract:\u0026nbsp;\u003C\/strong\u003E\u003C\/h3\u003E\u003Cp\u003EModern decision-making pipelines almost always rely on ML\/AI tools to quantify uncertainty via probability distributions. This naturally leads to stochastic models, which we would like to optimize over to drive better decisions. I will discuss my work in developing fundamental algorithmic techniques for stochastic combinatorial optimization to drive algorithm design in both theory and practice.\u003Cbr\u003E\u003Cbr\u003EI will introduce a novel resource allocation problem arising in cloud data centers: demands arrive online and need to be assigned to devices subject to capacity constraints to maximize utilization. Further, to be robust against failure, our assignment must remain feasible even in failover scenarios \u2013 when some device fails. These novel failover constraints introduce new trade-offs not present in classic assignment problems.\u0026nbsp; We design asymptotically optimal online algorithms for this problem in both the worst- and average-case (where demands are drawn i.i.d. from an unknown distribution). Along the way, we will see a lesser-known probabilistic tool: stochastic monotone matchings. Preliminary experiments on real cloud workloads show the potential of our algorithms to generate more power-efficient allocations, which can save millions and significantly reduce the environmental impact of cloud computing.\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Optimization under Uncertainty: Scheduling with Failover"}],"uid":"34977","created_gmt":"2024-11-06 18:54:36","changed_gmt":"2024-11-06 18:56:44","author":"Julie Smith","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2024-11-19T11:00:00-05:00","event_time_end":"2024-11-19T12:00:00-05:00","event_time_end_last":"2024-11-19T12:00:00-05:00","gmt_time_start":"2024-11-19 16:00:00","gmt_time_end":"2024-11-19 17:00:00","gmt_time_end_last":"2024-11-19 17:00:00","rrule":null,"timezone":"America\/New_York"},"location":"ISyE Groseclose 402","extras":[],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"177814","name":"Postdoc"},{"id":"78771","name":"Public"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"678230":{"#nid":"678230","#data":{"type":"event","title":"ISyE Seminar - Devansh Jalota ","body":[{"value":"\u003Ch3\u003E\u003Cstrong\u003ETitle:\u003C\/strong\u003E\u003C\/h3\u003E\u003Cp\u003EAlgorithm and Incentive Design for Sustainable Resource Allocation: Beyond\u003Cbr\u003EClassical Fisher Markets\u003Cbr\u003E\u0026nbsp;\u003C\/p\u003E\u003Ch3\u003E\u003Cstrong\u003EAbstract:\u0026nbsp;\u003C\/strong\u003E\u003C\/h3\u003E\u003Cp\u003ETechnological advances have opened new avenues for designing market\u003Cbr\u003Emechanisms for resource allocation, from enhancing resource allocation eDiciency with widespread data availability to enabling real-time algorithm implementation. While these technological advancements hold significant promise, they also introduce new societal challenges pertaining to equity, privacy, data uncertainty, and security that existing market mechanisms often fail to address. My research develops data-driven and online learning algorithms and incentive schemes to address these challenges of traditional market mechanisms, thereby advancing the science and practice of market design for sustainable and society-aware resource allocation.\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EIn this talk, I focus on addressing data uncertainty and privacy issues in the context of Fisher markets, a classical framework for fair resource allocation where the problem of computing equilibrium prices relies on complete information of user attributes, which are typically unavailable in practice. Motivated by this practical limitation, we study a modified online incomplete information variant of Fisher markets, where users with privately known utility and budget parameters, drawn i.i.d. from a distribution, arrive sequentially. In this novel market, we establish the limitations of static pricing and design dynamic posted-price algorithms with improved guarantees. Our main result is a posted-price algorithm that solely\u003Cbr\u003Erelies on revealed preference (RP) feedback, i.e., observations of user consumption,\u003Cbr\u003Eachieving the best-known guarantees for first-order algorithms in the RP setting while providing a regret analysis of a fairness-promoting logarithmic objective, unlike typical nonnegative and bounded eDiciency-promoting objectives in online learning.\u0026nbsp;\u003C\/p\u003E\u003Cp\u003ELink to Paper: \u003Ca href=\u0022https:\/\/arxiv.org\/pdf\/2205.00825\u0022\u003Ehttps:\/\/arxiv.org\/pdf\/2205.00825\u003C\/a\u003E\u003C\/p\u003E\u003Ch3\u003E\u003Cstrong\u003EBio:\u0026nbsp;\u003C\/strong\u003E\u003C\/h3\u003E\u003Cp\u003EDevansh Jalota is a PhD candidate in Computational and Mathematical Engineering at Stanford University, where he is a Stanford Interdisciplinary Graduate Fellow. His research develops data-driven learning algorithms and incentive schemes to advance the science and practice of market design for sustainable resource allocation, with a particular focus on applications in future mobility systems and electricity markets. Prior to joining Stanford, he received his bachelor\u2019s in applied mathematics and civil engineering at UC Berkeley.\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Ch3\u003E\u003Cstrong\u003EAbstract:\u0026nbsp;\u003C\/strong\u003E\u003C\/h3\u003E\u003Cp\u003ETechnological advances have opened new avenues for designing market\u003Cbr\u003Emechanisms for resource allocation, from enhancing resource allocation eDiciency with widespread data availability to enabling real-time algorithm implementation. While these technological advancements hold significant promise, they also introduce new societal challenges pertaining to equity, privacy, data uncertainty, and security that existing market mechanisms often fail to address. My research develops data-driven and online learning algorithms and incentive schemes to address these challenges of traditional market mechanisms, thereby advancing the science and practice of market design for sustainable and society-aware resource allocation.\u003Cbr\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EIn this talk, I focus on addressing data uncertainty and privacy issues in the context of Fisher markets, a classical framework for fair resource allocation where the problem of computing equilibrium prices relies on complete information of user attributes, which are typically unavailable in practice. Motivated by this practical limitation, we study a modified online incomplete information variant of Fisher markets, where users with privately known utility\u003Cbr\u003Eand budget parameters, drawn i.i.d. from a distribution, arrive sequentially. In this novel market, we establish the limitations of static pricing and design dynamic posted-price algorithms with improved guarantees. Our main result is a posted-price algorithm that solely relies on revealed preference (RP) feedback, i.e., observations of user consumption, achieving the best-known guarantees for first-order algorithms in the RP setting while providing a regret analysis of a fairness-promoting logarithmic objective, unlike typical nonnegative and bounded eDiciency-promoting objectives in online learning.\u003C\/p\u003E\u003Cp\u003ELink to Paper: \u003Ca href=\u0022https:\/\/arxiv.org\/pdf\/2205.00825\u0022\u003Ehttps:\/\/arxiv.org\/pdf\/2205.00825\u003C\/a\u003E\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Algorithm and Incentive Design for Sustainable Resource Allocation: Beyond Classical Fisher Markets"}],"uid":"34977","created_gmt":"2024-11-06 18:14:19","changed_gmt":"2024-11-06 18:15:42","author":"Julie Smith","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2024-11-21T11:00:00-05:00","event_time_end":"2024-11-21T12:00:00-05:00","event_time_end_last":"2024-11-21T12:00:00-05:00","gmt_time_start":"2024-11-21 16:00:00","gmt_time_end":"2024-11-21 17:00:00","gmt_time_end_last":"2024-11-21 17:00:00","rrule":null,"timezone":"America\/New_York"},"location":"ISyE Groseclose 402","extras":[],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"177814","name":"Postdoc"},{"id":"78771","name":"Public"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"678124":{"#nid":"678124","#data":{"type":"event","title":"Master of Science in Urban Analytics Information Session and Q\u0026A","body":[{"value":"\u003Cdiv\u003E\u003Cp\u003EMeet MS Urban Analytics program director, Dr. Subhro Guhathakurta, program alumni, and the School of City and Regional Planning academic advisor and graduate program coordinator, Ms. Anna Traykova, to learn more about the program.\u003Cbr\u003E\u0026nbsp;\u003C\/p\u003E\u003C\/div\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cdiv\u003E\u003Cp\u003EMeet MS Urban Analytics program director, Dr. Subhro Guhathakurta, program alumni, and the School of City and Regional Planning academic advisor and graduate program coordinator, Ms. Anna Traykova, to learn more about the program\u003C\/p\u003E\u003C\/div\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"  Meet MS Urban Analytics program director, Dr. Subhro Guhathakurta, program alumni, and the School of City and Regional Planning academic advisor and graduate program coordinator, Ms. Anna Traykova, to learn more about the program"}],"uid":"27764","created_gmt":"2024-11-04 15:01:51","changed_gmt":"2024-11-06 17:41:16","author":"Scott Jacobson","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2025-01-29T11:00:00-05:00","event_time_end":"2025-01-29T12:00:00-05:00","event_time_end_last":"2025-01-29T12:00:00-05:00","gmt_time_start":"2025-01-29 16:00:00","gmt_time_end":"2025-01-29 17:00:00","gmt_time_end_last":"2025-01-29 17:00:00","rrule":null,"timezone":"America\/New_York"},"location":"virtual","extras":[],"related_links":[{"url":"https:\/\/gatech.zoom.us\/meeting\/register\/tJcuc-6grzIuGNK66B4nVAEYQU5w-RiGVn8e#\/registration","title":"Register to Attend"}],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1788","name":"Other\/Miscellaneous"}],"invited_audience":[],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"678149":{"#nid":"678149","#data":{"type":"event","title":"ISYE Statistics Seminar - Dr. Yanxun Xu","body":[{"value":"\u003Cp\u003E\u003Cstrong\u003ETitle\u003C\/strong\u003E: Precision Medicine in HIV\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EAbstract\u003C\/strong\u003E: The use of antiretroviral therapy (ART) has significantly reduced HIV-related mortality and morbidity, transforming HIV infection to a chronic disease with the care now focusing on treatment adherence, comorbidities including mental health, and other long-term outcomes. Since combination ART with three or more drugs of different mechanisms or against different targets is recommended for all people living with HIV (PWH) and they must continue on it indefinitely once started, understanding the long-term ART effects on health outcomes and personalizing ART treatment based on individuals\u2019 characteristics is crucial for optimizing PWH\u2019s health outcomes and facilitating precision medicine in HIV. In this talk, I will present reinforcement learning (RL) methods designed to learn and understand the impact of ART on the health outcomes of PWH, and explore the future of HIV care through incorporating large language models (LLM) with RL.\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EBio:\u0026nbsp;\u003C\/strong\u003EDr.\u0026nbsp;Yanxun\u0026nbsp;Xu\u0026nbsp;is an Associate Professor and Joseph \u0026amp; Suzanne Jenniches Faculty Scholar in the Department of Applied Mathematics and Statistics, Division of Biostatistics in the School of Medicine at Johns Hopkins University. Her research interests lie in developing theory and methods for a broad range of problems, such as reinforcement learning, high-dimensional data analysis, Bayesian nonparametric statistics, and uncertainty quantification. She also develops new statistical and machine learning methods for various applications, including electronic health records, dynamic treatment regimens, cancer genomics, early detection of Alzheimer\u2019s disease, mental health in people with HIV, and early-phase clinical trial designs. Her research has been continuously funded by NSF, NIH, and industries.\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003E\u003Cstrong\u003EAbstract\u003C\/strong\u003E: The use of antiretroviral therapy (ART) has significantly reduced HIV-related mortality and morbidity, transforming HIV infection to a chronic disease with the care now focusing on treatment adherence, comorbidities including mental health, and other long-term outcomes. Since combination ART with three or more drugs of different mechanisms or against different targets is recommended for all people living with HIV (PWH) and they must continue on it indefinitely once started, understanding the long-term ART effects on health outcomes and personalizing ART treatment based on individuals\u2019 characteristics is crucial for optimizing PWH\u2019s health outcomes and facilitating precision medicine in HIV. In this talk, I will present reinforcement learning (RL) methods designed to learn and understand the impact of ART on the health outcomes of PWH, and explore the future of HIV care through incorporating large language models (LLM) with RL.\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Precision Medicine in HIV"}],"uid":"36433","created_gmt":"2024-11-04 21:53:51","changed_gmt":"2024-11-04 21:55:28","author":"mrussell89","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2024-11-05T11:00:00-05:00","event_time_end":"2024-11-05T12:00:00-05:00","event_time_end_last":"2024-11-05T12:00:00-05:00","gmt_time_start":"2024-11-05 16:00:00","gmt_time_end":"2024-11-05 17:00:00","gmt_time_end_last":"2024-11-05 17:00:00","rrule":null,"timezone":"America\/New_York"},"location":"Groseclose 402","extras":[],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"177814","name":"Postdoc"},{"id":"78771","name":"Public"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"678123":{"#nid":"678123","#data":{"type":"event","title":"Master of Science in Urban Analytics Information Session and Q\u0026A","body":[{"value":"\u003Cdiv\u003E\u003Cp\u003EMeet MS Urban Analytics program director, Dr. Subhro Guhathakurta, program alumni, and the School of City and Regional Planning academic advisor and graduate program coordinator, Ms. Anna Traykova, to learn more about the program\u003C\/p\u003E\u003C\/div\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cdiv\u003E\u003Cp\u003EMeet MS Urban Analytics program director, Dr. Subhro Guhathakurta, program alumni, and the School of City and Regional Planning academic advisor and graduate program coordinator, Ms. Anna Traykova, to learn more about the program\u003C\/p\u003E\u003C\/div\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"  Meet MS Urban Analytics program director, Dr. Subhro Guhathakurta, program alumni, and the School of City and Regional Planning academic advisor and graduate program coordinator, Ms. Anna Traykova, to learn more about the program"}],"uid":"27764","created_gmt":"2024-11-04 14:57:42","changed_gmt":"2024-11-04 15:01:31","author":"Scott Jacobson","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2024-11-20T11:00:00-05:00","event_time_end":"2024-11-20T12:00:00-05:00","event_time_end_last":"2024-11-20T12:00:00-05:00","gmt_time_start":"2024-11-20 16:00:00","gmt_time_end":"2024-11-20 17:00:00","gmt_time_end_last":"2024-11-20 17:00:00","rrule":null,"timezone":"America\/New_York"},"location":"virtual","extras":[],"related_links":[{"url":"https:\/\/gatech.zoom.us\/meeting\/register\/tJctdumvrj8tE9yRhEJnSbHPaT2283GSCUu0#\/registration","title":"Register to Attend"}],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1788","name":"Other\/Miscellaneous"}],"invited_audience":[],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"677852":{"#nid":"677852","#data":{"type":"event","title":"ISYE Statistics Seminar - Dr. Rina Foygel Barber","body":[{"value":"\u003Cdiv\u003E\u003Cstrong\u003EAbstract:\u0026nbsp;\u003C\/strong\u003E\u003C\/div\u003E\u003Cdiv\u003EAlgorithmic stability for regression and classification\u003Cbr\u003E\u003Cbr\u003EIn a supervised learning setting, a model fitting algorithm is unstable if small perturbations to the input (the training data) can often lead to large perturbations in the output (say, predictions returned by the fitted model). Algorithmic stability is a desirable property with many important implications such as generalization and robustness, but testing the stability property empirically is known to be impossible in the setting of complex black-box models. In this work, we establish that bagging any black-box regression algorithm automatically ensures that stability holds, with no assumptions on the algorithm or the data. Furthermore, we construct a new framework for defining stability in the context of classification, and show that using bagging to estimate our uncertainty about the output label will again allow stability guarantees for any black-box model. This work is joint with Jake Soloff and Rebecca Willett.\u003C\/div\u003E\u003Cdiv\u003E\u0026nbsp;\u003C\/div\u003E\u003Cdiv\u003E\u0026nbsp;\u003C\/div\u003E\u003Cdiv\u003E\u0026nbsp;\u003C\/div\u003E\u003Cdiv\u003E\u0026nbsp;\u003C\/div\u003E\u003Cdiv\u003E\u003Cstrong\u003EBio:\u0026nbsp;\u003C\/strong\u003E\u003C\/div\u003E\u003Cdiv\u003E\u0026nbsp;\u003C\/div\u003E\u003Cdiv\u003EProfessor in the \u003Ca href=\u0022http:\/\/www.stat.uchicago.edu\/\u0022\u003EDepartment of Statistics at the University of Chicago\u003C\/a\u003E. Before starting at U of C, I was a NSF postdoctoral fellow during 2012-13 in the \u003Ca href=\u0022http:\/\/www-stat.stanford.edu\/\u0022\u003EDepartment of Statistics at Stanford University\u003C\/a\u003E, supervised by \u003Ca href=\u0022http:\/\/www-stat.stanford.edu\/~candes\/\u0022\u003EEmmanuel Cand\u00e8s\u003C\/a\u003E. I received my PhD in Statistics at the University of Chicago in 2012, advised by \u003Ca href=\u0022https:\/\/www.math.cit.tum.de\/statistics\/personen\/mathias-drton\/\u0022\u003EMathias Drton\u003C\/a\u003E and \u003Ca href=\u0022http:\/\/ttic.uchicago.edu\/~nati\/\u0022\u003ENati Srebro\u003C\/a\u003E, and a MS in Mathematics at the University of Chicago in 2009. Prior to graduate school, I was a mathematics teacher at the \u003Ca href=\u0022http:\/\/www.parkschool.net\/academics\/upper-school\/program-of-studies\/mathematics\/\u0022\u003EPark School of Baltimore\u003C\/a\u003E from 2005 to 2007, and received an ScB in Mathematics from Brown University in 2005.\u003C\/div\u003E\u003Cp\u003E\u003Cbr\u003E\u0026nbsp;\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EIn a supervised learning setting, a model fitting algorithm is unstable if small perturbations to the input (the training data) can often lead to large perturbations in the output (say, predictions returned by the fitted model). Algorithmic stability is a desirable property with many important implications such as generalization and robustness, but testing the stability property empirically is known to be impossible in the setting of complex black-box models. In this work, we establish that bagging any black-box regression algorithm automatically ensures that stability holds, with no assumptions on the algorithm or the data. Furthermore, we construct a new framework for defining stability in the context of classification, and show that using bagging to estimate our uncertainty about the output label will again allow stability guarantees for any black-box model. This work is joint with Jake Soloff and Rebecca Willett.\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Algorithmic stability for regression and classification"}],"uid":"36433","created_gmt":"2024-10-23 13:59:36","changed_gmt":"2024-10-28 13:49:59","author":"mrussell89","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2024-10-29T14:00:00-04:00","event_time_end":"2024-10-29T15:00:00-04:00","event_time_end_last":"2024-10-29T15:00:00-04:00","gmt_time_start":"2024-10-29 18:00:00","gmt_time_end":"2024-10-29 19:00:00","gmt_time_end_last":"2024-10-29 19:00:00","rrule":null,"timezone":"America\/New_York"},"location":"Groseclose 402","extras":[],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"177814","name":"Postdoc"},{"id":"78771","name":"Public"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"677927":{"#nid":"677927","#data":{"type":"event","title":"ISyE Seminar - Jim Luedtke","body":[{"value":"\u003Cp\u003ETitle:\u003C\/p\u003E\u003Cp\u003EProbing-enhanced stochastic programming\u003C\/p\u003E\u003Cp\u003EAbstract:\u003C\/p\u003E\u003Cp\u003EWe consider a two-stage stochastic program where the decision-maker has the opportunity to obtain information about the distribution of the random variables X through a set of discrete actions that we refer to as probing. \u0026nbsp;Probing allows the decision-maker to observe components of a random vector Y that is jointly-distributed with X. We propose a three-stage optimization model for this problem, where the first-stage variables select components of Y to observe. \u0026nbsp;In the case that X and Y have finite support, a model of Goel and Grossmann can be applied to obtain a formulation of this problem whose size is proportional to the square of cardinality of the sample space of the random variables. \u0026nbsp; We propose to solve the model using bounds obtained from an information-based relaxation, combined with a branching scheme that enforces the consistency of decisions with observed information. \u0026nbsp;The branch-and-bound approach can naturally be combined with sampling in order to estimate both lower and upper bounds on the optimal solution value even for problems with continuous distribution. \u0026nbsp;We demonstrate the approach on instances of a stochastic facility location problem.\u003C\/p\u003E\u003Cp\u003EThis is joint work with Zhichao Ma, Jeff Linderoth, Youngdae Kim, and Logan Matthews.\u003C\/p\u003E\u003Cp\u003EBio:\u003C\/p\u003E\u003Cp\u003EJames (Jim) Luedtke is a Professor in the department of Industrial and Systems Engineering at the University of Wisconsin-Madison and a Discovery Fellow at the Wisconsin Institute for Discovery. Luedtke earned his Ph.D. at Georgia Tech and did postdoctoral work at the IBM T.J. Watson Research Center. Luedtke\u2019s research is focused on methods for solving stochastic and mixed-integer optimization problems, as well as applications of such models. His current research interests include investigation of computational methods for solving two and multi-stage stochastic integer programming problems, and integration of optimization and machine learning models. Luedtke serves on the editorial boards of the journals SIAM Journal on Optimization and Mathematical Programming Computation and is chair of the Mathematical Optimization Society Publications Committee.\u003Cbr\u003EGeorgia Tech ISyE Departmental Seminar Speaker Invitation\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EWe consider a two-stage stochastic program where the decision-maker has the opportunity to obtain information about the distribution of the random variables X through a set of discrete actions that we refer to as probing. \u0026nbsp;Probing allows the decision-maker to observe components of a random vector Y that is jointly-distributed with X. We propose a three-stage optimization model for this problem, where the first-stage variables select components of Y to observe. \u0026nbsp;In the case that X and Y have finite support, a model of Goel and Grossmann can be applied to obtain a formulation of this problem whose size is proportional to the square of cardinality of the sample space of the random variables. \u0026nbsp; We propose to solve the model using bounds obtained from an information-based relaxation, combined with a branching scheme that enforces the consistency of decisions with observed information. \u0026nbsp;The branch-and-bound approach can naturally be combined with sampling in order to estimate both lower and upper bounds on the optimal solution value even for problems with continuous distribution. \u0026nbsp;We demonstrate the approach on instances of a stochastic facility location problem.\u003C\/p\u003E\u003Cp\u003EThis is joint work with Zhichao Ma, Jeff Linderoth, Youngdae Kim, and Logan Matthews.\u003Cbr\u003E\u0026nbsp;\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":" Probing-enhanced stochastic programming"}],"uid":"36374","created_gmt":"2024-10-25 19:03:18","changed_gmt":"2024-10-25 19:06:11","author":"mwelch39","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2024-11-15T11:30:00-05:00","event_time_end":"2024-11-15T12:30:00-05:00","event_time_end_last":"2024-11-15T12:30:00-05:00","gmt_time_start":"2024-11-15 16:30:00","gmt_time_end":"2024-11-15 17:30:00","gmt_time_end_last":"2024-11-15 17:30:00","rrule":null,"timezone":"America\/New_York"},"location":" ISYE Groseclose 402","extras":[],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"177814","name":"Postdoc"},{"id":"78771","name":"Public"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"677918":{"#nid":"677918","#data":{"type":"event","title":"ISyE Seminar - Shmuel  S. Oren","body":[{"value":"\u003Cp\u003ETitle:\u003C\/p\u003E\u003Cp\u003EMobilizing Demand Flexibility in Wholesale Electricity Markets with VPP Supply Functions\u003C\/p\u003E\u003Cp\u003EAbstract:\u003C\/p\u003E\u003Cp\u003EFERC Order 2222 requires ISOs to develop market mechanisms that will enable aggregators of distributed resources to participate in the electricity wholesale market. In this work we describe the construction of a supply function for an aggregator\u2019s virtual power plant (VPP) based on a portfolio of curtailable devices, categorized into priority tranches, that are controlled by the aggregator through edge technology behind the meter. Only the nameplate capacity of the curtailed devices are known to the aggregator while the energy yield from curtailment is uncertain. However, the supply function of the VPP offered by the aggregator into the wholesale market must specify deliverable energy quantity as function of wholesale price, like any other generator. We employ a revenue management methodology to construct a supply function with controlled delivery risk, based on priority tranches of the curtailed devices and offline estimates of the energy yield probability distributions. This work is part of an ARPA E project aimed at implementing a VPP based on aggregated demand curtailments at PJM.\u003C\/p\u003E\u003Cp\u003E(Joint work with Hung Po Chao, ETA and Alex Papalexopoulos, ZOME)\u003C\/p\u003E\u003Cp\u003EBio:\u003C\/p\u003E\u003Cp\u003EDr. Shmuel S. Oren is Professor of the Graduate School in the Department of Industrial Engineering and Operations Research at UC Berkeley and is a co-founder and the Berkeley site director, of PSerc. He has been a member of the California ISO Market Surveillance Committee and a consultant to many private and public entities in the US and abroad. He holds a Ph.D in Engineering Economic Systems from Stanford University. He is a recipient of the INFORMS Hotelling Medal for life time achievement in Energy Natural Resources and Environment and the IEEE Outstanding Power Systems Educator Award. He is a Member of the US National Academy of Engineering, a Life Fellow of the IEEE and Fellow of INFORMS.\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EFERC Order 2222 requires ISOs to develop market mechanisms that will enable aggregators of distributed resources to participate in the electricity wholesale market. In this work we describe the construction of a supply function for an aggregator\u2019s virtual power plant (VPP) based on a portfolio of curtailable devices, categorized into priority tranches, that are controlled by the aggregator through edge technology behind the meter. Only the nameplate capacity of the curtailed devices are known to the aggregator while the energy yield from curtailment is uncertain. However, the supply function of the VPP offered by the aggregator into the wholesale market must specify deliverable energy quantity as function of wholesale price, like any other generator. We employ a revenue management methodology to construct a supply function with controlled delivery risk, based on priority tranches of the curtailed devices and offline estimates of the energy yield probability distributions. This work is part of an ARPA E project aimed at implementing a VPP based on aggregated demand curtailments at PJM.\u003Cbr\u003E\u0026nbsp;\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Mobilizing Demand Flexibility in Wholesale Electricity Markets with VPP Supply Functions"}],"uid":"36374","created_gmt":"2024-10-25 17:49:15","changed_gmt":"2024-10-25 17:49:15","author":"mwelch39","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2024-11-01T11:30:00-04:00","event_time_end":"2024-11-01T12:30:00-04:00","event_time_end_last":"2024-11-01T12:30:00-04:00","gmt_time_start":"2024-11-01 15:30:00","gmt_time_end":"2024-11-01 16:30:00","gmt_time_end_last":"2024-11-01 16:30:00","rrule":null,"timezone":"America\/New_York"},"location":" ISYE Groseclose 402","extras":[],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"177814","name":"Postdoc"},{"id":"78771","name":"Public"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"677880":{"#nid":"677880","#data":{"type":"event","title":"SCL Lunch and Learn: \u201cUnlocking the Power of Data: Transforming Supply Chain Performance\u0022","body":[{"value":"\u003Cp\u003E\u003Cstrong\u003EJoin us for an interactive webinar to learn how businesses can leverage data analytics to drive significant improvements in supply chain performance.\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EThursday, December 5, 2024 | 12-1pm ET\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003EWe will discuss the role of statistical analysis, performance metrics, and advanced tools for supply chain transformation. Practical examples and case studies will be shared to illustrate the impact of these techniques.\u003C\/p\u003E\u003Cp\u003E\u003Ca href=\u0022https:\/\/gatech.zoom.us\/webinar\/register\/1617297110089\/WN_cnMiMMHMRGuO_ZB6zAgtVg\u0022\u003E\u003Cstrong\u003ERegister Online to Attend\u003C\/strong\u003E\u003C\/a\u003E\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003ELearn how businesses can leverage data analytics to drive significant improvements in supply chain performance.\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Learn how businesses can leverage data analytics to drive significant improvements in supply chain performance."}],"uid":"27233","created_gmt":"2024-10-24 12:10:59","changed_gmt":"2024-10-24 13:58:02","author":"Andy Haleblian","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2024-12-05T12:00:00-05:00","event_time_end":"2024-12-05T13:00:00-05:00","event_time_end_last":"2024-12-05T13:00:00-05:00","gmt_time_start":"2024-12-05 17:00:00","gmt_time_end":"2024-12-05 18:00:00","gmt_time_end_last":"2024-12-05 18:00:00","rrule":null,"timezone":"America\/New_York"},"location":"Online via Zoom","extras":[],"hg_media":{"675422":{"id":"675422","type":"image","title":"SCL Lunch and Learn: \u201cUnlocking the Power of Data: Transforming Supply Chain Performance\u0022","body":null,"created":"1729772014","gmt_created":"2024-10-24 12:13:34","changed":"1729772014","gmt_changed":"2024-10-24 12:13:34","alt":"SCL Lunch and Learn: \u201cUnlocking the Power of Data: Transforming Supply Chain Performance\u0022","file":{"fid":"259043","name":"GTSCL-LNL_202412.png","image_path":"\/sites\/default\/files\/2024\/10\/24\/GTSCL-LNL_202412.png","image_full_path":"http:\/\/hg.gatech.edu\/\/sites\/default\/files\/2024\/10\/24\/GTSCL-LNL_202412.png","mime":"image\/png","size":152602,"path_740":"http:\/\/hg.gatech.edu\/sites\/default\/files\/styles\/740xx_scale\/public\/2024\/10\/24\/GTSCL-LNL_202412.png?itok=9B5Iq6km"}}},"media_ids":["675422"],"related_links":[{"url":"https:\/\/www.scl.gatech.edu\/dec-lnl","title":"Register Online to Attend the Webinar"},{"url":"https:\/\/www.scl.gatech.edu\/education\/professional-education\/course\/scaoc","title":"About our Supply Chain Optimization and Prescriptive Analytics course"},{"url":"https:\/\/www.scl.gatech.edu\/sites\/default\/files\/downloads\/gtscl-sca_brochure.pdf","title":"Supply Chain Analytics Professional Certificate"}],"groups":[{"id":"1250","name":"Center for Health and Humanitarian Systems (CHHS)"},{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"},{"id":"1243","name":"The Supply Chain and Logistics Institute (SCL)"}],"categories":[],"keywords":[{"id":"3615","name":"information session"},{"id":"186857","name":"go-gtmi"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"10377","name":"Career\/Professional development"}],"invited_audience":[{"id":"78771","name":"Public"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[{"value":"\u003Cp\u003Ecourse@scl.gatech.edu\u003C\/p\u003E","format":"limited_html"}],"email":[],"slides":[],"orientation":[],"userdata":""}},"677879":{"#nid":"677879","#data":{"type":"event","title":"ISyE Seminar - Michael P. Johnson","body":[{"value":"\u003Cp\u003ETitle:\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;Diversity, equity and inclusion and racial and social justice in the field of operations research and analytics: Results from an examination of recent scholarship and university academic programs\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EAbstract:\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EThis talk describes a project to better understand the state of diversity, equity and inclusion in the decision sciences. By doing so, we can identify areas for improvement in scholarship, practice and education. I discuss two recent studies: a review of recent scholarship in operations research, operations management and supply chain management intended to identify opportunities for DEI-forward work in these fields, and a survey of university programs in OR\/analytics to learn about the characteristics of students, faculty, DEI-related curricula and supports for traditionally-underrepresented students.\u003C\/p\u003E\u003Cp\u003ECo-authors:\u003C\/p\u003E\u003Cp\u003ETayo Fabusuyi, Assistant Research Scientist, University of Michigan Transportation Research Institute\u003C\/p\u003E\u003Cp\u003EElham Hesari, doctoral candidate, Public Policy PhD program, University of Massachusetts Boston\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EBio:\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EMichael P. Johnson is professor in the Department of Public Policy and Public Affairs and special assistant to the chancellor for Black life at University of Massachusetts Boston. He received his PhD in Operations Research from Northwestern University and his BS from Morehouse College. His research addresses decision modeling for nonprofit organizations and government agencies. His primary application areas include affordable and assisted housing, community development, climate change response, and diversity, equity and inclusion in the decision sciences. He has authored multiple books, including Supporting Shrinkage: Planning and Decision-Making for Legacy Cities (SUNY Press, 2021) and Decision Science for Housing and Community Development: Localized and Evidence\u2010Based Responses to Distressed Housing and Blighted Communities (Wiley, 2016). He has served as INFORMS vice-president of Chapters and Fora, and was founding chair of the Diversity, Equity and Inclusion committee. His research has received support from the National Science Foundation, the Abell Foundation and the INFORMS Diversity Ambassadors program.\u003Cbr\u003E\u0026nbsp;\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EThis talk describes a project to better understand the state of diversity, equity and inclusion in the decision sciences. By doing so, we can identify areas for improvement in scholarship, practice and education. I discuss two recent studies: a review of recent scholarship in operations research, operations management and supply chain management intended to identify opportunities for DEI-forward work in these fields, and a survey of university programs in OR\/analytics to learn about the characteristics of students, faculty, DEI-related curricula and supports for traditionally underrepresented students.\u003C\/p\u003E\u003Cp\u003E\u003Cbr\u003E\u0026nbsp;\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Diversity, equity and inclusion and racial and social justice in the field of operations research and analytics: Results from an examination of recent scholarship and university academic programs"}],"uid":"36374","created_gmt":"2024-10-23 22:25:38","changed_gmt":"2024-10-23 22:29:26","author":"mwelch39","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2024-10-25T11:30:00-04:00","event_time_end":"2024-10-25T12:30:00-04:00","event_time_end_last":"2024-10-25T12:30:00-04:00","gmt_time_start":"2024-10-25 15:30:00","gmt_time_end":"2024-10-25 16:30:00","gmt_time_end_last":"2024-10-25 16:30:00","rrule":null,"timezone":"America\/New_York"},"location":" ISYE Groseclose 402","extras":[],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"177814","name":"Postdoc"},{"id":"78771","name":"Public"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"677185":{"#nid":"677185","#data":{"type":"event","title":"LeeAnn and Walter Muller Distinguished Lecture Series","body":[{"value":"\u003Ch2\u003E2024 LeeAnn and Walter Muller Distinguished Lecture Series, Dr. Russell Meller\u003C\/h2\u003E\u003Cp\u003E\u003Cstrong\u003EFrom Academia to Industry: Observations of a Traveler\u003C\/strong\u003E\u003Cbr\u003EMarcus Nanotechnology Building, Room 1116-1118\u0026nbsp;\u003Cbr\u003ETuesday, October 29, 2024\u003Cbr\u003E3:30-4:30PM\u0026nbsp;\u003Cbr\u003EReception held in the Atrium\u003C\/p\u003E\u003Ch5\u003E\u003Cstrong\u003ERSVP here: \u003C\/strong\u003E\u003Ca href=\u0022https:\/\/eforms.isye.gatech.edu\/2024-distinguished-lecture-series\u0022\u003E\u003Cstrong\u003Ehttps:\/\/eforms.isye.gatech.edu\/2024-distinguished-lecture-series\u003C\/strong\u003E\u003C\/a\u003E\u003C\/h5\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Ch2\u003EAbstract:\u0026nbsp;\u003C\/h2\u003E\u003Cp\u003ESome people meticulously plan every step of their career, mapping out milestones with precision. I\u2019m not one of those people. Instead, I follow where my curiosity leads, seizing opportunities when they present themselves. In transitioning from academia to industry, I\u0027ve observed how to transform a job into a fulfilling career, the distinct differences between these two worlds, and the importance of continuous learning in the era of AI. I\u0027ll share these reflections, and I look forward to hearing your thoughts on them.\u003C\/p\u003E\u003Ch2\u003EAbout: Dr. Russell Meller, Keynote Speaker\u003C\/h2\u003E\u003Cp\u003EDr. Russell D. Meller is the Chief Scientist at FORTNA, a company that designs and implements complex distribution centers.\u0026nbsp; He oversees a group of 25+ researchers that develop all algorithms in the FortnaWES\u2122 software, perform data analysis on design projects, simulation of designs, emulation of material handling systems and all modeling aspects of the FortnaDCdesign Suite\u2122. Russ holds a B.S., M.S. \u0026amp; Ph.D. in Industrial and Operations Engineering from the University of Michigan.\u0026nbsp; He\u2019s been with FORTNA for over 10 years after an illustrious 20+ year career in academia, holding positions at Auburn University, Virginia Tech, the University of Arkansas and the Technical University of Graz (Austria).\u0026nbsp; In 2020, he was elected to the National Academy of Engineering, the highest honor for an Engineer in the United States. \u0026nbsp;His election was due in large part to creating a scalable design methodology at FORTNA for distribution centers.\u003C\/p\u003E\u003Cp\u003EDr. Russell D. Meller is the Chief Scientist at FORTNA, a company that designs and implements complex distribution centers.\u0026nbsp; Russ holds a B.S., M.S. \u0026amp; Ph.D. in Industrial and Operations Engineering from the University of Michigan.\u0026nbsp;He\u2019s been with FORTNA for over 10 years after an illustrious 20+ year career in academia, holding positions at Auburn University, Virginia Tech, the University of Arkansas and the Technical University of Graz (Austria).\u0026nbsp; In 2020, he was elected to the National Academy of Engineering, the highest honor for an Engineer in the United States.\u0026nbsp;His election was due in large part to creating a scalable design methodology at FORTNA for distribution centers.\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EJoin us for the Distinguished Lecture Series, with keynote speaker: Dr. Russell Meller, Chief Scientist at FORTNA\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"From Academia to Industry: Observations of a Traveler"}],"uid":"36284","created_gmt":"2024-09-27 20:47:10","changed_gmt":"2024-10-23 18:44:36","author":"chenriquez8","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2024-10-29T15:30:00-04:00","event_time_end":"2024-10-29T16:30:00-04:00","event_time_end_last":"2024-10-29T16:30:00-04:00","gmt_time_start":"2024-10-29 19:30:00","gmt_time_end":"2024-10-29 20:30:00","gmt_time_end_last":"2024-10-29 20:30:00","rrule":null,"timezone":"America\/New_York"},"location":"Marcus Nanotechnology Building, Room 1116-1118 ","extras":["free_food","freebies"],"hg_media":{"675174":{"id":"675174","type":"image","title":"Distinguished Lecture Series","body":null,"created":"1727794285","gmt_created":"2024-10-01 14:51:25","changed":"1727794285","gmt_changed":"2024-10-01 14:51:25","alt":"Distinguished Lecture Series","file":{"fid":"258776","name":"Distinguished Lecture Series  (1).png","image_path":"\/sites\/default\/files\/2024\/10\/01\/Distinguished%20Lecture%20Series%20%20%281%29.png","image_full_path":"http:\/\/hg.gatech.edu\/\/sites\/default\/files\/2024\/10\/01\/Distinguished%20Lecture%20Series%20%20%281%29.png","mime":"image\/png","size":4760149,"path_740":"http:\/\/hg.gatech.edu\/sites\/default\/files\/styles\/740xx_scale\/public\/2024\/10\/01\/Distinguished%20Lecture%20Series%20%20%281%29.png?itok=kAVIfbVy"}}},"media_ids":["675174"],"related_links":[{"url":"https:\/\/eforms.isye.gatech.edu\/2024-distinguished-lecture-series","title":"RSVP Link"}],"groups":[{"id":"660354","name":"Center for Academics, Success, and Equity"},{"id":"660346","name":"Master of Science in Analytics"},{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"},{"id":"1243","name":"The Supply Chain and Logistics Institute (SCL)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"10377","name":"Career\/Professional development"},{"id":"1788","name":"Other\/Miscellaneous"},{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"177814","name":"Postdoc"},{"id":"78771","name":"Public"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"677594":{"#nid":"677594","#data":{"type":"event","title":"SCL January 2025 Supply Chain and Logistics Career Fair","body":[{"value":"\u003Cp\u003EGeorgia Tech Supply Chain students and employers, please join us for our fall Supply Chain Day!\u0026nbsp;\u003C\/p\u003E\u003Ch3\u003E\u003Cstrong\u003EEvent Details\u003C\/strong\u003E\u003C\/h3\u003E\u003Ch4\u003EOn Campus\/In-Person (Georgia Tech Exhibition Hall)\u003C\/h4\u003E\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EWednesday, January 15, 2025 | 9:30am-1pm ET\u003C\/strong\u003E\u003C\/li\u003E\u003C\/ul\u003E\u003Ch3\u003EStudents\u003C\/h3\u003E\u003Cp\u003E\u003Cstrong\u003EWe strongly encourage you to attend to seek full-time employment\u003C\/strong\u003E, \u003Cstrong\u003Einternships, and projects\u003C\/strong\u003E (rather than waiting until the end of the semester).\u003C\/p\u003E\u003Ch3\u003EOrganizations\u003C\/h3\u003E\u003Cp\u003EIf you are interested in hosting a table for the upcoming session, please let us know after reviewing the below information within our website. Registration closes Monday, December 30th.\u003C\/p\u003E\u003Ch4\u003EMORE INFORMATION AND EVENT REGISTRATION\u003C\/h4\u003E\u003Cp\u003EVisit\u0026nbsp;\u003Ca href=\u0022https:\/\/www.scl.gatech.edu\/outreach\/supplychainday\u0022\u003E\u003Cstrong\u003Ehttps:\/\/www.scl.gatech.edu\/outreach\/supplychainday\u003C\/strong\u003E\u003C\/a\u003E.\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EGeorgia Tech Supply Chain\u0026nbsp;students and employers, please join us for our spring Supply Chain Day! We will be hosting an On Campus session\u0026nbsp;Wednesday, January 15, 2025 from 9:30am-1pm ET at the Georgia Tech Exhibition Hall.\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Supply chain and logistics career fair where industry supply chain representatives meet Georgia Tech students."}],"uid":"27233","created_gmt":"2024-10-15 18:50:04","changed_gmt":"2024-10-15 18:53:45","author":"Andy Haleblian","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2025-01-15T09:30:00-05:00","event_time_end":"2025-01-15T13:00:00-05:00","event_time_end_last":"2025-01-15T13:00:00-05:00","gmt_time_start":"2025-01-15 14:30:00","gmt_time_end":"2025-01-15 18:00:00","gmt_time_end_last":"2025-01-15 18:00:00","rrule":null,"timezone":"America\/New_York"},"location":"Georgia Tech Exhibition Hall","extras":[],"hg_media":{"675325":{"id":"675325","type":"image","title":"5-SCDay-_20250115.jpg","body":null,"created":"1729018361","gmt_created":"2024-10-15 18:52:41","changed":"1729018361","gmt_changed":"2024-10-15 18:52:41","alt":"Supply Chain Day is Wednesday, January 15, 2025 | 9:30am-1pm ET","file":{"fid":"258936","name":"5-SCDay-_20250115.jpg","image_path":"\/sites\/default\/files\/2024\/10\/15\/5-SCDay-_20250115.jpg","image_full_path":"http:\/\/hg.gatech.edu\/\/sites\/default\/files\/2024\/10\/15\/5-SCDay-_20250115.jpg","mime":"image\/jpeg","size":235674,"path_740":"http:\/\/hg.gatech.edu\/sites\/default\/files\/styles\/740xx_scale\/public\/2024\/10\/15\/5-SCDay-_20250115.jpg?itok=XuYEtwd1"}}},"media_ids":["675325"],"related_links":[{"url":"https:\/\/www.scl.gatech.edu\/outreach\/supplychainday","title":"Register online to attend (for Georgia Tech students)"},{"url":"https:\/\/www.scl.gatech.edu","title":"Supply Chain and Logistics Institute website"}],"groups":[{"id":"1250","name":"Center for Health and Humanitarian Systems (CHHS)"},{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"},{"id":"1243","name":"The Supply Chain and Logistics Institute (SCL)"}],"categories":[],"keywords":[{"id":"780","name":"employment"},{"id":"9845","name":"GTSCL"},{"id":"233","name":"Logistics"},{"id":"167074","name":"Supply Chain"},{"id":"1996","name":"Recruiting"},{"id":"5172","name":"career day"},{"id":"122741","name":"physical internet"},{"id":"186857","name":"go-gtmi"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"10377","name":"Career\/Professional development"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"177814","name":"Postdoc"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[{"value":"\u003Cp\u003Eevent@scl.gatech.edu\u003C\/p\u003E","format":"limited_html"}],"email":[],"slides":[],"orientation":[],"userdata":""}},"676897":{"#nid":"676897","#data":{"type":"event","title":"ISYE Statistics Seminar - Pierre Bellec","body":[{"value":"\u003Cp\u003E\u003Cstrong\u003ETitle:\u003C\/strong\u003E Uncertainty quantification for iterative algorithms\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EAbstract:\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003EThis paper investigates the iterates obtained from iterative algorithms in high-dimensional linear regression problems, in the regime where the feature dimension is comparable with the sample size. \u0026nbsp;The analysis and proposed estimators are applicable to Gradient Descent (GD), proximal GD and their accelerated variants such as Fast Iterative Soft-Thresholding (FISTA). \u0026nbsp;The paper proposes novel estimators for the generalization error of the iterate for any fixed iteration along the trajectory. These estimators are proved to be root-n consistent under Gaussian designs. \u0026nbsp;Applications to early-stopping are provided: when the generalization error of the iterates is a U-shape function of the iterations, the estimates allow to select from the data an iteration that achieves the smallest generalization error along the trajectory. \u0026nbsp;Additionally, we provide a technique for developing debiasing corrections and valid confidence intervals for the components of the true coefficient vector from the iterate at any finite iteration.\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EBio:\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003EPierre C Bellec is an associate professor in the Statistics Department at Rutgers University. He was elected fellow of the Institute of Mathematical Statistics in 2023. In the last few years, his work focused on uncertainty quantifications in regression models, including some recent applications to iterative algorithms and bagging. Before joining Rutgers in 2016, he completed his PhD at ENSAE in Paris, France where he was advised by Alexandre Tsybakov.\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003E\u003Cstrong\u003EAbstract:\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003EThis paper investigates the iterates obtained from iterative algorithms in high-dimensional linear regression problems, in the regime where the feature dimension is comparable with the sample size. \u0026nbsp;The analysis and proposed estimators are applicable to Gradient Descent (GD), proximal GD and their accelerated variants such as Fast Iterative Soft-Thresholding (FISTA). \u0026nbsp;The paper proposes novel estimators for the generalization error of the iterate for any fixed iteration along the trajectory. These estimators are proved to be root-n consistent under Gaussian designs. \u0026nbsp;Applications to early-stopping are provided: when the generalization error of the iterates is a U-shape function of the iterations, the estimates allow to select from the data an iteration that achieves the smallest generalization error along the trajectory. \u0026nbsp;Additionally, we provide a technique for developing debiasing corrections and valid confidence intervals for the components of the true coefficient vector from the iterate at any finite iteration.\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Uncertainty quantification for iterative algorithms   "}],"uid":"36433","created_gmt":"2024-09-17 17:18:28","changed_gmt":"2024-10-08 12:12:17","author":"mrussell89","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2024-09-24T11:00:00-04:00","event_time_end":"2024-09-24T12:00:00-04:00","event_time_end_last":"2024-09-24T12:00:00-04:00","gmt_time_start":"2024-09-24 15:00:00","gmt_time_end":"2024-09-24 16:00:00","gmt_time_end_last":"2024-09-24 16:00:00","rrule":null,"timezone":"America\/New_York"},"location":"Groseclose 402","extras":[],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"177814","name":"Postdoc"},{"id":"78771","name":"Public"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"676871":{"#nid":"676871","#data":{"type":"event","title":"ISyE Seminar - Nick Sahinidis","body":[{"value":"\u003Cp\u003ETitle:\u003C\/p\u003E\u003Cp\u003EConvexification and optimization of problems involving the Euclidean norm.\u003C\/p\u003E\u003Cp\u003EAbstract:\u003C\/p\u003E\u003Cp\u003EThe field of mixed-integer nonlinear optimization has advanced significantly over the past three decades. However, even small instances of many nonconvex optimization problems involving the Euclidean norm are beyond the capabilities of existing algorithms. These problems stem from applications such as molecular energy minimization, object packing, and facility location, and often share specific features that make them challenging. In this presentation, we identify these features, introduce new algorithms to address them, and present numerical results that demonstrate the impact of our techniques. Furthermore, we identify several related open problems and opportunities for analytical and computational advances. This is joint work with Anatoliy Kuznetsov.\u003C\/p\u003E\u003Cp\u003EBio:\u003C\/p\u003E\u003Cp\u003ENick Sahinidis is Butler Family Chair and Professor of Industrial \u0026amp; Systems Engineering and Chemical \u0026amp; Biomolecular Engineering at the Georgia Institute of Technology. Dr. Sahinidis previously taught at the University of Illinois at Urbana-Champaign (1991-2007) and Carnegie Mellon University (2007-2020). He has pioneered algorithms and developed widely used software for optimization and machine learning. He received the INFORMS Computing Society Prize in 2004, the Beale-Orchard-Hays Prize from the Mathematical Programming Society in 2006, the Computing in Chemical Engineering Award in 2010, the Constantin Carath\u00e9odory Prize in 2015, and the National Award and Gold Medal from the Hellenic Operational Research Society in 2016. He is a member of the US National Academy of Engineering, a fellow of INFORMS, a fellow of AIChE, a fellow of the Asia-Pacific Artificial Intelligence Association, and past Editor-in-Chief of Optimization and Engineering.\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EThe field of mixed-integer nonlinear optimization has advanced significantly over the past three decades. However, even small instances of many nonconvex optimization problems involving the Euclidean norm are beyond the capabilities of existing algorithms. These problems stem from applications such as molecular energy minimization, object packing, and facility location, and often share specific features that make them challenging. In this presentation, we identify these features, introduce new algorithms to address them, and present numerical results that demonstrate the impact of our techniques. Furthermore, we identify several related open problems and opportunities for analytical and computational advances. This is joint work with Anatoliy Kuznetsov.\u003Cbr\u003E\u0026nbsp;\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Convexification and optimization of problems involving the Euclidean norm."}],"uid":"36374","created_gmt":"2024-09-17 14:56:31","changed_gmt":"2024-10-08 09:14:06","author":"mwelch39","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2024-10-18T11:30:00-04:00","event_time_end":"2024-10-18T12:30:00-04:00","event_time_end_last":"2024-10-18T12:30:00-04:00","gmt_time_start":"2024-10-18 15:30:00","gmt_time_end":"2024-10-18 16:30:00","gmt_time_end_last":"2024-10-18 16:30:00","rrule":null,"timezone":"America\/New_York"},"location":" ISYE Groseclose 402","extras":[],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"177814","name":"Postdoc"},{"id":"78771","name":"Public"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"677242":{"#nid":"677242","#data":{"type":"event","title":"ISyE Seminar - Alper Atamturk","body":[{"value":"\u003Cp\u003ETitle:\u0026nbsp;\u003Cbr\u003EMulti-period mixed-integer quadratic programming\u003C\/p\u003E\u003Cp\u003EAbstract:\u0026nbsp;\u003Cbr\u003EIn this talk, we consider multi-period convex quadratic optimization problems with indicator variables. This problem class has important applications in machine learning and model predictive control. We study a sub-class with a factorable or block-factorable cost matrix and show that it is solvable in polynomial time. We also give a compact convex hull description in an extended space with linear and conic quadratic inequalities. Our computational experiments with data from neuron activation inference and hybrid-electric vehicle power management indicate promises as well as challenges. \u0026nbsp;\u003C\/p\u003E\u003Cp\u003EJoint work with Andres Gomez and Jisun Lee.\u003C\/p\u003E\u003Cp\u003EBio:\u0026nbsp;\u003Cbr\u003EAlper Atamturk is the Earl J. Isaac Chair in the Science and Analysis of Decision Making, Professor and Chair of the Department of Industrial Engineering and Operations at the University of California, Berkeley. He received his Ph.D. from the Georgia Institute of Technology in 1998. His research interests are in optimization, integer programming, optimization under uncertainty with applications to machine learning, energy systems, portfolio and network design. \u0026nbsp;\u0026nbsp;\u003Cbr\u003E\u003Cbr\u003EAlper serves as the UC Berkeley site director of the NSF AI Institute for Advances in Optimization. He serves as co-editor for Mathematical Programming, area editor for Mathematical Programming Computation, and associate editor for Operations Research, Discrete Optimization, and Journal of Risk. He is a Fellow of INFORMS and Vannevar Bush Fellow of the US Department of Defense. He received the Farkas Prize from INFORMS Optimization Society in 2023.\u0026nbsp;\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EIn this talk, we consider multi-period convex quadratic optimization problems with indicator variables. This problem class has important applications in machine learning and model predictive control. We study a sub-class with a factorable or block-factorable cost matrix and show that it is solvable in polynomial time. We also give a compact convex hull description in an extended space with linear and conic quadratic inequalities. Our computational experiments with data from neuron activation inference and hybrid-electric vehicle power management indicate promises as well as challenges. \u0026nbsp;\u003Cbr\u003E\u0026nbsp;\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Multi-period mixed-integer quadratic programming"}],"uid":"36374","created_gmt":"2024-10-02 14:48:52","changed_gmt":"2024-10-08 09:12:55","author":"mwelch39","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2024-10-11T11:30:00-04:00","event_time_end":"2024-10-11T12:30:00-04:00","event_time_end_last":"2024-10-11T12:30:00-04:00","gmt_time_start":"2024-10-11 15:30:00","gmt_time_end":"2024-10-11 16:30:00","gmt_time_end_last":"2024-10-11 16:30:00","rrule":null,"timezone":"America\/New_York"},"location":" ISYE Groseclose 402","extras":[],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"177814","name":"Postdoc"},{"id":"78771","name":"Public"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"677129":{"#nid":"677129","#data":{"type":"event","title":"Info Session: Lean Warehousing course","body":[{"value":"\u003Cp\u003E\u003Cstrong\u003EJoin us for an interactive webinar to learn more about Georgia Tech\u0027s Supply Chain and Logistics (SCL) Lean Warehousing course hosted by our course instructor, Chuck Emery.\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EMonday, October 7, 2024 | 12-1pm 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session"},{"id":"186857","name":"go-gtmi"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"10377","name":"Career\/Professional development"}],"invited_audience":[{"id":"78771","name":"Public"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[{"value":"\u003Cp\u003Ecourse@scl.gatech.edu\u003C\/p\u003E","format":"limited_html"}],"email":[],"slides":[],"orientation":[],"userdata":""}},"676815":{"#nid":"676815","#data":{"type":"event","title":"ISyE Seminar Speaker - Julie L. Swann","body":[{"value":"\u003Cp\u003ETitle:\u003C\/p\u003E\u003Cp\u003ESmarter Decisions for a Better World: Research in Health \u0026amp; INFORMS Innovations\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EAbstract:\u003C\/p\u003E\u003Cp\u003EThis seminar will contain topics in two major areas. In the first, Dr. Swann will discuss research related to health systems, particularly methodologies from OR, AI, ML and more applied to problems of interest to healthcare providers, governmental agencies, and other decision-makers. Examples include agent-based simulations of pandemics with modeling and assessment of interventions to improve equitable outcomes in the population, learning models to enable dynamic optimization of community policies, and interpretable modeling to identify patients at greatest risk for unplanned hospitalizations.\u003C\/p\u003E\u003Cp\u003EIn the second half of the seminar, Dr. Swann will discuss innovations and future planning with the INFORMS society, including topics such as AI, quantum computing, early career practitioners, accreditation and certification for data science programs, research reproducibility for publications, and strategic planning. Discussion and questions will be welcome.\u003C\/p\u003E\u003Cp\u003EBio:\u003C\/p\u003E\u003Cp\u003EJulie L. Swann is the department head and A. Doug Allison Distinguished Professor of the Edward P. Fitts Department of Industrial and Systems Engineering at NC State University. She is an affiliate faculty in the Joint Department of Biomedical Engineering at NC State and the University of North Carolina at Chapel Hill. Previously, Swann was the Nash Professor in the Stewart School of Industrial and Systems at Georgia Tech, a co-founder of the Center of Health and Humanitarian Systems at Georgia Tech, and the Chair of the Council of Industrial Engineering Academic Department Heads (CIEADH). Swann is a Fellow of INFORMS, IISE, and AIMBE and the 2024 President of INFORMS.\u003C\/p\u003E\u003Cp\u003EThroughout her career, Swann has conducted research, outreach and education to improve how health and humanitarian systems and supply chains operate worldwide while advancing scientific innovations. Her research relates to public health, public policy, epidemiology, infectious disease, supply chain management, and disaster response, along with building or employing mathematical and computational models. The work allowed her to serve as a science advisor for the H1N1 pandemic response at the Centers for Disease Control and Prevention. From 2020-2023 she was active in supporting pandemic preparations and decision-making in local, state, and federal health agencies, while also serving as a subject matter expert to the media including outlets such as The Washington Post, The LA Times, The Atlantic, The Wall Street Journal, Forbes, Scientific American, and The Hill) along with radio and television.\u003C\/p\u003E\u003Cp\u003EAlong with the CDC, Swann has collaborated with health and humanitarian organizations such as The American Red Cross; The Carter Center; CARE USA; Children\u2019s Healthcare of Atlanta; Emory University Hospital; State Departments of Public Health; and many others including companies.\u003C\/p\u003E\u003Cp\u003EWorldwide, Dr. Swann has contributed to the education of thousands of practitioners in health and humanitarian systems through the co-creation and teaching in a professional certificate program at Georgia Tech. This contribution includes teaching in the MASHLM program in Lugano, Switzerland, and co-chairing the annual Health and Humanitarian Logistics Conference.\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EAbstract:\u003C\/p\u003E\u003Cp\u003EThis seminar will contain topics in two major areas. In the first, Dr. Swann will discuss research related to health systems, particularly methodologies from OR, AI, ML and more applied to problems of interest to healthcare providers, governmental agencies, and other decision-makers. Examples include agent-based simulations of pandemics with modeling and assessment of interventions to improve equitable outcomes in the population, learning models to enable dynamic optimization of community policies, and interpretable modeling to identify patients at greatest risk for unplanned hospitalizations.\u003C\/p\u003E\u003Cp\u003EIn the second half of the seminar, Dr. Swann will discuss innovations and future planning with the INFORMS society, including topics such as AI, quantum computing, early career practitioners, accreditation and certification for data science programs, research reproducibility for publications, and strategic planning. 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Of particular interest is whether agent strategies converge to well-known solution concepts such as Nash Equilibrium (NE). Most \u201cfixed order\u201d learning dynamics restrict an agent\u2019s underlying state to be its own strategy. In \u201chigher order\u201d learning, agent dynamics can include auxiliary states that can capture phenomena such as path dependencies. We introduce higher-order gradient play dynamics that resemble projected gradient ascent with auxiliary states. The dynamics are \u201cpayoff based\u201d in that each agent\u0027s dynamics depend on its own evolving payoff. While these payoffs depend on the strategies of other agents in a game setting, agent dynamics do not depend explicitly on the nature of the game or the strategies of other agents. In this sense, dynamics are \u201cuncoupled\u201d since an agent\u2019s dynamics do not depend explicitly on the utility functions of other agents. We first show that for any specific game with an isolated completely mixed-strategy NE, there exist higher-order gradient play dynamics that lead (locally) to that NE, both for the specific game and nearby games with perturbed utility functions. Conversely, we show that for any higher-order gradient play dynamics, there exists a game with a unique isolated completely mixed-strategy NE for which the dynamics do not lead to NE. These results build on prior work that showed that uncoupled fixed-order learning cannot lead to NE in certain instances, whereas higher-order variants can. Finally, we consider the mixed-strategy equilibrium associated with coordination games. While higher-order gradient play can converge to such equilibria, we show such dynamics must be inherently irrational.\u003C\/p\u003E\u003Cp\u003EBio:\u003C\/p\u003E\u003Cp\u003EJeff S. Shamma is with the University of Illinois at Urbana-Champaign where he is the Department Head of Industrial and Enterprise Systems Engineering (ISE) and Jerry S. Dobrovolny Chair. His prior academic appointments include faculty positions at the King Abdullah University of Science and Technology (KAUST) and the Georgia Institute of Technology, where he was the Julian T. Hightower Chair in Systems and Controls. Jeff received a PhD in Systems Science and Engineering from MIT in 1988. He is a Fellow of IEEE and IFAC; a recipient of the IFAC High Impact Paper Award, AACC Donald P. Eckman Award, and NSF Young Investigator Award; and a past Distinguished Lecturer of the IEEE Control Systems Society. He has been a plenary or semi-plenary speaker at several conferences, including NeurIPS, World Congress of the Game Theory Society, IEEE Conference on Decision and Control, and the American Control Conference. 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While these payoffs depend on the strategies of other agents in a game setting, agent dynamics do not depend explicitly on the nature of the game or the strategies of other agents. In this sense, dynamics are \u201cuncoupled\u201d since an agent\u2019s dynamics do not depend explicitly on the utility functions of other agents. We first show that for any specific game with an isolated completely mixed-strategy NE, there exist higher-order gradient play dynamics that lead (locally) to that NE, both for the specific game and nearby games with perturbed utility functions. Conversely, we show that for any higher-order gradient play dynamics, there exists a game with a unique isolated completely mixed-strategy NE for which the dynamics do not lead to NE. These results build on prior work that showed that uncoupled fixed-order learning cannot lead to NE in certain instances, whereas higher-order variants can. Finally, we consider the mixed-strategy equilibrium associated with coordination games. 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(ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"177814","name":"Postdoc"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"676426":{"#nid":"676426","#data":{"type":"event","title":"Info Session: World Class Sales and Operations Planning course","body":[{"value":"\u003Cp\u003E\u003Cstrong\u003EJoin us for an interactive webinar to learn more about Georgia Tech\u0027s Supply Chain and Logistics (SCL) World Class Sales and Operations Planning course.\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EFriday, September 13, 2024 | 12-1pm 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ET\u003C\/strong\u003E\u003C\/p\u003E\u003Cul\u003E\u003Cli\u003EExplore the upcoming schedule and suggested prerequisites\u003C\/li\u003E\u003Cli\u003ELearn about the 4 courses which make up the certificate program\u003C\/li\u003E\u003Cli\u003EHear about new content and enhancements\u003C\/li\u003E\u003Cli\u003EHave your questions answered live during the event.\u0026nbsp;\u003C\/li\u003E\u003Cli\u003EAll registrants will receive the presentation slide deck via email after the session.\u003C\/li\u003E\u003C\/ul\u003E\u003Cp\u003E\u003Ca href=\u0022https:\/\/eforms.scl.gatech.edu\/infosessionSCAAug2024\u0022\u003E\u003Cstrong\u003ERegister Online to Attend\u003C\/strong\u003E\u003C\/a\u003E\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EJoin us for an interactive webinar to learn more about Georgia Tech\u0027s Supply Chain and Logistics (SCL) Supply Chain Analytics course offerings.\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Learn about our Supply Chain Analytics Professional (SCA) courses taught through Georgia Tech Professional Education"}],"uid":"27233","created_gmt":"2024-08-20 16:22:00","changed_gmt":"2024-08-29 13:05:48","author":"Andy Haleblian","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2024-08-29T12:00:00-04:00","event_time_end":"2024-08-29T13:00:00-04:00","event_time_end_last":"2024-08-29T13:00:00-04:00","gmt_time_start":"2024-08-29 16:00:00","gmt_time_end":"2024-08-29 17:00:00","gmt_time_end_last":"2024-08-29 17:00:00","rrule":null,"timezone":"America\/New_York"},"location":"Online via Zoom","extras":[],"hg_media":{"674657":{"id":"674657","type":"image","title":"August 29, Supply Chain Analytics Info Session","body":null,"created":"1724170492","gmt_created":"2024-08-20 16:14:52","changed":"1724170492","gmt_changed":"2024-08-20 16:14:52","alt":"August 29, Supply Chain Analytics Info Session","file":{"fid":"258208","name":"829 Info Session.png","image_path":"\/sites\/default\/files\/2024\/08\/20\/829%20Info%20Session.png","image_full_path":"http:\/\/hg.gatech.edu\/\/sites\/default\/files\/2024\/08\/20\/829%20Info%20Session.png","mime":"image\/png","size":134265,"path_740":"http:\/\/hg.gatech.edu\/sites\/default\/files\/styles\/740xx_scale\/public\/2024\/08\/20\/829%20Info%20Session.png?itok=qZrcQZEx"}}},"media_ids":["674657"],"related_links":[{"url":"https:\/\/www.scl.gatech.edu\/sca","title":"SCL Supply Chain Analytics Professional (SCA) Series"},{"url":"https:\/\/www.scl.gatech.edu\/sites\/default\/files\/downloads\/gtscl-sca_brochure.pdf","title":"SCL Supply Chain Analytics Professional (SCA) Series Flyer"}],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"},{"id":"1243","name":"The Supply Chain and Logistics Institute (SCL)"}],"categories":[],"keywords":[{"id":"3615","name":"information session"},{"id":"186857","name":"go-gtmi"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"10377","name":"Career\/Professional development"}],"invited_audience":[{"id":"78771","name":"Public"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[{"value":"\u003Cp\u003Ecourse@scl.gatech.edu\u003C\/p\u003E","format":"limited_html"}],"email":[],"slides":[],"orientation":[],"userdata":""}},"676054":{"#nid":"676054","#data":{"type":"event","title":"Info Session: SCL Professional Education Courses and Certificates","body":[{"value":"\u003Cp\u003E\u003Cstrong\u003EJoin us for an interactive webinar to learn more about Georgia Tech\u0027s Supply Chain and Logistics (SCL) courses and professional certificates.\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EThursday, September 3, 2024 | 12-1pm ET\u003C\/strong\u003E\u003C\/p\u003E\u003Cul\u003E\u003Cli\u003EExplore upcoming SCL schedules and requirements and have your questions answered live during the event.\u003C\/li\u003E\u003Cli\u003EAll registrants will receive the presentation slide deck via email after the session.\u003C\/li\u003E\u003C\/ul\u003E\u003Cp\u003E\u003Ca href=\u0022https:\/\/eforms.scl.gatech.edu\/infosessionSept2024\u0022\u003E\u003Cstrong\u003ERegister Online to Attend\u003C\/strong\u003E\u003C\/a\u003E\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EJoin us for an interactive webinar to learn more about Georgia Tech\u0027s Supply Chain and Logistics (SCL) courses and professional certificates.\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Learn More About Courses and Certificates offered by SCL through Georgia Tech Professional Education"}],"uid":"27233","created_gmt":"2024-08-17 22:58:02","changed_gmt":"2024-08-27 22:36:58","author":"Andy Haleblian","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2024-09-03T12:00:00-04:00","event_time_end":"2024-09-03T13:00:00-04:00","event_time_end_last":"2024-09-03T13:00:00-04:00","gmt_time_start":"2024-09-03 16:00:00","gmt_time_end":"2024-09-03 17:00:00","gmt_time_end_last":"2024-09-03 17:00:00","rrule":null,"timezone":"America\/New_York"},"location":"Online via Zoom","extras":[],"hg_media":{"674615":{"id":"674615","type":"image","title":"September 3, 2024 Information Session","body":null,"created":"1723936441","gmt_created":"2024-08-17 23:14:01","changed":"1723936795","gmt_changed":"2024-08-17 23:19:55","alt":"September 3, 2024 Information Session","file":{"fid":"258165","name":"20240903_InfoSession.png","image_path":"\/sites\/default\/files\/2024\/08\/17\/20240903_InfoSession.png","image_full_path":"http:\/\/hg.gatech.edu\/\/sites\/default\/files\/2024\/08\/17\/20240903_InfoSession.png","mime":"image\/png","size":138428,"path_740":"http:\/\/hg.gatech.edu\/sites\/default\/files\/styles\/740xx_scale\/public\/2024\/08\/17\/20240903_InfoSession.png?itok=ecggJj3m"}}},"media_ids":["674615"],"related_links":[{"url":"https:\/\/www.scl.gatech.edu\/education\/professional-education","title":"SCL Professional Education Offerings"}],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"},{"id":"1243","name":"The Supply Chain and Logistics Institute (SCL)"}],"categories":[],"keywords":[{"id":"186857","name":"go-gtmi"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"10377","name":"Career\/Professional development"}],"invited_audience":[{"id":"78771","name":"Public"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[{"value":"\u003Cp\u003Ecourse@scl.gatech.edu\u003C\/p\u003E","format":"limited_html"}],"email":[],"slides":[],"orientation":[],"userdata":""}},"670541":{"#nid":"670541","#data":{"type":"event","title":"SCL Course: Supply Chain Optimization and Prescriptive Analytics (Virtual\/Instructor-led)","body":[{"value":"\u003Ch3\u003E\u003Cstrong\u003ECourse Description\u003C\/strong\u003E\u003C\/h3\u003E\u003Cp\u003EThis course is the fourth in the 4-course Supply Chain Analytics Professional certificate program. It incorporates learning advanced analytics and mathematical optimization to find solutions for supply chain problems. You\u2019ll learn how to use linear programming, mixed integer programming, and heuristics to conduct prescriptive analytics related to production processes, distribution networks, and routing. The course serves as a capstone for the program by culminating in a hackathon where you\u2019ll design networks, inventory policies, and scenarios and then evaluate the outcomes via simulations.\u003C\/p\u003E\u003Cp\u003EThe online version of the course is comprised of (4) half-day online instructor-led LIVE group webinars and pre-work (e.g. installing and testing software on your computer, testing connectivity with LMS and meeting software, etc.) to be completed before the first day of the course.\u003C\/p\u003E\u003Ch3\u003E\u003Cstrong\u003EWho Should Attend\u003C\/strong\u003E\u003C\/h3\u003E\u003Cp\u003EExperienced business professionals who perform or want to perform analytics to improve their supply chain management processes. They want to tackle strategic goals and to perform leading edge analytics projects that address the full complexity of supply chains.\u003C\/p\u003E\u003Ch3\u003E\u003Cstrong\u003EHow You Will Benefit\u003C\/strong\u003E\u003C\/h3\u003E\u003Cul\u003E\u003Cli\u003EUse mathematical optimization to transform Supply Chain Management (SCM) processes.\u003C\/li\u003E\u003Cli\u003EApply LP, MIP, and heuristics to SCM, particularly in production planning, routing, and network design.\u003C\/li\u003E\u003Cli\u003EUtilize PowerBI and Python in optimization projects.\u003C\/li\u003E\u003Cli\u003EParticipate in a hackathon that pulls together everything learned throughout the certificate program.\u003C\/li\u003E\u003C\/ul\u003E\u003Ch3\u003E\u003Cstrong\u003EWhat Is Covered\u003C\/strong\u003E\u003C\/h3\u003E\u003Cul\u003E\u003Cli\u003ERole of mathematical optimization in addressing complex SCM challenges \u0026nbsp;\u003C\/li\u003E\u003Cli\u003EAppropriate application of linear programming (LP), mixed integer programming (MIP), and heuristics\u003C\/li\u003E\u003Cli\u003EEvaluation of production processes, distribution networks, and routes using optimization\u003C\/li\u003E\u003Cli\u003EAbility to pull together all content of the certificate program into a prescriptive analytics project\u003C\/li\u003E\u003Cli\u003EHands-on practice with these skills using data from the (fictional) Cardboard Company (CBC)\u003C\/li\u003E\u003C\/ul\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003ELearn advanced analytics and mathematical optimization to find solutions for supply chain problems.\u0026nbsp;The course also serves as a capstone for the Supply Chain Analytics Professional certificate program\u0026nbsp;by culminating in a hackathon where you\u2019ll design networks, inventory policies, and scenarios and then evaluate the outcomes via simulations.\u003C\/p\u003E\r\n","format":"limited_html"}],"field_summary_sentence":[{"value":"Learn advanced analytics and mathematical optimization to find solutions for supply chain problems."}],"uid":"27233","created_gmt":"2023-10-20 14:52:27","changed_gmt":"2024-08-27 18:41:37","author":"Andy Haleblian","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2024-12-09T13:00:00-05:00","event_time_end":"2024-12-12T17:00:00-05:00","event_time_end_last":"2024-12-12T17:00:00-05:00","gmt_time_start":"2024-12-09 18:00:00","gmt_time_end":"2024-12-12 22:00:00","gmt_time_end_last":"2024-12-12 22:00:00","rrule":null,"timezone":"America\/New_York"},"location":"Online\/Virtually-led","extras":[],"related_links":[{"url":"https:\/\/www.scl.gatech.edu\/education\/professional-education\/course\/scaoc","title":"Course webpage within the SCL website"}],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"},{"id":"1243","name":"The Supply Chain and Logistics Institute (SCL)"}],"categories":[],"keywords":[{"id":"7251","name":"analytics"},{"id":"167074","name":"Supply Chain"},{"id":"122741","name":"physical internet"},{"id":"186857","name":"go-gtmi"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"10377","name":"Career\/Professional development"}],"invited_audience":[{"id":"78771","name":"Public"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[{"value":"\u003Cp\u003E\u003Ca href=\u0022mailto:info@scl.gatech.edu\u0022\u003Einfo@scl.gatech.edu\u003C\/a\u003E\u003C\/p\u003E","format":"limited_html"}],"email":[],"slides":[],"orientation":[],"userdata":""}},"675371":{"#nid":"675371","#data":{"type":"event","title":"SCL Course: Principles of Transportation Management (Virtual\/Instructor-led)","body":[{"value":"\u003Ch4\u003E\u003Cstrong\u003ECourse Description\u003C\/strong\u003E\u003C\/h4\u003E\u003Cp\u003EThis course prepares students in the basics of transportation operations and analysis. The course includes review of the key elements of transportation such as: modes of transportation, transportation procurement, cost minimization techniques, the role of ports in global logistics, and international trade terms. \u0026nbsp;The course also will discuss emerging trends in North American transportation markets, emerging techniques, and greenhouse gas emissions reduction.\u003C\/p\u003E\u003Ch4\u003E\u003Cstrong\u003EWho Should Attend\u003C\/strong\u003E\u003C\/h4\u003E\u003Cp\u003EThis course is designed for Supply Chain Managers, Distribution Managers, Transportation Planners, Transportation Clerks, Transportation Analysts, and Transportation Managers and learners seeking to enter these roles. \u0026nbsp;Supply chain professionals from other domains will also benefit through gaining insights into transportation operations.\u003C\/p\u003E\u003Ch4\u003E\u003Cstrong\u003EHow You Will Benefit\u003C\/strong\u003E\u003C\/h4\u003E\u003Cp\u003E\u003Cstrong\u003EUpon completion of this course, you will be able to:\u003C\/strong\u003E\u003C\/p\u003E\u003Cul\u003E\u003Cli\u003EUnderstand the characteristics and best use of specific transportation modes\u003C\/li\u003E\u003Cli\u003EApply transportation cost analysis techniques\u003C\/li\u003E\u003Cli\u003EUnderstand the multimodal role of ports in global trade\u003C\/li\u003E\u003Cli\u003EIdentify and apply best practices in transportation procurement\u003C\/li\u003E\u003Cli\u003EUnderstand how to minimize transportation costs through consolidation techniques\u003C\/li\u003E\u003Cli\u003EUnderstand the role of Incoterms in global trade\u003C\/li\u003E\u003Cli\u003EUnderstand emerging techniques in logistics including techniques to reduce greenhouse gas emissions in logistics\u003C\/li\u003E\u003C\/ul\u003E\u003Ch4\u003E\u003Cstrong\u003EWhat is Covered\u003C\/strong\u003E\u003C\/h4\u003E\u003Cul\u003E\u003Cli\u003EComparison of characteristics of alternative transportation modes\u003C\/li\u003E\u003Cli\u003EComponents of Port Logistics systems\u003C\/li\u003E\u003Cli\u003EBest practices in transportation procurement\u003C\/li\u003E\u003Cli\u003EApplication of practical transportation cost analysis techniques\u003C\/li\u003E\u003Cli\u003EINCOTERMS purpose, types, and use\u003C\/li\u003E\u003Cli\u003EGreenhouse gas emission generation in logistics and mitigation strategies\u003C\/li\u003E\u003Cli\u003ENew business models in logistics enabled by emerging technologies\u003C\/li\u003E\u003C\/ul\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EThis course prepares students in the basics of transportation operations and analysis. \u0026nbsp;The course includes review of the key elements of transportation such as: modes of transportation, transportation procurement, cost minimization techniques, the role of ports in global logistics, and international trade terms. \u0026nbsp;The course also will discuss emerging trends in North American transportation markets, emerging techniques, and greenhouse gas emissions reduction.\u003C\/p\u003E\r\n","format":"limited_html"}],"field_summary_sentence":[{"value":"This course prepares students in the basics of transportation operations and analysis."}],"uid":"27233","created_gmt":"2024-07-07 22:51:50","changed_gmt":"2024-08-27 18:41:12","author":"Andy Haleblian","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2024-10-23T08:00:00-04:00","event_time_end":"2024-10-25T17:00:00-04:00","event_time_end_last":"2024-10-25T17:00:00-04:00","gmt_time_start":"2024-10-23 12:00:00","gmt_time_end":"2024-10-25 21:00:00","gmt_time_end_last":"2024-10-25 21:00:00","rrule":null,"timezone":"America\/New_York"},"location":"Online\/Virtually-led","extras":[],"related_links":[{"url":"https:\/\/www.scl.gatech.edu\/ptm","title":"Course webpage within the SCL website"}],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"},{"id":"1243","name":"The Supply Chain and Logistics Institute (SCL)"}],"categories":[],"keywords":[{"id":"3843","name":"distribution"},{"id":"168044","name":"sourcing"},{"id":"168","name":"Transportation"},{"id":"167074","name":"Supply Chain"},{"id":"122741","name":"physical internet"},{"id":"186857","name":"go-gtmi"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"10377","name":"Career\/Professional development"}],"invited_audience":[{"id":"78771","name":"Public"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[{"value":"\u003Cp\u003E\u003Ca href=\u0022mailto:info@scl.gatech.edu\u0022\u003Einfo@scl.gatech.edu\u003C\/a\u003E\u003C\/p\u003E","format":"limited_html"}],"email":[],"slides":[],"orientation":[],"userdata":""}},"672807":{"#nid":"672807","#data":{"type":"event","title":"SCL September 2024 Supply Chain and Logistics Career Fair","body":[{"value":"\u003Cp\u003EGeorgia Tech Supply Chain students and employers, please join us for our fall Supply Chain Day!\u0026nbsp;\u003C\/p\u003E\u003Ch3\u003E\u003Cstrong\u003EEvent Details\u003C\/strong\u003E\u003C\/h3\u003E\u003Ch4\u003EOn Campus\/In-Person (Georgia Tech Exhibition Hall)\u003C\/h4\u003E\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EThursday, September 12, 2024 | 10am - 2pm ET\u003C\/strong\u003E\u003C\/li\u003E\u003C\/ul\u003E\u003Ch3\u003EStudents\u003C\/h3\u003E\u003Cp\u003E\u003Cstrong\u003EWe strongly encourage you to attend to seek full-time employment\u003C\/strong\u003E, \u003Cstrong\u003Einternships, and projects\u003C\/strong\u003E (rather than waiting until the end of the semester).\u003C\/p\u003E\u003Ch3\u003EOrganizations\u003C\/h3\u003E\u003Cp\u003EIf you are interested in hosting a table for the upcoming session, please let us know after reviewing the below information within our website. 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We will be hosting an On Campus session\u0026nbsp;Thursday, September 12, 2024 from 10am-2pm ET at the Georgia Tech Exhibition Hall.\u003C\/p\u003E\r\n","format":"limited_html"}],"field_summary_sentence":[{"value":"Supply chain and logistics career fair where industry supply chain representatives meet Georgia Tech students."}],"uid":"27233","created_gmt":"2024-02-07 16:00:51","changed_gmt":"2024-08-27 18:40:26","author":"Andy Haleblian","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2024-09-12T10:00:00-04:00","event_time_end":"2024-09-12T14:00:00-04:00","event_time_end_last":"2024-09-12T14:00:00-04:00","gmt_time_start":"2024-09-12 14:00:00","gmt_time_end":"2024-09-12 18:00:00","gmt_time_end_last":"2024-09-12 18:00:00","rrule":null,"timezone":"America\/New_York"},"location":"Georgia Tech Exhibition Hall","extras":[],"hg_media":{"672998":{"id":"672998","type":"image","title":"Thursday, September 12, 2024 Supply Chain Day Banner","body":null,"created":"1707322510","gmt_created":"2024-02-07 16:15:10","changed":"1707322510","gmt_changed":"2024-02-07 16:15:10","alt":"Thursday, September 12, 2024 Supply Chain Day Banner","file":{"fid":"256338","name":"20240912_SCDay_HgBanner.jpg","image_path":"\/sites\/default\/files\/2024\/02\/07\/20240912_SCDay_HgBanner.jpg","image_full_path":"http:\/\/hg.gatech.edu\/\/sites\/default\/files\/2024\/02\/07\/20240912_SCDay_HgBanner.jpg","mime":"image\/jpeg","size":235226,"path_740":"http:\/\/hg.gatech.edu\/sites\/default\/files\/styles\/740xx_scale\/public\/2024\/02\/07\/20240912_SCDay_HgBanner.jpg?itok=2qEsSKJI"}}},"media_ids":["672998"],"related_links":[{"url":"https:\/\/www.scl.gatech.edu\/outreach\/supplychainday","title":"Register online to attend (for Georgia Tech students)"},{"url":"https:\/\/www.scl.gatech.edu","title":"Supply Chain and Logistics Institute website"}],"groups":[{"id":"1250","name":"Center for Health and Humanitarian Systems (CHHS)"},{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"},{"id":"1243","name":"The Supply Chain and Logistics Institute (SCL)"}],"categories":[],"keywords":[{"id":"780","name":"employment"},{"id":"9845","name":"GTSCL"},{"id":"233","name":"Logistics"},{"id":"167074","name":"Supply Chain"},{"id":"1996","name":"Recruiting"},{"id":"5172","name":"career day"},{"id":"122741","name":"physical internet"},{"id":"186857","name":"go-gtmi"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"10377","name":"Career\/Professional development"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"177814","name":"Postdoc"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[{"value":"\u003Cp\u003Eevent@scl.gatech.edu\u003C\/p\u003E","format":"limited_html"}],"email":[],"slides":[],"orientation":[],"userdata":""}},"676053":{"#nid":"676053","#data":{"type":"event","title":"ISyE Seminar Speaker - Bobby Kleinberg","body":[{"value":"\u003Cp\u003E\u003Cstrong\u003ETITLE:\u003C\/strong\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003ETrustworthy Forecasting Algorithms\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EABSTRACT:\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003E\u003Cbr\u003EAlgorithms are increasingly tasked with forecasting the probabilities of uncertain events: a creditor repaying a loan, a user clicking an advertisement, or a word appearing next in a stream of text, for example. Such forecasts are trustworthy if their users can be sure they won\u0027t regret treating the predicted probabilities as if they were the actual distributions from which outcomes were sampled. The term \u0022calibration\u0022 refers to various measures of forecast accuracy that attempt to formalize this property of trustworthiness. Defining calibration, and designing algorithms to achieve it, turns out to be a tightrope walk between strong definitions, which ensure reliable results for downstream users but are computationally and statistically harder to achieve, and weak definitions, which have the opposite benefits and drawbacks. I will report on some recent research that locating a sweet spot between these two extremes, requiring no more samples or computation than the weakest definitions but providing guarantees that are, in many cases, as useful for downstream users as the strongest ones.\u003C\/p\u003E\u003Cp\u003EThis talk is based on joint work with Michael Kim, Princewill Okoroafor, Renato Paes Leme, Jon Schneider, and Yifeng Teng.\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EBIO:\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003E\u003Cbr\u003EBobby Kleinberg is a Professor of Computer Science at Cornell University and a part-time Faculty Researcher at Google. His research concerns algorithms and their applications to machine learning, economics, networking, and other areas. Prior to receiving his doctorate from MIT in 2005, Kleinberg spent three years at Akamai Technologies; he and his co-workers received the 2018 SIGCOMM Networking Systems Award for pioneering the first Internet content delivery network. He is a Fellow of the ACM and a recipient of the ACM SIGecom Mid-Career Award for advancing the understanding of on-line learning and decision problems and their application to mechanism design.\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003E\u003Cstrong\u003EABSTRACT:\u003C\/strong\u003E\u003Cbr\u003EAlgorithms are increasingly tasked with forecasting the probabilities of uncertain events: a creditor repaying a loan, a user clicking an advertisement, or a word appearing next in a stream of text, for example. Such forecasts are trustworthy if their users can be sure they won\u0027t regret treating the predicted probabilities as if they were the actual distributions from which outcomes were sampled. The term \u0022calibration\u0022 refers to various measures of forecast accuracy that attempt to formalize this property of trustworthiness. Defining calibration, and designing algorithms to achieve it, turns out to be a tightrope walk between strong definitions, which ensure reliable results for downstream users but are computationally and statistically harder to achieve, and weak definitions, which have the opposite benefits and drawbacks. I will report on some recent research that locating a sweet spot between these two extremes, requiring no more samples or computation than the weakest definitions but providing guarantees that are, in many cases, as useful for downstream users as the strongest ones.\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Trustworthy Forecasting Algorithms"}],"uid":"36374","created_gmt":"2024-08-17 20:55:32","changed_gmt":"2024-08-17 20:58:45","author":"mwelch39","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2024-08-30T11:30:00-04:00","event_time_end":"2024-08-30T12:30:00-04:00","event_time_end_last":"2024-08-30T12:30:00-04:00","gmt_time_start":"2024-08-30 15:30:00","gmt_time_end":"2024-08-30 16:30:00","gmt_time_end_last":"2024-08-30 16:30:00","rrule":null,"timezone":"America\/New_York"},"location":" ISyE Groseclose 402","extras":[],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"174045","name":"Graduate students"},{"id":"177814","name":"Postdoc"},{"id":"78751","name":"Undergraduate students"},{"id":"78771","name":"Public"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"676046":{"#nid":"676046","#data":{"type":"event","title":"ISyE Picture Day 2024","body":[{"value":"\u003Cp\u003EIt\u0027s that time again -- annual picture day for ISyE staff, faculty and PhD students! Photos will be taking place over the course of 3 days in the Cecil G. Johnson ISyE Studio, located on the first floor in ISyE Main, Room 103. All faculty, and staff members are highly encouraged to take new headshots; \u003Cstrong\u003Eall Ph.D. students are required.\u0026nbsp;\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003E\u003Cem\u003E*If availability doesn\u2019t match, participants can select next best day for their schedule*\u003C\/em\u003E\u003C\/p\u003E\u003Cul\u003E\u003Cli\u003ETime: 10:00AM - 3:00PM\u003C\/li\u003E\u003Cli\u003ELocation:\u0026nbsp;Cecil G. Johnson ISyE Studio, 1st\u0026nbsp;Floor (ISyE Main)\u003C\/li\u003E\u003Cli\u003EDates:\u0026nbsp;\u003Cul\u003E\u003Cli\u003ETuesday, September 17: Staff\u003C\/li\u003E\u003Cli\u003EWednesday, September 18: Faculty\u003C\/li\u003E\u003Cli\u003EThursday, September 19: Ph.D. Students*\u003C\/li\u003E\u003C\/ul\u003E\u003C\/li\u003E\u003C\/ul\u003E\u003Cp\u003E\u003Cstrong\u003ERecommendations for attire:\u0026nbsp;\u003C\/strong\u003E\u003C\/p\u003E\u003Cul\u003E\u003Cli\u003EDo NOT wear red\u003C\/li\u003E\u003Cli\u003EWear blue, or dark-colored clothing (will be on a white backdrop)\u003C\/li\u003E\u003Cli\u003EAvoid wearing large jewelry, baggy clothes, and loud patterns\u003C\/li\u003E\u003C\/ul\u003E\u003Cp\u003EFor more information, please contact Amanda Ford (af73@gatech.edu) and Camille Carpenter (chenriquez8@gatech.edu).\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EDon\u0027t miss getting your updated headshot for the current academic year!\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Don\u0027t miss getting your updated headshot for the current academic year!"}],"uid":"36284","created_gmt":"2024-08-16 18:44:22","changed_gmt":"2024-08-16 19:17:31","author":"chenriquez8","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2024-09-17T10:00:00-04:00","event_time_end":"2024-09-19T15:00:00-04:00","event_time_end_last":"2024-09-19T15:00:00-04:00","gmt_time_start":"2024-09-17 14:00:00","gmt_time_end":"2024-09-19 19:00:00","gmt_time_end_last":"2024-09-19 19:00:00","rrule":null,"timezone":"America\/New_York"},"location":"Cecil G. Johnson Studio, 1st Floor ISyE Main","extras":[],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"174045","name":"Graduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"675916":{"#nid":"675916","#data":{"type":"event","title":"ISYE Statistic Seminar - Alon Kipnis","body":[{"value":"\u003Cp\u003EAbstract\u003Cbr\u003ESuppose we have two tables of counts, each indexed by the same set of categories, and we wish to determine whether the underlying generating mechanism behind each table might be different. Furthermore, if the mechanism is different, we suspect it likely varies only in a small fraction of the observed categories. This question arises in several applications, including attributing authorship based on word frequencies.\u003C\/p\u003E\u003Cp\u003EWe propose a tool for this problem built on P-values derived from a Binomial Allocation model and the notion of Higher Criticism (Donoho \u0026amp; Jin 2004). Our proposal offers an interpretable and easy-to-apply tool, and our theoretical analysis shows that it is powerful against the aforementioned changes in the generating mechanism. Specifically, under a calibration of the number of categories to rarity and signal intensity parameters, the power of our test experiences a phase transition that matches the phase transition of the likelihood ratio test.\u003C\/p\u003E\u003Cp\u003EOur analytic framework goes beyond contingency tables, encompassing a wide range of rare and weak signal models experiencing departures on a moderate scale. We discuss several interesting new models falling under this category, including the detection of a few edits within text written by a generative language model.\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EAbstract\u003Cbr\u003ESuppose we have two tables of counts, each indexed by the same set of categories, and we wish to determine whether the underlying generating mechanism behind each table might be different. Furthermore, if the mechanism is different, we suspect it likely varies only in a small fraction of the observed categories. This question arises in several applications, including attributing authorship based on word frequencies.\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Rare and Weak Signal Detection and Authorship Challenges: From the Federalist Papers to ChatGPT\u0022"}],"uid":"36433","created_gmt":"2024-08-12 18:18:11","changed_gmt":"2024-08-13 13:00:44","author":"mrussell89","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2024-08-15T13:30:00-04:00","event_time_end":"2024-08-15T14:30:00-04:00","event_time_end_last":"2024-08-15T14:30:00-04:00","gmt_time_start":"2024-08-15 17:30:00","gmt_time_end":"2024-08-15 18:30:00","gmt_time_end_last":"2024-08-15 18:30:00","rrule":null,"timezone":"America\/New_York"},"location":"ISYE Main 228","extras":["free_food"],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"174045","name":"Graduate students"},{"id":"177814","name":"Postdoc"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"675914":{"#nid":"675914","#data":{"type":"event","title":"ISYE Statistic Seminar - Lan Gao","body":[{"value":"\u003Cp\u003E\u003Cstrong\u003EAbstract:\u003C\/strong\u003E We investigate the robustness of the model-X knockoffs framework with respect to the misspecified or estimated feature distribution. We achieve such a goal by theoretically studying the feature selection performance of a practically implemented knockoffs algorithm, which we name as the approximate knockoffs (ARK) procedure, under the measures of the false discovery rate (FDR) and k-familywise error rate (k-FWER). The approximate knockoffs procedure differs from the model-X knockoffs procedure only in that the former uses the misspecified or estimated feature distribution. A key technique in our theoretical analyses is to couple the approximate knockoffs procedure with the model-X knockoffs procedure so that random variables in these two procedures can be close in realizations. We prove that if such coupled model-X knockoffs procedure exists, the approximate knockoffs procedure can achieve the asymptotic FDR or k-FWER control at the target level. We showcase three specific constructions of such coupled model-X knockoff variables, verifying their existence and justifying the robustness of the model-X knockoffs framework. Additionally, we formally connect our concept of knockoff variable coupling to a type of Wasserstein distance.\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EBio:\u0026nbsp;\u003C\/strong\u003ELan Gao is an Assistant Professor in Department of Business Analytics and Statistics at University of Tennessee Knoxville (UTK). She obtained her bachelor\u2019s degree in Statistics at Wuhan University in 2015 and PhD degree in statistics from the Chinese University of Hong Kong in 2019. She worked as a postdoctoral scholar at University of Southern California before joining UTK. Her research interests lie in the areas of high-dimensional statistics and inference, nonparametric statistics, asymptotic theory, and machine learning.\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003E\u003Cstrong\u003EAbstract:\u003C\/strong\u003E We investigate the robustness of the model-X knockoffs framework with respect to the misspecified or estimated feature distribution. We achieve such a goal by theoretically studying the feature selection performance of a practically implemented knockoffs algorithm, which we name as the approximate knockoffs (ARK) procedure, under the measures of the false discovery rate (FDR) and k-familywise error rate (k-FWER). The approximate knockoffs procedure differs from the model-X knockoffs procedure only in that the former uses the misspecified or estimated feature distribution. A key technique in our theoretical analyses is to couple the approximate knockoffs procedure with the model-X knockoffs procedure so that random variables in these two procedures can be close in realizations. We prove that if such coupled model-X knockoffs procedure exists, the approximate knockoffs procedure can achieve the asymptotic FDR or k-FWER control at the target level. We showcase three specific constructions of such coupled model-X knockoff variables, verifying their existence and justifying the robustness of the model-X knockoffs framework. Additionally, we formally connect our concept of knockoff variable coupling to a type of Wasserstein distance.\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"ARK: Robust Knockoffs Inference via Coupling"}],"uid":"36433","created_gmt":"2024-08-12 18:08:05","changed_gmt":"2024-08-12 18:12:02","author":"mrussell89","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2024-09-10T15:30:00-04:00","event_time_end":"2024-09-10T16:30:00-04:00","event_time_end_last":"2024-09-10T16:30:00-04:00","gmt_time_start":"2024-09-10 19:30:00","gmt_time_end":"2024-09-10 20:30:00","gmt_time_end_last":"2024-09-10 20:30:00","rrule":null,"timezone":"America\/New_York"},"location":"Groseclose 402","extras":[],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"174045","name":"Graduate students"},{"id":"177814","name":"Postdoc"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"675795":{"#nid":"675795","#data":{"type":"event","title":"ISyE Speed Networking\/Interview Skills with the ISyE Advisory Board","body":[{"value":"\u003Cp\u003EJoin ISyE Advisory Board Members to polish your interview skills and discuss potential career paths!\u003C\/p\u003E\u003Cp\u003ESet up almost like a speed-dating event where students meet with Advisory Board members (ISyE alum) to practice interview\/networking skills.\u003C\/p\u003E\u003Cp\u003E\u003Ca href=\u0022https:\/\/eforms.isye.gatech.edu\/form\/isye-ab-speed-networking-event\u0022\u003ERSVP To Event\u003C\/a\u003E\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EISyE Speed Networking\/Interview Skills with the ISyE Advisory Board\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"ISyE Speed Networking\/Interview Skills with the ISyE Advisory Board"}],"uid":"27764","created_gmt":"2024-08-06 14:08:46","changed_gmt":"2024-08-08 14:52:31","author":"Scott Jacobson","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2024-09-12T18:00:00-04:00","event_time_end":"2024-09-12T20:00:00-04:00","event_time_end_last":"2024-09-12T20:00:00-04:00","gmt_time_start":"2024-09-12 22:00:00","gmt_time_end":"2024-09-13 00:00:00","gmt_time_end_last":"2024-09-13 00:00:00","rrule":null,"timezone":"America\/New_York"},"location":"ISyE Atrium","extras":[],"hg_media":{"674503":{"id":"674503","type":"image","title":"Speed Interviewing","body":null,"created":"1722953531","gmt_created":"2024-08-06 14:12:11","changed":"1722953531","gmt_changed":"2024-08-06 14:12:11","alt":"Speed Interviewing","file":{"fid":"258038","name":"image001.png","image_path":"\/sites\/default\/files\/2024\/08\/06\/image001.png","image_full_path":"http:\/\/hg.gatech.edu\/\/sites\/default\/files\/2024\/08\/06\/image001.png","mime":"image\/png","size":1355693,"path_740":"http:\/\/hg.gatech.edu\/sites\/default\/files\/styles\/740xx_scale\/public\/2024\/08\/06\/image001.png?itok=vIPzGhK9"}}},"media_ids":["674503"],"related_links":[{"url":"https:\/\/eforms.isye.gatech.edu\/form\/isye-ab-speed-networking-event","title":"RSVP To Event"}],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[],"invited_audience":[],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[{"value":"\u003Cp\u003E\u003Ca href=\u0022https:\/\/www.isye.gatech.edu\/user\/970\/contact\u0022 target=\u0022_blank\u0022\u003ENicoly Myles\u003C\/a\u003E\u003C\/p\u003E","format":"limited_html"}],"email":[],"slides":[],"orientation":[],"userdata":""}},"673750":{"#nid":"673750","#data":{"type":"event","title":"SCL Course: World Class Sales and Operations Planning (Virtual\/Instructor-led)","body":[{"value":"\u003Ch4\u003ECOURSE DESCRIPTION\u003C\/h4\u003E\u003Cp\u003EThis course focuses on defining, executing, and improving the S\u0026amp;OP process. Participants will be introduced to the appropriate stakeholders of S\u0026amp;OP, the importance of S\u0026amp;OP to corporate performance, S\u0026amp;OP cadence, and the use of visionary technology to bring S\u0026amp;OP to the next level. Business cases will be used to show concrete examples of companies where S\u0026amp;OP is effectively applied.\u003C\/p\u003E\u003Ch4\u003EWHO SHOULD ATTEND\u003C\/h4\u003E\u003Cul\u003E\u003Cli\u003EChief Operating Officers, Supply Chain, Sales, Marketing and Finance Management Executives (Directors, VPs, EVPs)\u003C\/li\u003E\u003Cli\u003ESupply Chain and Logistics Managers, Consultants, Supervisors, Planners, and Engineers\u003C\/li\u003E\u003Cli\u003ESupply Chain Education and Human Resource Management personnel\u003C\/li\u003E\u003Cli\u003EInventory and Demand Planners\u003C\/li\u003E\u003Cli\u003EProcurement and Sourcing Analysts and Managers\u003C\/li\u003E\u003Cli\u003EManufacturing Planners, Analysts, and Managers\u003C\/li\u003E\u003Cli\u003ESales Operations Managers, Analysts, Planners, Supervisors, Directors\u003C\/li\u003E\u003C\/ul\u003E\u003Ch4\u003EHOW YOU WILL BENEFIT\u003C\/h4\u003E\u003Cp\u003E\u003Cstrong\u003EUpon completion of this course, you will be able to:\u003C\/strong\u003E\u003C\/p\u003E\u003Cul\u003E\u003Cli\u003EUnderstand the need for an S\u0026amp;OP cycle in a company\u003C\/li\u003E\u003Cli\u003EApply principles key to success of an S\u0026amp;OP process\u003C\/li\u003E\u003Cli\u003EExperience true market examples relevant to their businesses\u003C\/li\u003E\u003C\/ul\u003E\u003Ch4\u003ELEARNING OBJECTIVES\u003C\/h4\u003E\u003Cul\u003E\u003Cli\u003ELearn how to identify and apply best fit S\u0026amp;OP process and technology enablers to your organization and make it a reality based process.\u003C\/li\u003E\u003Cli\u003EWalk through a complete simulated S\u0026amp;OP cycle supported by a technology enabler.\u003C\/li\u003E\u003Cli\u003EUnderstand the interaction and integration between the financial and operation levels of S\u0026amp;O.\u003C\/li\u003E\u003Cli\u003ELearn the key components of an effective S\u0026amp;OP business case through discussion of real life examples of how companies have benefited from the implementation of best practices in S\u0026amp;OP.\u003C\/li\u003E\u003C\/ul\u003E\u003Ch4\u003EWHAT IS COVERED\u003C\/h4\u003E\u003Cul\u003E\u003Cli\u003EDefining the S\u0026amp;OP process before adopting technology\u003C\/li\u003E\u003Cli\u003EThe advantages of value based and reality based S\u0026amp;OP\u003C\/li\u003E\u003Cli\u003EWhy S\u0026amp;OP needs to be integrated closely with operational planning\u003C\/li\u003E\u003Cli\u003EWhat is the scope of each role in the S\u0026amp;OP Cycle\u003C\/li\u003E\u003Cli\u003EWhat are the most valuable outputs and results of the S\u0026amp;OP Cycle\u003C\/li\u003E\u003Cli\u003EHow can technology enable companies to take performance to the next level\u003C\/li\u003E\u003Cli\u003EExperience a complete simulated technology-enabled S\u0026amp;OP Cycle\u003C\/li\u003E\u003C\/ul\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EThis course focuses on defining, executing, and improving the S\u0026amp;OP process. Participants will be introduced to the appropriate stakeholders of S\u0026amp;OP, the importance of S\u0026amp;OP to corporate performance, S\u0026amp;OP cadence, and the use of visionary technology to bring S\u0026amp;OP to the next level. Business cases will be used to show concrete examples of companies where S\u0026amp;OP is effectively applied.\u003C\/p\u003E\r\n","format":"limited_html"}],"field_summary_sentence":[{"value":"This course focuses on defining, executing, and improving the S\u0026OP process."}],"uid":"27233","created_gmt":"2024-03-26 01:29:05","changed_gmt":"2024-07-05 13:20:29","author":"Andy Haleblian","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2024-09-25T08:00:00-04:00","event_time_end":"2024-09-27T12:00:00-04:00","event_time_end_last":"2024-09-27T12:00:00-04:00","gmt_time_start":"2024-09-25 12:00:00","gmt_time_end":"2024-09-27 16:00:00","gmt_time_end_last":"2024-09-27 16:00:00","rrule":null,"timezone":"America\/New_York"},"location":"Online\/Virtually-led","extras":[],"related_links":[{"url":"https:\/\/www.pe.gatech.edu\/courses\/world-class-sales-and-operations-planning","title":"Course registration page"},{"url":"http:\/\/www.scl.gatech.edu\/wcsop","title":"Course webpage within the SCL website"},{"url":"https:\/\/www.scl.gatech.edu\/sites\/default\/files\/downloads\/gtscl-sdpbrochure.pdf","title":"Supply \u0026 Demand Planning Certificate Course Series Flyer"}],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"},{"id":"1243","name":"The Supply Chain and Logistics Institute (SCL)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"10377","name":"Career\/Professional development"}],"invited_audience":[{"id":"78771","name":"Public"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[{"value":"\u003Cp\u003E\u003Ca href=\u0022mailto:info@scl.gatech.edu\u0022\u003Einfo@scl.gatech.edu\u003C\/a\u003E\u003C\/p\u003E","format":"limited_html"}],"email":[],"slides":[],"orientation":[],"userdata":""}},"674496":{"#nid":"674496","#data":{"type":"event","title":"ISyE Seminar - Vineet Goyal","body":[{"value":"\u003Ch3\u003E\u003Cstrong\u003ETitle:\u003C\/strong\u003E\u003C\/h3\u003E\r\n\r\n\u003Cp\u003EJoint Assortment and Inventory Optimization under the Markov chain choice model\u003C\/p\u003E\r\n\r\n\u003Ch3\u003E\u003Cstrong\u003EAbstract:\u003C\/strong\u003E\u003C\/h3\u003E\r\n\r\n\u003Cp\u003EWe consider a joint assortment and inventory optimization problem faced by a seller who needs to decide on both the assortment and the inventories of a set of N\u0026nbsp;substitutable products before the start of a selling season\u0026nbsp;to maximize the expected profit. This is an important problem faced by most retailers in many product\u0026nbsp;categories where the inventory decisions need to be made in advance of the selling season.\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003EWe model dynamic stock-out based substitution using a Markov Chain choice model where substitutions between products are captured via probabilistic transitions. The Markov\u0026nbsp;chain model is a significant\u0026nbsp;generalization of the multinomial logit model and has been shown to provide a good approximation for a large class of choice models under reasonable\u0026nbsp;assumptions. Furthermore, the static assortment optimization remains tractable under this model. In particular, we can solve the unconstrained problem optimally and give a constant factor approximation for the capacity constrained version.\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003EIn this work, we consider a\u0026nbsp;problem where we need to make the assortment and inventory decisions jointly to maximize expected profit from T customers arriving sequentially with i.i.d. preferences according to the given Markov chain choice model. We present a near-optimal algorithm for the problem that achieves an $O(\\sqrt{NT})$ regret with respect to an LP upper bound. Our algorithm balances between expected revenue and inventory costs by identifying a subset of products that can pool demand without significantly cannibalizing the revenue in the presence of dynamic substitution. We also present computational experiments that show that our algorithm empirically outperforms natural approaches both on synthetic and realistic instances.\u003C\/p\u003E\r\n\r\n\u003Ch3\u003E\u003Cstrong\u003EBio:\u0026nbsp;\u003C\/strong\u003E\u003C\/h3\u003E\r\n\r\n\u003Cp\u003EVineet\u0026nbsp;Goyal\u0026nbsp;is Associate Professor in the Industrial Engineering and Operations Research Department at Columbia University where he joined in 2010. He received his Bachelor\u0027s degree in Computer Science from Indian Institute of Technology, Delhi in 2003 and his Ph.D. in Algorithms, Combinatorics and Optimization (ACO) from Carnegie Mellon University in 2008. Before coming to Columbia, he spent two years as a Postdoctoral Associate at the Operations Research Center at MIT. He is interested in the design of efficient and robust data-driven algorithms for large scale dynamic optimization problems with applications in \u0026nbsp;revenue management and healthcare problems. His research has been continually supported by grants from NSF and industry including NSF CAREER Award in 2014 and faculty research awards from Google, IBM, Adobe and Amazon.\u003C\/p\u003E\r\n","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Ch3\u003E\u003Cstrong\u003EAbstract:\u003C\/strong\u003E\u003C\/h3\u003E\r\n\r\n\u003Cp\u003EWe consider a joint assortment and inventory optimization problem faced by a seller who needs to decide on both the assortment and the inventories of a set of N\u0026nbsp;substitutable products before the start of a selling season\u0026nbsp;to maximize the expected profit. This is an important problem faced by most retailers in many product\u0026nbsp;categories where the inventory decisions need to be made in advance of the selling season.\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003EWe model dynamic stock-out based substitution using a Markov Chain choice model where substitutions between products are captured via probabilistic transitions. The Markov\u0026nbsp;chain model is a significant\u0026nbsp;generalization of the multinomial logit model and has been shown to provide a good approximation for a large class of choice models under reasonable\u0026nbsp;assumptions. Furthermore, the static assortment optimization remains tractable under this model. In particular, we can solve the unconstrained problem optimally and give a constant factor approximation for the capacity constrained version.\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003EIn this work, we consider a\u0026nbsp;problem where we need to make the assortment and inventory decisions jointly to maximize expected profit from T customers arriving sequentially with i.i.d. preferences according to the given Markov chain choice model. We present a near-optimal algorithm for the problem that achieves an $O(\\sqrt{NT})$ regret with respect to an LP upper bound. Our algorithm balances between expected revenue and inventory costs by identifying a subset of products that can pool demand without significantly cannibalizing the revenue in the presence of dynamic substitution. We also present computational experiments that show that our algorithm empirically outperforms natural approaches both on synthetic and realistic instances.\u003C\/p\u003E\r\n","format":"limited_html"}],"field_summary_sentence":[{"value":"Joint Assortment and Inventory Optimization under the Markov chain choice model"}],"uid":"34977","created_gmt":"2024-05-03 11:35:22","changed_gmt":"2024-05-03 21:02:38","author":"Julie Smith","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2024-05-08T11:00:00-04:00","event_time_end":"2024-05-08T12:00:00-04:00","event_time_end_last":"2024-05-08T12:00:00-04:00","gmt_time_start":"2024-05-08 15:00:00","gmt_time_end":"2024-05-08 16:00:00","gmt_time_end_last":"2024-05-08 16:00:00","rrule":null,"timezone":"America\/New_York"},"location":"ISyE Groseclose 402","extras":[],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"174045","name":"Graduate students"},{"id":"177814","name":"Postdoc"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"674297":{"#nid":"674297","#data":{"type":"event","title":"Professional Education Course: Inventory Management and Resource Allocation in Supply Chains","body":[{"value":"\u003Ch3\u003EClasses\u0026nbsp;will be taught by LIVE video instruction similar to the experience you would receive in person with the same interactive components.\u0026nbsp;Each course will run for 1-week Monday through Thursday from 9:30am to 1:00pm EDT each day.\u003C\/h3\u003E\r\n\r\n\u003Ch3\u003E\u003Cstrong\u003ECourse Description\u003C\/strong\u003E\u003C\/h3\u003E\r\n\r\n\u003Cp\u003EMany Supply Chain decisions are concerned with the timely and efficient procurement, allocation, and distribution of resources (e.g. funds, supplies, volunteers, money, employees) through a supply chain network. This course will explore methodologies for \u201cmedium term\u201d decision making including procurement and inventory policies, strategies for distribution and allocation of limited resources, and supply chain design.\u003C\/p\u003E\r\n\r\n\u003Ch3\u003E\u003Cstrong\u003EWho Should Attend\u003C\/strong\u003E\u003C\/h3\u003E\r\n\r\n\u003Cp\u003EThis course is designed for representatives from governmental or non-governmental organizations, private corporations, military, and foundations, including but not limited to senior executives overseeing administrative and operational functions of an organization, logistics and supply chain managers, program managers, directors of field operations, directors of emergency\/disaster preparedness and response, and public health professionals.\u003C\/p\u003E\r\n\r\n\u003Ch3\u003E\u003Cstrong\u003EHow You Will Benefit\u003C\/strong\u003E\u003C\/h3\u003E\r\n\r\n\u003Cul\u003E\r\n\t\u003Cli\u003EProvide immediate impact to your organization through applied and real-world case studies.\u003C\/li\u003E\r\n\t\u003Cli\u003ELearn to bring NGOs, businesses, or government entities together to enhance collaboration, cooperation, and communication.\u003C\/li\u003E\r\n\t\u003Cli\u003EDiscover current trends and procedures to help your organization and team members get and stay ahead of the curve.\u003C\/li\u003E\r\n\u003C\/ul\u003E\r\n\r\n\u003Ch3\u003E\u003Cstrong\u003EWhat Is Covered\u003C\/strong\u003E\u003C\/h3\u003E\r\n\r\n\u003Cul\u003E\r\n\t\u003Cli\u003EProcurement decisions\u003C\/li\u003E\r\n\t\u003Cli\u003EInventory management techniques for a single event versus ongoing operations under uncertainty\u003C\/li\u003E\r\n\t\u003Cli\u003EStrategies for resource allocation geographically and over time\u003C\/li\u003E\r\n\u003C\/ul\u003E\r\n\r\n\u003Ch3\u003E\u003Cstrong\u003EAbout the Course and the\u0026nbsp;HHSCM Course Series\u003C\/strong\u003E\u003C\/h3\u003E\r\n\r\n\u003Cp\u003EThis course is the second in a 3-part virtually synchronous professional education program. Register and pay for all three required\u0026nbsp;\u003Ca href=\u0022https:\/\/pe.gatech.edu\/node\/54\u0022\u003EHealth and Humanitarian Supply Chain Management Certificate courses\u003C\/a\u003E\u0026nbsp;and receive a discount of $400 off per course. Enter coupon code\u0026nbsp;\u003Cstrong\u003ESCL-HHS\u003C\/strong\u003E\u0026nbsp;at checkout with the Georgia Tech Professional Education website..\u0026nbsp;\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003EAdditionally, there are scholarships available for the certificate program. Apply at\u0026nbsp;\u003Ca href=\u0022https:\/\/hhls.scl.gatech.edu\/\u0022\u003Ehttps:\/\/hhls.scl.gatech.edu\/\u003C\/a\u003E\u0026nbsp;by February 19, 2023.\u0026nbsp;\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003EQuestions? Reach out to\u0026nbsp;\u003Ca href=\u0022mailto:chhs@gatech.edu\u0022\u003Echhs@gatech.edu\u003C\/a\u003E!\u003C\/p\u003E\r\n","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EThis course explores methodologies for tactical decision making including procurement and inventory policies, strategies for distribution and allocation of limited resources, and transportation decisions.\u003C\/p\u003E\r\n","format":"limited_html"}],"field_summary_sentence":[{"value":"Inventory availability is the most important aspect of customer service, and the cost of inventory is one of the most important entries on a company\u0027s balance sheet."}],"uid":"27233","created_gmt":"2024-04-22 12:15:16","changed_gmt":"2024-04-22 12:24:56","author":"Andy Haleblian","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2024-05-13T09:30:00-04:00","event_time_end":"2024-05-16T13:00:00-04:00","event_time_end_last":"2024-05-16T13:00:00-04:00","gmt_time_start":"2024-05-13 13:30:00","gmt_time_end":"2024-05-16 17:00:00","gmt_time_end_last":"2024-05-16 17:00:00","rrule":null,"timezone":"America\/New_York"},"location":"Virtual\/Instructor-led","extras":[],"related_links":[{"url":"https:\/\/pe.gatech.edu\/courses\/inventory-management-and-resource-allocation-supply-chains","title":"Registration link via Georgia Tech Professional Education"},{"url":"https:\/\/chhs.gatech.edu\/education\/professional-education\/course\/invmgmt","title":"Course Details via Center for Health and Humanitarian Systems website"},{"url":"https:\/\/pe.gatech.edu\/certificates\/health-humanitarian-supply-chain-management-certificate","title":"Health \u0026 Humanitarian Supply Chain Management Certificate"},{"url":"https:\/\/hhls.scl.gatech.edu\/","title":"Apply for a Scholarship!"}],"groups":[{"id":"1250","name":"Center for Health and Humanitarian Systems (CHHS)"},{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"},{"id":"1243","name":"The Supply Chain and Logistics Institute (SCL)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"10377","name":"Career\/Professional development"}],"invited_audience":[{"id":"78771","name":"Public"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[{"value":"\u003Cp\u003Echhs@gatech.edu\u0026nbsp;\u003C\/p\u003E\r\n","format":"limited_html"}],"email":[],"slides":[],"orientation":[],"userdata":""}},"674176":{"#nid":"674176","#data":{"type":"event","title":"ISYE Statistic Seminar - Guang Cheng","body":[{"value":"\u003Cblockquote\u003E\r\n\u003Cp\u003E\u003Cstrong\u003ETitle\u003C\/strong\u003E: Watermarking of Generative Tabular Data\u003Cbr \/\u003E\r\n\u003Cbr \/\u003E\r\n\u003Cstrong\u003EAbstract\u003C\/strong\u003E: In the first half of this talk, we provide an overview of synthetic data generation and claim that \u0022creating something out of nothing\u0022 is possible and beneficial through concrete examples. This prompts the exploration of \u0022Generative Data Science,\u0022 which elucidates the underlying principles behind generative AI, and we further highlight its distinctions from statistical machine learning.\u003Cbr \/\u003E\r\nThe latter half of this talk showcases our recent research on watermarking, an essential technique for establishing ownership in generative data, as an embodiment of generative data science. Specifically, we illustrate the embedding and detecting (invisible) watermarks in generative tabular data, ensuring their resilience against attacks through rigorous statistical analyses and theoretical validation.\u003Cbr \/\u003E\r\n\u003Cbr \/\u003E\r\n\u003Cstrong\u003EBio\u003C\/strong\u003E:\u0026nbsp;\u003Cstrong\u003EGuang Cheng\u003C\/strong\u003E\u0026nbsp;is a Professor of Statistics and Data Science at UCLA and leads the Trustworthy AI Lab (\u003Ca href=\u0022https:\/\/www.stat.ucla.edu\/~guangcheng\/\u0022 rel=\u0022noopener noreferrer\u0022 target=\u0022_blank\u0022\u003Ehttps:\/\/www.stat.ucla.edu\/~guangcheng\/\u003C\/a\u003E). He received his BA in Economics from Tsinghua University in 2002 and PhD in Statistics from the University of Wisconsin-Madison in 2006. His research interests include generative data science, deep learning theory, and statistical machine learning. Cheng is an Institute of Mathematical Statistics Fellow, Simons Fellow in Mathematics, NSF CAREER awardee, and a member of the Institute for Advanced Study, Princeton.\u0026nbsp;\u003C\/p\u003E\r\n\u003C\/blockquote\u003E\r\n","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cblockquote\u003E\r\n\u003Cp\u003E\u003Cstrong\u003EAbstract\u003C\/strong\u003E: In the first half of this talk, we provide an overview of synthetic data generation and claim that \u0022creating something out of nothing\u0022 is possible and beneficial through concrete examples. This prompts the exploration of \u0022Generative Data Science,\u0022 which elucidates the underlying principles behind generative AI, and we further highlight its distinctions from statistical machine learning.\u003Cbr \/\u003E\r\nThe latter half of this talk showcases our recent research on watermarking, an essential technique for establishing ownership in generative data, as an embodiment of generative data science. Specifically, we illustrate the embedding and detecting (invisible) watermarks in generative tabular data, ensuring their resilience against attacks through rigorous statistical analyses and theoretical validation.\u003C\/p\u003E\r\n\u003C\/blockquote\u003E\r\n","format":"limited_html"}],"field_summary_sentence":[{"value":"Watermarking of Generative Tabular Data"}],"uid":"36433","created_gmt":"2024-04-12 20:34:47","changed_gmt":"2024-04-12 20:36:19","author":"mrussell89","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2024-04-16T11:00:00-04:00","event_time_end":"2024-04-16T12:00:00-04:00","event_time_end_last":"2024-04-16T12:00:00-04:00","gmt_time_start":"2024-04-16 15:00:00","gmt_time_end":"2024-04-16 16:00:00","gmt_time_end_last":"2024-04-16 16:00:00","rrule":null,"timezone":"America\/New_York"},"location":"Groseclose 402","extras":["free_food"],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"177814","name":"Postdoc"},{"id":"78771","name":"Public"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"674170":{"#nid":"674170","#data":{"type":"event","title":"ISYE Statistic Seminar - Yiping Lu","body":[{"value":"\u003Cp\u003E\u003Cstrong\u003ETitle\u003C\/strong\u003E: Title Simulation-Calibrated Scientific Machine Learning\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003EAbstract\u003C\/strong\u003E: Machine learning (ML) has achieved great success in a variety of applications suggesting a new way to build flexible, universal, and efficient approximators for complex high-dimensional data. These successes have inspired many researchers to apply ML to other scientific applications such as industrial engineering, scientific computing, and operational research, where similar challenges often occur. However, the luminous success of ML is overshadowed by persistent concerns that the mathematical theory of large-scale machine learning, especially deep learning, is still lacking and the trained ML predictor is always biased. In this talk, I\u2019ll introduce a novel framework of (S)imulation-(Ca)librated (S)cientific (M)achine (L)earning (SCaSML), which can leverage the structure of physical models to achieve the following goals: 1) make unbiased predictions even based on biased machine learning predictors; 2) beat the curse of dimensionality with an estimator suffers from it. The SCASML paradigm combines a (possibly) biased machine learning algorithm with a de-biasing step design using rigorous numerical analysis and stochastic simulation. Theoretically, I\u2019ll try to understand whether the SCaSML algorithms are optimal and what factors (e.g., smoothness, dimension, and boundness) determine the improvement of the convergence rate. Empirically, I\u2019ll introduce different estimators that enable unbiased and trustworthy estimation for physical quantities with a biased machine learning estimator. Applications include but are not limited to estimating the moment of a function, simulating high-dimensional stochastic processes, uncertainty quantification using bootstrap methods, and randomized linear algebra.\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003EBio\u003C\/strong\u003E: Dr.Yiping Lu is \u0026nbsp;Courant instructor at Courant Institute of Mathematical Sciences, New York University, and an incoming tenure-track assistant professor at Industrial Engineering \u0026amp; Management Science, Northwestern University. I received my Ph.D. degree in applied math from Stanford University in 2023 and my Bachelor\u2019s degree in applied math from Peking University in 2019. \u0026nbsp;The long-term goal of my research is to develop a hybrid scientific research discipline that combines domain knowledge (differential equation, stochastic process, control,\u2026), machine learning and (randomized) experiments. To this end, I\u2019m working on an interdisciplinary research approach across probability and statistics, machine learning, numerical algorithms, control theory, signal processing\/inverse problem, and operations research. Yiping was a recipient of the Conference on Parsimony and Learning (CPAL) Rising Star Award in 2024, the Rising Star in Data Science from the University of Chicago in 2022, the Stanford Interdisciplinary Graduate Fellowship and the SenseTime Scholarship in 2021 for undergraduates in AI in 2019. He also serves as an area chair\/senior PC member for NeurIPS and AISTATS. Homepage: https:\/\/2prime.github.io\/\u003C\/p\u003E\r\n","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003E\u003Cstrong\u003EAbstract\u003C\/strong\u003E: Machine learning (ML) has achieved great success in a variety of applications suggesting a new way to build flexible, universal, and efficient approximators for complex high-dimensional data. These successes have inspired many researchers to apply ML to other scientific applications such as industrial engineering, scientific computing, and operational research, where similar challenges often occur. However, the luminous success of ML is overshadowed by persistent concerns that the mathematical theory of large-scale machine learning, especially deep learning, is still lacking and the trained ML predictor is always biased. In this talk, I\u2019ll introduce a novel framework of (S)imulation-(Ca)librated (S)cientific (M)achine (L)earning (SCaSML), which can leverage the structure of physical models to achieve the following goals: 1) make unbiased predictions even based on biased machine learning predictors; 2) beat the curse of dimensionality with an estimator suffers from it. The SCASML paradigm combines a (possibly) biased machine learning algorithm with a de-biasing step design using rigorous numerical analysis and stochastic simulation. Theoretically, I\u2019ll try to understand whether the SCaSML algorithms are optimal and what factors (e.g., smoothness, dimension, and boundness) determine the improvement of the convergence rate. Empirically, I\u2019ll introduce different estimators that enable unbiased and trustworthy estimation for physical quantities with a biased machine learning estimator. Applications include but are not limited to estimating the moment of a function, simulating high-dimensional stochastic processes, uncertainty quantification using bootstrap methods, and randomized linear algebra.\u003C\/p\u003E\r\n","format":"limited_html"}],"field_summary_sentence":[{"value":"Title Simulation-Calibrated Scientific Machine Learning"}],"uid":"36433","created_gmt":"2024-04-12 20:01:40","changed_gmt":"2024-04-12 20:04:12","author":"mrussell89","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2024-04-18T12:30:00-04:00","event_time_end":"2024-04-18T13:30:00-04:00","event_time_end_last":"2024-04-18T13:30:00-04:00","gmt_time_start":"2024-04-18 16:30:00","gmt_time_end":"2024-04-18 17:30:00","gmt_time_end_last":"2024-04-18 17:30:00","rrule":null,"timezone":"America\/New_York"},"location":"Groseclose 118","extras":["free_food"],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"},{"id":"177814","name":"Postdoc"},{"id":"78771","name":"Public"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"674036":{"#nid":"674036","#data":{"type":"event","title":"ISyE Seminar Speaker - Yihong Wu","body":[{"value":"\u003Cp\u003ETitle:\u003C\/p\u003E\r\n\r\n\u003Cp\u003ERecent advances in Empirical Bayes: statistical and optimization perspectives\u003C\/p\u003E\r\n\r\n\u003Cp\u003EAbstract:\u003C\/p\u003E\r\n\r\n\u003Cp\u003EIntroduced by Robbins in the 1950s, Empirical Bayes is a powerful approach and popular framework for large-scale inference that aims at learning and adapting to latent structure in data, by finding data-driven estimators to compete with the Bayesian oracle that knows the true prior. This talk surveys some recent theoretical, methodological, and algorithmic advances in empirical Bayes, in both classical sequence models and extensions where latent variables and data interact through more complex designs. A central theme of this talk is the nonparametric maximum likelihood estimator of Kiefer and Wolfowitz. Along the way, various open problems in the theory and practice of empirical Bayes will be discussed.\u003C\/p\u003E\r\n\r\n\u003Cp\u003EThis talk is based on joint works with Zhou Fan (Yale), Leying Guan (Yale), Soham Jana (Princeton), Yury Polyanskiy (MIT), and Yandi Shen (Yale): https:\/\/arxiv.org\/abs\/2008.08244, https:\/\/arxiv.org\/abs\/2109.03943, https:\/\/arxiv.org\/abs\/2209.01328, https:\/\/arxiv.org\/abs\/2211.12692, https:\/\/arxiv.org\/abs\/2312.12708\u003C\/p\u003E\r\n\r\n\u003Cp\u003EBio:\u003C\/p\u003E\r\n\r\n\u003Cp\u003EYihong Wu is a Professor in the Department of Statistics and Data Science at Yale University. He received his B.E. degree from Tsinghua University in 2006 and Ph.D. degree from Princeton University in 2011. He was a postdoctoral fellow with the Statistics Department in The Wharton School at the University of Pennsylvania from 2011 to 2012 and an assistant professor in the Department of ECE at the University of Illinois at Urbana-Champaign from 2013 to 2015. His research interests are in the theoretical and algorithmic aspects of high-dimensional statistics, information theory, and optimization. He was elected an IMS fellow in 2023 and was a recipient of the NSF CAREER award in 2017 and the Sloan Research Fellowship in Mathematics in 2018.\u003Cbr \/\u003E\r\n\u0026nbsp;\u003C\/p\u003E\r\n","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EAbstract:\u003C\/p\u003E\r\n\r\n\u003Cp\u003EIntroduced by Robbins in the 1950s, Empirical Bayes is a powerful approach and popular framework for large-scale inference that aims at learning and adapting to latent structure in data, by finding data-driven estimators to compete with the Bayesian oracle that knows the true prior. This talk surveys some recent theoretical, methodological, and algorithmic advances in empirical Bayes, in both classical sequence models and extensions where latent variables and data interact through more complex designs. A central theme of this talk is the nonparametric maximum likelihood estimator of Kiefer and Wolfowitz. Along the way, various open problems in the theory and practice of empirical Bayes will be discussed.\u003C\/p\u003E\r\n\r\n\u003Cp\u003EThis talk is based on joint works with Zhou Fan (Yale), Leying Guan (Yale), Soham Jana (Princeton), Yury Polyanskiy (MIT), and Yandi Shen (Yale): https:\/\/arxiv.org\/abs\/2008.08244, https:\/\/arxiv.org\/abs\/2109.03943, https:\/\/arxiv.org\/abs\/2209.01328, https:\/\/arxiv.org\/abs\/2211.12692, https:\/\/arxiv.org\/abs\/2312.12708\u003C\/p\u003E\r\n","format":"limited_html"}],"field_summary_sentence":[{"value":"Recent advances in Empirical Bayes: statistical and optimization perspectives"}],"uid":"36374","created_gmt":"2024-04-08 12:26:30","changed_gmt":"2024-04-08 21:54:42","author":"mwelch39","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2024-04-12T11:30:00-04:00","event_time_end":"2024-04-12T12:30:00-04:00","event_time_end_last":"2024-04-12T12:30:00-04:00","gmt_time_start":"2024-04-12 15:30:00","gmt_time_end":"2024-04-12 16:30:00","gmt_time_end_last":"2024-04-12 16:30:00","rrule":null,"timezone":"America\/New_York"},"location":" ISYE Groseclose 402","extras":[],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"174045","name":"Graduate students"},{"id":"177814","name":"Postdoc"},{"id":"78771","name":"Public"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"669548":{"#nid":"669548","#data":{"type":"event","title":"Day In the Atrium - Walmart","body":[{"value":"\u003Cp\u003EWe can\u0027t wait to welcome Walmart on campus next week! Be sure to stop by for Day In The Atrium with Walmart, on September 14 at 11AM.\u003C\/p\u003E\r\n\r\n\u003Cp\u003EThis is a great time to network and connect with industry partners in the field to learn about upcoming opportunities and resources.\u003C\/p\u003E\r\n\r\n\u003Cp\u003ERefreshments will be served.\u003C\/p\u003E\r\n","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EBe sure to stop by for Day In The Atrium with Walmart, on September 14 at 11AM.\u003C\/p\u003E\r\n","format":"limited_html"}],"field_summary_sentence":[{"value":"Be sure to stop by for Day In The Atrium with Walmart, on September 14 at 11AM"}],"uid":"36284","created_gmt":"2023-09-08 17:43:23","changed_gmt":"2024-04-08 20:57:33","author":"chenriquez8","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2023-09-14T11:00:00-04:00","event_time_end":"2023-09-14T14:00:00-04:00","event_time_end_last":"2023-09-14T14:00:00-04:00","gmt_time_start":"2023-09-14 15:00:00","gmt_time_end":"2023-09-14 18:00:00","gmt_time_end_last":"2023-09-14 18:00:00","rrule":null,"timezone":"America\/New_York"},"location":"ISyE Main, Atrium - 2nd Floor","extras":[],"hg_media":{"671658":{"id":"671658","type":"image","title":"Day In the Atrium, Walmart","body":null,"created":"1694195011","gmt_created":"2023-09-08 17:43:31","changed":"1694195011","gmt_changed":"2023-09-08 17:43:31","alt":"Flyer for Day In the Atrium with Walmart","file":{"fid":"254759","name":"1.png","image_path":"\/sites\/default\/files\/2023\/09\/08\/1.png","image_full_path":"http:\/\/hg.gatech.edu\/\/sites\/default\/files\/2023\/09\/08\/1.png","mime":"image\/png","size":263110,"path_740":"http:\/\/hg.gatech.edu\/sites\/default\/files\/styles\/740xx_scale\/public\/2023\/09\/08\/1.png?itok=ObKZSRU2"}}},"media_ids":["671658"],"related_links":[{"url":"https:\/\/tech.walmart.com\/content\/walmart-global-tech\/en_us.html","title":"Walmart Global Tech"}],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"10377","name":"Career\/Professional development"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"174045","name":"Graduate students"},{"id":"177814","name":"Postdoc"},{"id":"78771","name":"Public"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[{"value":"\u003Cp\u003EDonald Phan,\u0026nbsp;Development Assc-Fundraising\u003C\/p\u003E\r\n\r\n\u003Cp\u003Edonald.phan@isye.gatech.edu\u003C\/p\u003E\r\n","format":"limited_html"}],"email":[],"slides":[],"orientation":[],"userdata":""}},"674063":{"#nid":"674063","#data":{"type":"event","title":"SCL Course: Engineering the Warehouse (Virtual\/Instructor-led)","body":[{"value":"\u003Ch4\u003ECOURSE DESCRIPTION\u003C\/h4\u003E\r\n\r\n\u003Cp\u003EThe requirement for high levels of customer service, increasing numbers of SKUs and high labor costs have dramatically increased the complexity of warehouse operations. It is no longer sufficient to manage a warehouse based on a simple, arbitrary \u201cABC\u201d classification of SKUs, which treats all those in a category as if they were identical. Instead, each decision \u2013 such as where to store or where to pick product \u2013 must be based on careful engineering and economic analysis. Each SKU must identify its own cheapest, fastest path through the warehouse to the customer and then compete with all the other SKUs for the necessary resources. This results in warehouse operations that are finely tuned to patterns of customer orders and maximally efficient. Learn the concepts necessary to address modern warehouse trade-offs between space and time in optimizing and managing your warehouse.\u003C\/p\u003E\r\n\r\n\u003Cp\u003EEssential learning for those who are seeking cost reductions through better handling methods. Also valuable for those who must replace, upgrade, or add material handling equipment.\u0026nbsp;The two-day course will include case examples and a guided exercise to ensure mastery of the techniques presented.\u003C\/p\u003E\r\n\r\n\u003Ch4\u003EWHO SHOULD ATTEND\u003C\/h4\u003E\r\n\r\n\u003Cp\u003ESupply chain and logistics consultants, supply chain engineers and analysts, facility engineers, and warehouse supervisors and team leaders\u003C\/p\u003E\r\n\r\n\u003Ch4\u003EHOW YOU WILL BENEFIT\u003C\/h4\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003EUpon completion of this course, you will be able to:\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cul\u003E\r\n\t\u003Cli\u003EExchange space for time (or vice versa) to better meet business objectives.\u003C\/li\u003E\r\n\t\u003Cli\u003EUnderstand when to use dedicated storage and when to use shared storage.\u003C\/li\u003E\r\n\t\u003Cli\u003EIdentify the most convenient locations in a warehouse based on an economic model.\u003C\/li\u003E\r\n\t\u003Cli\u003EIdentify patterns in customer orders and exploit these to speed fulfillment.\u003C\/li\u003E\r\n\t\u003Cli\u003EEvaluate warehouse performance.\u003C\/li\u003E\r\n\t\u003Cli\u003EOptimally size and stock a forward pick area.\u003C\/li\u003E\r\n\t\u003Cli\u003EUnderstand the best practices in order-picking.\u003C\/li\u003E\r\n\u003C\/ul\u003E\r\n\r\n\u003Ch4\u003EWHAT IS COVERED\u003C\/h4\u003E\r\n\r\n\u003Cul\u003E\r\n\t\u003Cli\u003EWarehouse performance\u003C\/li\u003E\r\n\t\u003Cli\u003EModern warehouse trade-offs\u003C\/li\u003E\r\n\t\u003Cli\u003ESize and stocking optimization\u003C\/li\u003E\r\n\t\u003Cli\u003EOrder-picking best practices\u003C\/li\u003E\r\n\t\u003Cli\u003EAutomation\u003C\/li\u003E\r\n\u003C\/ul\u003E\r\n\r\n\u003Ch4\u003ECOURSE MATERIALS\u003C\/h4\u003E\r\n\r\n\u003Cul\u003E\r\n\t\u003Cli\u003EOnline access to course material in electronic format\u0026nbsp;\u003C\/li\u003E\r\n\t\u003Cli\u003EAccess to an e-copy of the book \u201cWarehouse \u0026amp; Distribution Science\u201d\u0026nbsp;as well as access to an accompanying suite of software to aid in warehouse analytics and optimization.\u003C\/li\u003E\r\n\u003C\/ul\u003E\r\n\r\n\u003Ch4\u003ECOURSE PREREQUISITES\u003C\/h4\u003E\r\n\r\n\u003Cp\u003ENone.\u003C\/p\u003E\r\n\r\n\u003Ch4\u003ECERTIFICATE INFORMATION\u003C\/h4\u003E\r\n\r\n\u003Cp\u003EThis course is part of the \u003Ca href=\u0022https:\/\/www.scl.gatech.edu\/education\/professional-education\/courses#DOAD\u0022\u003EDistribution Operations Analysis \u0026amp; Design (DOAD) Certificate\u003C\/a\u003E.\u003C\/p\u003E\r\n","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EThe requirement for high levels of customer service, increasing numbers of SKUs and high labor costs have dramatically increased the complexity of warehouse operations. It is no longer sufficient to manage a warehouse based on a simple, arbitrary \u201cABC\u201d classification of SKUs, which treats all those in a category as if they were identical. Instead, each decision \u2013 such as where to store or where to pick product \u2013 must be based on careful engineering and economic analysis.\u003C\/p\u003E\r\n","format":"limited_html"}],"field_summary_sentence":[{"value":"Learn the concepts necessary to address modern warehouse trade-offs between space and time in optimizing and managing your warehouse."}],"uid":"27233","created_gmt":"2024-04-08 20:41:02","changed_gmt":"2024-04-08 20:43:29","author":"Andy Haleblian","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2024-08-13T11:00:00-04:00","event_time_end":"2024-08-16T17:00:00-04:00","event_time_end_last":"2024-08-16T17:00:00-04:00","gmt_time_start":"2024-08-13 15:00:00","gmt_time_end":"2024-08-16 21:00:00","gmt_time_end_last":"2024-08-16 21:00:00","rrule":null,"timezone":"America\/New_York"},"location":"Online\/Virtually-led","extras":[],"related_links":[{"url":"https:\/\/www.scl.gatech.edu\/engwh","title":"Course webpage within the SCL website"}],"groups":[{"id":"1250","name":"Center for Health and Humanitarian Systems (CHHS)"},{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"},{"id":"1243","name":"The Supply Chain and Logistics Institute (SCL)"}],"categories":[],"keywords":[{"id":"6140","name":"warehousing"},{"id":"7149","name":"inventory"},{"id":"167167","name":"storage"},{"id":"122741","name":"physical internet"},{"id":"143871","name":"Physical Internet Center"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"10377","name":"Career\/Professional development"}],"invited_audience":[{"id":"78771","name":"Public"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[{"value":"\u003Cp\u003E\u003Ca href=\u0022mailto:info@scl.gatech.edu\u0022\u003Einfo@scl.gatech.edu\u003C\/a\u003E\u003C\/p\u003E\r\n","format":"limited_html"}],"email":[],"slides":[],"orientation":[],"userdata":""}},"674024":{"#nid":"674024","#data":{"type":"event","title":"ISyE Seminar - Xiaochen Xian","body":[{"value":"\u003Ch3\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cstrong\u003E\u003Cspan\u003ETitle:\u003C\/span\u003E\u003C\/strong\u003E\u003Cspan\u003E \u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/h3\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003EEmpowering Multiple Awareness Levels: Data-driven Decision-making and System Informatics for Computationally Aware Systems\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Ch3\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cstrong\u003E\u003Cspan\u003EAbstract:\u003C\/span\u003E\u003C\/strong\u003E\u003Cspan\u003E \u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/h3\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003EIn recent years, our endeavors to combat pandemics, cybersecurity, and wildfire, have highlighted the significance of computational awareness that empowers proactive decision-making and action-taking to mitigate potential risks and minimize the impact of adverse events. Computationally aware systems acquire, process, and distribute information related to what happened, what is happening, and what is going to happen in an environment to detect potential changes and anomalies. K\u003Cspan\u003Eey challenges across\u003C\/span\u003E different awareness levels \u003Cspan\u003Ehinder wider and more intelligent application of \u003C\/span\u003Ecomputationally aware systems,\u003Cspan\u003E including: (i) data variability, uncertainty, and inconsistent granularities; (ii) limited system resources for sensing, processing, and decision-making, and (iii) the complicated interactions and ever-changing dynamics in the environment of interest.\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003EIn this presentation, I will focus on three directions of works on the sensory-level, representation-level, and cross-level awareness: (i) a weakly supervised learning method to tackle data granularity challenges; (ii) an on-demand learning method to address label-induced constraints for classification problems under resource constraints; (iii) pathwise sampling methods for online data-driven routing and detection. The theoretical, numerical, and experimental investigations demonstrate the potential for significant improvements in the accuracy, timeliness, and autonomy of the detection of abnormal events, thereby mitigating potential catastrophic consequences and adverse outcomes.\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Ch3\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cstrong\u003E\u003Cspan\u003EBiograph: \u003C\/span\u003E\u003C\/strong\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/h3\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003EDr. Xiaochen Xian is currently an assistant professor in the Department of Industrial and Systems Engineering at the University of Florida. She received her B.S. degree in Mathematics and Applied Mathematics from Zhejiang University, China in 2014, and the M.S. degree in Statistics, and the Ph.D. degree in Industrial and Systems Engineering from the University of Wisconsin-Madison in 2017 and 2019.\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003EDr. Xian\u2019s research focuses on computationally aware systems with a special interest in novel methodologies in data-driven decision-making and machine learning under constraint to enable theoretically sound and viable analytical tools.\u003Cspan\u003E Her research has been supported by federal and local agencies including NSF, NIH, the Florida Center for Cybersecurity, and \u003C\/span\u003Ethe Florida Space Grant Consortium.\u003Cspan\u003E She is the \u003C\/span\u003Erecipient of multiple awards, including NIH NIBIB Trailblazer Award, Cottmeyer Family Faculty Fellowships, finalist of INFORMS QSR Best Referred Paper, INFORMS DMDA Workshop Best Paper, and IISE QCRE Best Track Paper, second runner-up of Best Paper Award in IEEE TASE, feature articles in IISE magazine, AIE, and YoungStats. Dr. Xian is an associate editor of IEEE Transactions on Automation Science and Engineering and IEEE International Conference on Automation Science and Engineering. \u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Ch3\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cstrong\u003E\u003Cspan\u003EAbstract:\u003C\/span\u003E\u003C\/strong\u003E\u003Cspan\u003E \u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/h3\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003EIn recent years, our endeavors to combat pandemics, cybersecurity, and wildfire, have highlighted the significance of computational awareness that empowers proactive decision-making and action-taking to mitigate potential risks and minimize the impact of adverse events. Computationally aware systems acquire, process, and distribute information related to what happened, what is happening, and what is going to happen in an environment to detect potential changes and anomalies. K\u003Cspan\u003Eey challenges across\u003C\/span\u003E different awareness levels \u003Cspan\u003Ehinder wider and more intelligent application of \u003C\/span\u003Ecomputationally aware systems,\u003Cspan\u003E including: (i) data variability, uncertainty, and inconsistent granularities; (ii) limited system resources for sensing, processing, and decision-making, and (iii) the complicated interactions and ever-changing dynamics in the environment of interest.\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003EIn this presentation, I will focus on three directions of works on the sensory-level, representation-level, and cross-level awareness: (i) a weakly supervised learning method to tackle data granularity challenges; (ii) an on-demand learning method to address label-induced constraints for classification problems under resource constraints; (iii) pathwise sampling methods for online data-driven routing and detection. The theoretical, numerical, and experimental investigations demonstrate the potential for significant improvements in the accuracy, timeliness, and autonomy of the detection of abnormal events, thereby mitigating potential catastrophic consequences and adverse outcomes.\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n","format":"limited_html"}],"field_summary_sentence":[{"value":"Empowering Multiple Awareness Levels: Data-driven Decision-making and System Informatics for Computationally Aware Systems"}],"uid":"34977","created_gmt":"2024-04-05 15:39:26","changed_gmt":"2024-04-05 15:42:05","author":"Julie Smith","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2024-04-15T11:00:00-04:00","event_time_end":"2024-04-15T12:00:00-04:00","event_time_end_last":"2024-04-15T12:00:00-04:00","gmt_time_start":"2024-04-15 15:00:00","gmt_time_end":"2024-04-15 16:00:00","gmt_time_end_last":"2024-04-15 16:00:00","rrule":null,"timezone":"America\/New_York"},"location":"ISyE Groseclose 402","extras":[],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"174045","name":"Graduate students"},{"id":"177814","name":"Postdoc"},{"id":"78751","name":"Undergraduate students"},{"id":"78771","name":"Public"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"673785":{"#nid":"673785","#data":{"type":"event","title":"ISYE Statistic Seminar - Song Mei","body":[{"value":"\u003Cp\u003ETitle: Revisiting neural network approximation theory in the age of generative AI\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003EAbstract: Textbooks on deep learning theory primarily perceive neural networks as universal function approximators. While this classical viewpoint is fundamental, it inadequately explains the impressive capabilities of modern generative AI models such as language models and diffusion models. This talk puts forth a refined perspective: neural networks often serve as algorithm approximators, going beyond mere function approximation. I will explain how this refined perspective offers a deeper insight into the success of modern generative AI models.\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003EBio: Song Mei is an assistant professor of statistics and EECS at UC Berkeley. He received his Ph. D. from Stanford in June 2020. Song\u2019s research lies at the intersection of statistics and machine learning. His recent research focuses on theory of deep learning and foundation models.\u0026nbsp;\u003C\/p\u003E\r\n","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EAbstract: Textbooks on deep learning theory primarily perceive neural networks as universal function approximators. While this classical viewpoint is fundamental, it inadequately explains the impressive capabilities of modern generative AI models such as language models and diffusion models. This talk puts forth a refined perspective: neural networks often serve as algorithm approximators, going beyond mere function approximation. I will explain how this refined perspective offers a deeper insight into the success of modern generative AI models.\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n","format":"limited_html"}],"field_summary_sentence":[{"value":"Revisiting neural network approximation theory in the age of generative AI "}],"uid":"36433","created_gmt":"2024-03-27 18:09:19","changed_gmt":"2024-03-27 18:13:33","author":"mrussell89","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2024-04-02T12:30:00-04:00","event_time_end":"2024-04-02T13:30:00-04:00","event_time_end_last":"2024-04-02T13:30:00-04:00","gmt_time_start":"2024-04-02 16:30:00","gmt_time_end":"2024-04-02 17:30:00","gmt_time_end_last":"2024-04-02 17:30:00","rrule":null,"timezone":"America\/New_York"},"location":"Groseclose 402","extras":["free_food"],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"174045","name":"Graduate students"},{"id":"177814","name":"Postdoc"},{"id":"78751","name":"Undergraduate students"},{"id":"78771","name":"Public"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"673637":{"#nid":"673637","#data":{"type":"event","title":"ISyE Seminar Speaker - Julia Yan","body":[{"value":"\u003Cp\u003E\u003Cstrong\u003ETitle:\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003ETrading flexibility for adoption: From dynamic to static walking in ridesharing\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003EAbstract:\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003EOn-demand ridesharing aims to fulfill riders\u0027 transportation needs whenever and wherever they want. \u0026nbsp;Although this service level appeals to riders, overall system efficiency can improve substantially if riders are willing to be flexible. Here, we explore riders\u0027 flexibility in space via walking to more accessible pickup locations. Ridesharing platforms have traditionally implemented dynamic walking to optimize rider pickup locations and rider-driver assignment jointly. \u0026nbsp;We propose an alternative that we call static walking, which presents a predetermined pickup location to the rider before optimizing rider-driver assignment. Although dynamic walking enables more efficient matching of riders and drivers, we hypothesize that riders prefer static walking because of the certainty of the pickup location before booking the ride. Using simulations on Lyft data, we show that static walking can capture up to 96% of the value of dynamic walking in congested urban networks at a fixed adoption rate. Furthermore, experimentation on Lyft\u0027s user interface suggests that providing riders with information on pickup location before an opt-in decision can increase walking adoption --- to the extent that static walking may outperform dynamic walking overall. More broadly, this study highlights the importance of carefully designing flexibility mechanisms on platforms: a little flexibility goes a long way, especially when flexibility presents a barrier to adoption.\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003EBio:\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003EJulia Yan is an Assistant Professor in the Operations and Logistics division at UBC\u2019s Sauder School of Business. Her interests are in applied optimization problems in urban mobility, and more broadly to problems of societal interest. Prior to joining UBC, she received her PhD from MIT and her AB from Princeton University; she also spent one year as a postdoctoral research fellow at Lyft\u0027s Rideshare Labs.\u003C\/p\u003E\r\n","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EOn-demand ridesharing aims to fulfill riders\u0027 transportation needs whenever and wherever they want. \u0026nbsp;Although this service level appeals to riders, overall system efficiency can improve substantially if riders are willing to be flexible. Here, we explore riders\u0027 flexibility in space via walking to more accessible pickup locations. Ridesharing platforms have traditionally implemented dynamic walking to optimize rider pickup locations and rider-driver assignment jointly. \u0026nbsp;We propose an alternative that we call static walking, which presents a predetermined pickup location to the rider before optimizing rider-driver assignment. Although dynamic walking enables more efficient matching of riders and drivers, we hypothesize that riders prefer static walking because of the certainty of the pickup location before booking the ride. Using simulations on Lyft data, we show that static walking can capture up to 96% of the value of dynamic walking in congested urban networks at a fixed adoption rate. Furthermore, experimentation on Lyft\u0027s user interface suggests that providing riders with information on pickup location before an opt-in decision can increase walking adoption --- to the extent that static walking may outperform dynamic walking overall. More broadly, this study highlights the importance of carefully designing flexibility mechanisms on platforms: a little flexibility goes a long way, especially when flexibility presents a barrier to adoption.\u003Cbr \/\u003E\r\n\u0026nbsp;\u003C\/p\u003E\r\n","format":"limited_html"}],"field_summary_sentence":[{"value":"Trading flexibility for adoption: From dynamic to static walking in ridesharing"}],"uid":"36374","created_gmt":"2024-03-21 03:34:32","changed_gmt":"2024-03-21 03:34:31","author":"mwelch39","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2024-03-29T11:30:00-04:00","event_time_end":"2024-03-29T12:30:00-04:00","event_time_end_last":"2024-03-29T12:30:00-04:00","gmt_time_start":"2024-03-29 15:30:00","gmt_time_end":"2024-03-29 16:30:00","gmt_time_end_last":"2024-03-29 16:30:00","rrule":null,"timezone":"America\/New_York"},"location":" ISYE Groseclose 402","extras":[],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"177814","name":"Postdoc"},{"id":"174045","name":"Graduate students"},{"id":"78771","name":"Public"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"673568":{"#nid":"673568","#data":{"type":"event","title":"ISyE Statistic Seminar - Jiming Jiang","body":[{"value":"\u003Ch3\u003ETitle:\u003C\/h3\u003E\r\n\r\n\u003Cp\u003EThat Prasad-Rao is Robust: Estimation of MSPE of OBP under Potential Model Misspecification\u003C\/p\u003E\r\n\r\n\u003Ch3\u003E\u003Cspan\u003E\u003Cspan\u003EAbstract:\u003C\/span\u003E\u003C\/span\u003E\u003C\/h3\u003E\r\n\r\n\u003Cp\u003E\u200b\u200b\u200b\u200b\u200b\u200b\u200b\u003Cspan\u003E\u003Cspan\u003EWe consider estimation of the mean squared prediction error (MSPE) of the observed best\u0026nbsp;\u003C\/span\u003E\u003C\/span\u003E\u003Cspan\u003E\u003Cspan\u003Epredictor (OBP) in small area estimation under an area-level model with potential model\u0026nbsp;\u003C\/span\u003E\u003C\/span\u003E\u003Cspan\u003E\u003Cspan\u003Emisspecification. It was previously thought that the traditional Prasad-Rao (P-R) linearization\u0026nbsp;\u003C\/span\u003E\u003C\/span\u003E\u003Cspan\u003E\u003Cspan\u003Emethod could not be used, because it is derived under the assumption that the underlying\u0026nbsp;\u003C\/span\u003E\u003C\/span\u003E\u003Cspan\u003E\u003Cspan\u003Emodel is correctly specified. However, we show that, when it comes to estimating the\u0026nbsp;unconditional MSPE, the PR estimator, derived for estimating the MSPE of OBP assuming\u0026nbsp;\u003C\/span\u003E\u003C\/span\u003E\u003Cspan\u003E\u003Cspan\u003Ethat the underlying model is correct, remains first-order unbiased even when the underlying\u0026nbsp;\u003C\/span\u003E\u003C\/span\u003E\u003Cspan\u003E\u003Cspan\u003Emodel is misspecified in its mean function. A second-order unbiased estimator of the MSPE\u0026nbsp;\u003C\/span\u003E\u003C\/span\u003E\u003Cspan\u003E\u003Cspan\u003Eis derived by modifying the PR MSPE estimator. The PR and modified PR estimators also\u0026nbsp;\u003C\/span\u003E\u003C\/span\u003E\u003Cspan\u003E\u003Cspan\u003Ehave much smaller variation compared to the existing MSPE estimators for OBP. The\u0026nbsp;\u003C\/span\u003E\u003C\/span\u003E\u003Cspan\u003E\u003Cspan\u003Etheoretical findings are supported by empirical results including simulation studies and\u0026nbsp;\u003C\/span\u003E\u003C\/span\u003E\u003Cspan\u003E\u003Cspan\u003Ereal-data applications. This work is joint with Xiaohui Liu and Haiqiang Ma of Jiangxi\u0026nbsp;\u003C\/span\u003E\u003C\/span\u003E\u003Cspan\u003E\u003Cspan\u003EUniversity of Finance and Economics.\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Ch3\u003E\u003Cspan\u003E\u003Cspan\u003EAbstract:\u003C\/span\u003E\u003C\/span\u003E\u003C\/h3\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003EWe consider estimation of the mean squared prediction error (MSPE) of the observed best\u0026nbsp;\u003C\/span\u003E\u003C\/span\u003E\u003Cspan\u003E\u003Cspan\u003Epredictor (OBP) in small area estimation under an area-level model with potential model\u0026nbsp;\u003C\/span\u003E\u003C\/span\u003E\u003Cspan\u003E\u003Cspan\u003Emisspecification. It was previously thought that the traditional Prasad-Rao (P-R) linearization\u0026nbsp;\u003C\/span\u003E\u003C\/span\u003E\u003Cspan\u003E\u003Cspan\u003Emethod could not be used, because it is derived under the assumption that the underlying\u0026nbsp;\u003C\/span\u003E\u003C\/span\u003E\u003Cspan\u003E\u003Cspan\u003Emodel is correctly specified. However, we show that, when it comes to estimating the\u0026nbsp;unconditional MSPE, the PR estimator, derived for estimating the MSPE of OBP assuming\u0026nbsp;\u003C\/span\u003E\u003C\/span\u003E\u003Cspan\u003E\u003Cspan\u003Ethat the underlying model is correct, remains first-order unbiased even when the underlying\u0026nbsp;\u003C\/span\u003E\u003C\/span\u003E\u003Cspan\u003E\u003Cspan\u003Emodel is misspecified in its mean function. A second-order unbiased estimator of the MSPE\u0026nbsp;\u003C\/span\u003E\u003C\/span\u003E\u003Cspan\u003E\u003Cspan\u003Eis derived by modifying the PR MSPE estimator. The PR and modified PR estimators also\u0026nbsp;\u003C\/span\u003E\u003C\/span\u003E\u003Cspan\u003E\u003Cspan\u003Ehave much smaller variation compared to the existing MSPE estimators for OBP. The\u0026nbsp;\u003C\/span\u003E\u003C\/span\u003E\u003Cspan\u003E\u003Cspan\u003Etheoretical findings are supported by empirical results including simulation studies and\u0026nbsp;\u003C\/span\u003E\u003C\/span\u003E\u003Cspan\u003E\u003Cspan\u003Ereal-data applications. This work is joint with Xiaohui Liu and Haiqiang Ma of Jiangxi\u0026nbsp;\u003C\/span\u003E\u003C\/span\u003E\u003Cspan\u003E\u003Cspan\u003EUniversity of Finance and Economics.\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n","format":"limited_html"}],"field_summary_sentence":[{"value":"That Prasad-Rao is Robust: Estimation of MSPE of OBP under Potential Model Misspecification"}],"uid":"34977","created_gmt":"2024-03-18 11:21:47","changed_gmt":"2024-03-18 11:21:46","author":"Julie Smith","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2024-03-26T11:00:00-04:00","event_time_end":"2024-03-26T12:00:00-04:00","event_time_end_last":"2024-03-26T12:00:00-04:00","gmt_time_start":"2024-03-26 15:00:00","gmt_time_end":"2024-03-26 16:00:00","gmt_time_end_last":"2024-03-26 16:00:00","rrule":null,"timezone":"America\/New_York"},"location":"ISyE Groseclose 402","extras":[],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"174045","name":"Graduate students"},{"id":"177814","name":"Postdoc"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"673434":{"#nid":"673434","#data":{"type":"event","title":"The Infinite Possibilities of an ISyE Degree \u0026 Other Impossible Things ","body":[{"value":"\u003Cp\u003E\u003Cstrong\u003E\u003Ca href=\u0022https:\/\/eforms.isye.gatech.edu\/form\/case-rsvp\u0022\u003EREGISTER NOW!\u003C\/a\u003E\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003ESeminar Description:\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003EFrom aerospace engineer to Disney Imagineer to Game Developer to Augmented Realities, Susan will discuss the foundational keys to success of her Industrial \u0026amp; Systems Engineering background and how it has helped her navigate different challenges, industries and team building. In addition to navigating all those roles and being a woman, she will discuss the advantages of higher education, training, curiosity and networking.\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cem\u003E\u003Cstrong\u003ELunch will be provided\u003C\/strong\u003E\u003C\/em\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003ESusan\u0027s Bio:\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003ESusan Bonds started as an aerospace engineer before becoming a Disney Imagineer, developing ground-breaking attractions like Indiana Jones Adventure at Disneyland, Alien Encounter at Walt Disney World, and Mission: SPACE at Epcot. She also ran the innovative Concept Studio within Imagineering, bringing stories to life with emerging technologies.\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003EIn gaming, she produced URU: Ages Beyond Myst, an MMORPG based on the popular PC games Myst and Riven. As founder of 42 Entertainment in 2004, she has developed and produced over 45+ immersive experiences and franchise masterplans \u2014 winning back-to-back Cyber Grand Prix Cannes Lions for Why So Serious (The Dark Knight) and Trent Reznor\u2019s Year Zero (NIN) concept album, and both directed and produced the documentary feature Zedd True Colors, one of EDM\u2019s top artists.\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003EA leader \u0026amp; speaker in immersive media and game design, Susan was named by Forbes Top 12 Women to Watch in Gaming, and has been featured in 60 Innovators Shaping our Creative Future, Wired, USA Today, New York Times, Nightline, WSJ, Contagious, Art of Immersion: How the Digital Generation is remaking Hollywood, Madison Avenue and the way we tell stories, and the Future of Storytelling.\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Ca href=\u0022https:\/\/eforms.isye.gatech.edu\/form\/case-rsvp\u0022\u003EREGISTER NOW!\u003C\/a\u003E\u003C\/p\u003E\r\n","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EFrom aerospace engineer to Disney Imagineer to Game Developer to Augmented Realities, Susan will discuss the foundational keys to success of her Industrial \u0026amp; Systems Engineering background\u003C\/p\u003E\r\n","format":"limited_html"}],"field_summary_sentence":[{"value":"From aerospace engineer to Disney Imagineer to Game Developer to Augmented Realities, Susan will discuss the foundational keys to success of her Industrial \u0026 Systems Engineering background"}],"uid":"36475","created_gmt":"2024-03-08 20:05:49","changed_gmt":"2024-03-08 20:07:21","author":"nmyles3","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2024-03-26T11:00:00-04:00","event_time_end":"2024-03-26T12:00:00-04:00","event_time_end_last":"2024-03-26T12:00:00-04:00","gmt_time_start":"2024-03-26 15:00:00","gmt_time_end":"2024-03-26 16:00:00","gmt_time_end_last":"2024-03-26 16:00:00","rrule":null,"timezone":"America\/New_York"},"location":"ISyE Main, 755 Ferst Drive, Atlanta, G A30332","extras":[],"hg_media":{"673351":{"id":"673351","type":"image","title":"Susan Bonds ","body":null,"created":"1709928424","gmt_created":"2024-03-08 20:07:04","changed":"1709928385","gmt_changed":"2024-03-08 20:06:25","alt":"Susan Bonds","file":{"fid":"256735","name":"SusanBonds Headshot.jpg","image_path":"\/sites\/default\/files\/2024\/03\/08\/SusanBonds%20Headshot_0.jpg","image_full_path":"http:\/\/hg.gatech.edu\/\/sites\/default\/files\/2024\/03\/08\/SusanBonds%20Headshot_0.jpg","mime":"image\/jpeg","size":34652,"path_740":"http:\/\/hg.gatech.edu\/sites\/default\/files\/styles\/740xx_scale\/public\/2024\/03\/08\/SusanBonds%20Headshot_0.jpg?itok=dLymfuOQ"}}},"media_ids":["673351"],"groups":[{"id":"660354","name":"Center for Academics, Success, and Equity"},{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[{"id":"193512","name":"Women in Industrial \u0026 Systems Engineering"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"10377","name":"Career\/Professional development"},{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"174045","name":"Graduate students"},{"id":"177814","name":"Postdoc"},{"id":"78771","name":"Public"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"673427":{"#nid":"673427","#data":{"type":"event","title":"ISyE Seminar Speaker - Garud Iyengar","body":[{"value":"\u003Cp\u003E\u003Cstrong\u003ETitle:\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDoubly robust methods for multi armed bandits\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003EAbstract:\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003EIn a multi-arm bandit (MAB) setting, in each epoch, the decision maker chooses one arm and observes the reward associated with only that arm, i.e. the rewards of all other arms are missing! Doubly robust (DR) estimation is a well-known technique in the statistics literature on handling missing data. The pseudo-reward samples generated by the DR technique are unbiased provided either the model for the data or the probability that the data is missing is known. In the MAB setting, the probability that the data for an arm is missing is known! Therefore, the DR technique generates unbiased pseudo-rewards for the unselected arms. In each round, the conventional methods for MAB update the estimate for the reward of the chosen arm, and the DR estimator imputes the missing rewards and computes new estimates for all arms in all rounds. Thus, there is a possibility that DR estimates for the rewards of all arms converge uniformly independent of the specific arm selection policy used. This potentially allows us to simultaneously reduce regret and optimize other criteria, e.g. identify the best arm, or the arms on a Pareto front. We show that this is indeed possible in many contexts such as revenue management, Pareto front identification, and sparse linear reinforcement learning, and this leads to improved theoretical guarantees and empirical performance.\u003C\/p\u003E\r\n\r\n\u003Cp\u003EJoint work with Wonyoung (Tim) Kim and Assaf Zeevi\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003EBio:\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003EGarud Iyengar is the Tang Professor of Operations at Columbia Engineering. He received his B. Tech. in Electrical Engineering from IIT Kanpur, and an MS and PhD in Electrical Engineering from Stanford \u0026nbsp;University. His research interests are broadly in control, machine learning and optimization. His current projects focus on the areas of large-scale power systems and supply chains, causal inference, and modeling of cellular processes. He was elected an INFORMS Fellow in 2018. He was the Chair of the Department of Industrial Engineering and Operations Research from 2013-19, and the Associate Director for Research at the Columbia Data Science Institute from 2017-19. He has been an Amazon Scholar since 2019. He is currently the Senior Vice Dean for Research and Academic Programs at Columbia Engineering.\u003C\/p\u003E\r\n","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003E\u003Cstrong\u003EAbstract:\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003EIn a multi-arm bandit (MAB) setting, in each epoch, the decision maker chooses one arm and observes the reward associated with only that arm, i.e. the rewards of all other arms are missing! Doubly robust (DR) estimation is a well-known technique in the statistics literature on handling missing data. The pseudo-reward samples generated by the DR technique are unbiased provided either the model for the data or the probability that the data is missing is known. In the MAB setting, the probability that the data for an arm is missing is known! Therefore, the DR technique generates unbiased pseudo-rewards for the unselected arms. In each round, the conventional methods for MAB update the estimate for the reward of the chosen arm, and the DR estimator imputes the missing rewards and computes new estimates for all arms in all rounds. Thus, there is a possibility that DR estimates for the rewards of all arms converge uniformly independent of the specific arm selection policy used. This potentially allows us to simultaneously reduce regret and optimize other criteria, e.g. identify the best arm, or the arms on a Pareto front. We show that this is indeed possible in many contexts such as revenue management, Pareto front identification, and sparse linear reinforcement learning, and this leads to improved theoretical guarantees and empirical performance.\u003Cbr \/\u003E\r\n\u0026nbsp;\u003C\/p\u003E\r\n","format":"limited_html"}],"field_summary_sentence":[{"value":"Doubly Robust Methods for Multi Armed Bandits"}],"uid":"36374","created_gmt":"2024-03-08 19:36:02","changed_gmt":"2024-03-08 19:36:02","author":"mwelch39","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2024-03-15T11:30:00-04:00","event_time_end":"2024-03-15T12:30:00-04:00","event_time_end_last":"2024-03-15T12:30:00-04:00","gmt_time_start":"2024-03-15 15:30:00","gmt_time_end":"2024-03-15 16:30:00","gmt_time_end_last":"2024-03-15 16:30:00","rrule":null,"timezone":"America\/New_York"},"location":" ISYE Groseclose 402","extras":[],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[],"invited_audience":[{"id":"174045","name":"Graduate students"},{"id":"177814","name":"Postdoc"},{"id":"78771","name":"Public"},{"id":"78751","name":"Undergraduate students"},{"id":"78761","name":"Faculty\/Staff"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"673202":{"#nid":"673202","#data":{"type":"event","title":"2024 ISyE LeeAnn and Walter Muller Distinguished Scholarship Lecture Series, Dr. Wei Chen","body":[{"value":"\u003Cp\u003EDr. Wei Chen is the Wilson-Cook Professor in Engineering Design and Chair of the Department of Mechanical Engineering at Northwestern University. Directing the Integrated DEsign Automation Laboratory, her current research involves the use of statistical inference, machine learning, and uncertainty quantification techniques for the design of emerging materials systems including microstructural materials, metamaterials, and programmable materials.\u003C\/p\u003E\r\n\r\n\u003Cp\u003EShe serves as the Design Thrust lead for the newly funded NSF Engineering Research Center (ERC) on Hybrid Autonomous Manufacturing, Moving from Evolution to Revolution (HAMMER), where she works on digital twin systems for concurrent materials and manufacturing process design. Dr. Chen is an elected member of the National Academy of Engineering (NAE) and currently serves as the President of the International Society of Structural and Multidisciplinary Design (ISSMO). She served as Editor-in-chief of the ASME Journal of Mechanical Design and the Chair of the ASME Design Engineering Division (DED).\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDr. Chen is the recipient of the 2022 Engineering Science Medal from the Society of Engineering Science (SES), ASME Pi Tau Sigma Charles Russ Richards Memorial Award (2021), ASME Design Automation Award (2015), Intelligent Optimal Design Prize (2005), ASME Pi Tau Sigma Gold Medal achievement award (1998), and the NSF Faculty Career Award (1996). She received her Ph.D. from the Georgia Institute of Technology in 1995.\u003C\/p\u003E\r\n","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EThe 2024 LeeAnn and Walter Muller Distinguished Scholarship Lecture Series will feature Dr. Wei Chen, Wilson-Cook Professor in Engineering Design and Chair of Mechanical Engineering at Northwestern University\u0027s McCormick School of Engineering.\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDr. Chen\u0027s lecture focuses on Latent Variable Gaussian Process (LVGP) for Adaptive, Interpretable, and Multifidelity Design for Emerging Materials and Structures.\u003C\/p\u003E\r\n","format":"limited_html"}],"field_summary_sentence":[{"value":"Latent Variable Gaussian Process (LVGP) for Adaptive, Interpretable, and Multifidelity Design for Emerging Materials and Structures"}],"uid":"36284","created_gmt":"2024-02-26 23:50:31","changed_gmt":"2024-03-04 18:04:49","author":"chenriquez8","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2024-03-28T15:30:00-04:00","event_time_end":"2024-03-28T16:30:00-04:00","event_time_end_last":"2024-03-28T16:30:00-04:00","gmt_time_start":"2024-03-28 19:30:00","gmt_time_end":"2024-03-28 20:30:00","gmt_time_end_last":"2024-03-28 20:30:00","rrule":null,"timezone":"America\/New_York"},"location":"Marcus Nanotechnology Building, Room 1116-1118, 345 Ferst Dr. NW Atlanta, GA, 30318","extras":[],"hg_media":{"673293":{"id":"673293","type":"image","title":"Dr. Wei Chen","body":null,"created":"1709574631","gmt_created":"2024-03-04 17:50:31","changed":"1709574589","gmt_changed":"2024-03-04 17:49:49","alt":"Dr. Wei Chen for ISyE Distinguished Scholarship Lecture Series","file":{"fid":"256663","name":"Wei Chen_DSLS.png","image_path":"\/sites\/default\/files\/2024\/03\/04\/Wei%20Chen_DSLS.png","image_full_path":"http:\/\/hg.gatech.edu\/\/sites\/default\/files\/2024\/03\/04\/Wei%20Chen_DSLS.png","mime":"image\/png","size":2601029,"path_740":"http:\/\/hg.gatech.edu\/sites\/default\/files\/styles\/740xx_scale\/public\/2024\/03\/04\/Wei%20Chen_DSLS.png?itok=AMrNTlJy"}}},"media_ids":["673293"],"related_links":[{"url":"https:\/\/eforms.isye.gatech.edu\/distinguished-lecture-series","title":"RSVP here!"}],"groups":[{"id":"1237","name":"College of Engineering"},{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"174045","name":"Graduate students"},{"id":"177814","name":"Postdoc"},{"id":"78771","name":"Public"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"673163":{"#nid":"673163","#data":{"type":"event","title":"The Black IEs Hosts Dr. Elliott Williams","body":[{"value":"\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003EWe are excited to invite you to the upcoming meeting of the Black IEs, scheduled for \u003Cstrong\u003EMonday, March 4th\u003C\/strong\u003E.\u0026nbsp; This promises to be an insightful and engaging event, and we are thrilled to announce that our esteemed guest speaker for the occasion will be Dr. Elliott Williams.\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003EDr. Williams is a recognized expert in Business Analytics and Industrial Engineering, and we are confident that his expertise will contribute significantly to our discussions.\u0026nbsp; His presentation will provide valuable insights into his journey to BAIE at Universal Destinations and Experiences, along with an overview of business analytics and industrial engineering, and we believe it will be an enriching experience for all attendees. \u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cstrong\u003EAbout Dr. Elliott Williams:\u003C\/strong\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003EDr. Williams is currently a Director of Business Analytics and Industrial Engineering at Universal Destinations \u0026amp; Experiences (UDX). He leads a team of Data Scientists and Revenue Management experts to provide pricing recommendations and solutions for products across UDX parks. \u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003EDuring his career, Elliott has designed and implemented revenue management, supply chain and logistics solutions. Elliott has held pivotal roles in product development, product management, and solution delivery within client services organizations to provide successful solutions for his customers.\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003EDr. Williams has a Bachelor of Science in Electrical Engineering from Massachusetts Institute of Technology and a Ph.D. in Industrial Engineering from the University of Florida.\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003EHere are the details for the meeting:\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cstrong\u003EDate:\u003C\/strong\u003E Monday, March 4th\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cstrong\u003ETime:\u003C\/strong\u003E\u0026nbsp; 6:00pm\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cstrong\u003ELocation:\u003C\/strong\u003E\u0026nbsp; ISyE Main 126\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cstrong\u003E\u003Cem\u003EFood will be provided. \u003C\/em\u003E\u003C\/strong\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003EWe strongly encourage you to \u003Ca href=\u0022https:\/\/gatech.co1.qualtrics.com\/jfe\/form\/SV_dnecd5PrGqNuD3w\u0022\u003ERSVP\u003C\/a\u003E by Wednesday, February 28th to confirm your attendance.\u0026nbsp; \u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EThe Black IEs Invites you to hear guest speaker Dr. Elliott Williams\u003C\/p\u003E\r\n","format":"limited_html"}],"field_summary_sentence":[{"value":"The Black IEs Invites you to hear guest speaker Dr. Elliott Williams"}],"uid":"36475","created_gmt":"2024-02-26 17:37:22","changed_gmt":"2024-02-26 22:24:38","author":"nmyles3","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2024-03-04T18:00:00-05:00","event_time_end":"2024-03-04T19:00:00-05:00","event_time_end_last":"2024-03-04T19:00:00-05:00","gmt_time_start":"2024-03-04 23:00:00","gmt_time_end":"2024-03-05 00:00:00","gmt_time_end_last":"2024-03-05 00:00:00","rrule":null,"timezone":"America\/New_York"},"location":"ISyE Main, 755 Ferst Drive, Atlanta, GA 30332, Room 126","extras":[],"related_links":[{"url":"https:\/\/gatech.co1.qualtrics.com\/jfe\/form\/SV_dnecd5PrGqNuD3w","title":"RSVP here"}],"groups":[{"id":"660354","name":"Center for Academics, Success, and Equity"},{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[{"id":"191751","name":"BlackIE Workshops"},{"id":"193536","name":"Black IEs, Industrial \u0026 Systems Engineering"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"10377","name":"Career\/Professional development"},{"id":"1795","name":"Seminar\/Lecture\/Colloquium"},{"id":"1791","name":"Student sponsored"}],"invited_audience":[{"id":"174045","name":"Graduate students"},{"id":"177814","name":"Postdoc"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[{"value":"\u003Cp\u003EDr. Nicoly Y. Myles\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDirector of the Center for Academics, Success, \u0026amp; Equity (CASE)\u003C\/p\u003E\r\n\r\n\u003Cp\u003Enicoly.myles@gatech.edu\u003C\/p\u003E\r\n","format":"limited_html"}],"email":[],"slides":[],"orientation":[],"userdata":""}},"672956":{"#nid":"672956","#data":{"type":"event","title":"Creating Financial Power for Women: Helping you navigate your personal relationship around money","body":[{"value":"\u003Cp\u003E\u003Ca href=\u0022https:\/\/eforms.isye.gatech.edu\/form\/case-rsvp\u0022\u003ERegister now\u003C\/a\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003EThis talk addresses historical and sociological factors contributing to gender-based imbalance of financial power and offers practical suggestions for how attendees can work within this challenging framework to advance their financial position and their domestic partnerships.\u0026nbsp; This talk is based on a number of academic interviews, research and 24 years as a financial advisor working with women breadwinners.\u0026nbsp; It also addresses top financial planning mistakes made successful women professionals and how to avoid them.\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cem\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003EMeredith\u0027s Bio\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/em\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003EMeredith Moore is the founder of Artisan Financial Strategies, LLC and has been a financial advisor for 24 years with a focus on financial planning for women in senior leadership in corporate America and women founders of small business.\u0026nbsp;\u0026nbsp; She holds a Bachelor of Industrial and Systems Engineering from Georgia Tech, is a 2017 graduate of Leadership Atlanta, a 2021 Atlanta Business Chronicle \u201cWoman of Influence\u201d honoree, and a member of Entrepreneurs Organization and International Women\u2019s Forum.\u0026nbsp; She is also a board member for the Atlanta Women\u2019s Foundation and the Clarkston Community Health Center\u0026nbsp;\u0026nbsp; She has guest lectured at Georgia Tech\u2019s Scheller College of Business and Emory\u2019s Goizueta business school.\u0026nbsp; She has spoken at over a dozen Fortune 500\/100 affinity groups and is a TEDx Women speaker.\u0026nbsp; Her articles on the power dynamics of money have also been published and quoted in publications such as Forbes and US News and World Report.\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003E\u003Cem\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003EThis talk addresses historical and sociological factors contributing to gender-based imbalance of financial power and offers practical suggestions for how attendees can work within this challenging framework to advance their financial position and their domestic partnerships.\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/em\u003E\u003C\/p\u003E\r\n","format":"limited_html"}],"field_summary_sentence":[{"value":"This talk addresses historical and sociological factors contributing to gender-based imbalance of financial power and offers practical suggestions for how attendees can work within this challenging framework to advance their financial position and their d"}],"uid":"36475","created_gmt":"2024-02-14 21:44:55","changed_gmt":"2024-02-26 14:51:29","author":"nmyles3","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2024-03-07T11:00:00-05:00","event_time_end":"2024-03-07T12:00:00-05:00","event_time_end_last":"2024-03-07T12:00:00-05:00","gmt_time_start":"2024-03-07 16:00:00","gmt_time_end":"2024-03-07 17:00:00","gmt_time_end_last":"2024-03-07 17:00:00","rrule":null,"timezone":"America\/New_York"},"location":"Zoom","extras":[],"hg_media":{"673084":{"id":"673084","type":"image","title":"Meredith-Moore.jpeg","body":null,"created":"1707947812","gmt_created":"2024-02-14 21:56:52","changed":"1707947745","gmt_changed":"2024-02-14 21:55:45","alt":"Meredith Moore","file":{"fid":"256438","name":"Meredith-Moore.jpeg","image_path":"\/sites\/default\/files\/2024\/02\/14\/Meredith-Moore.jpeg","image_full_path":"http:\/\/hg.gatech.edu\/\/sites\/default\/files\/2024\/02\/14\/Meredith-Moore.jpeg","mime":"image\/jpeg","size":4801769,"path_740":"http:\/\/hg.gatech.edu\/sites\/default\/files\/styles\/740xx_scale\/public\/2024\/02\/14\/Meredith-Moore.jpeg?itok=ZGnkFZ1c"}}},"media_ids":["673084"],"related_links":[{"url":"https:\/\/eforms.isye.gatech.edu\/form\/case-rsvp","title":"Register now"}],"groups":[{"id":"660354","name":"Center for Academics, Success, and Equity"},{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[{"id":"193511","name":"WISyE "},{"id":"193512","name":"Women in Industrial \u0026 Systems Engineering"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"10377","name":"Career\/Professional development"},{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"174045","name":"Graduate students"},{"id":"78771","name":"Public"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[{"value":"\u003Cp\u003EDr. Nicoly Y. Myles\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDirector of the Center for Academics, Success, \u0026amp; Equity (CASE)\u003C\/p\u003E\r\n\r\n\u003Cp\u003Enicoly.myles@gatech.edu\u003C\/p\u003E\r\n","format":"limited_html"}],"email":[],"slides":[],"orientation":[],"userdata":""}},"672959":{"#nid":"672959","#data":{"type":"event","title":"Let\u0027s Be Strategic:  How strategic volunteering can build your skills and network","body":[{"value":"\u003Cp\u003E\u003Ca href=\u0022https:\/\/eforms.isye.gatech.edu\/form\/case-rsvp\u0022\u003ERegister now\u003C\/a\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003EStrategic Volunteering is a method of aligning volunteer service opportunities with your leadership and skill development plans. By doing so, you can give back to your community while utilizing your current skills, developing new areas of expertise, expanding your network, and gaining recognition and measurable results from your volunteer experiences.\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003E\u003Cem\u003ELunch will be served.\u003C\/em\u003E\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003EHeather\u0027s bio:\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003EHeather Rocker is an award-winning leader with over two decades of experience in business and technology. She has a servant-leader mindset and is passionate about advancing STEM education and diversity, equity, and inclusion (DEI). Heather has led many nonprofit boards and graduated from the Leadership Atlanta Class of 2019. She is the President \u0026amp; CEO of Women in Technology, a nonprofit that empowers women and girls to succeed in STEAM careers. Heather\u0027s accomplishments have earned her several awards and recognitions, including a spot on Atlanta Magazine\u0027s 2024 list of the 500 Most Powerful Leaders in Atlanta, induction into the Georgia\u0026nbsp;Tech College of Engineering\u0027s Council of Outstanding Young Engineers, and the Turknett Leadership Character Award.\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003ELet\u0027s Be Strategic: \u0026nbsp;How strategic volunteering can build your skills and network\u003C\/p\u003E\r\n","format":"limited_html"}],"field_summary_sentence":[{"value":"Let\u0027s Be Strategic:  How strategic volunteering can build your skills and network"}],"uid":"36475","created_gmt":"2024-02-14 22:27:30","changed_gmt":"2024-02-26 14:46:14","author":"nmyles3","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2024-03-12T11:00:00-04:00","event_time_end":"2024-03-12T12:00:00-04:00","event_time_end_last":"2024-03-12T12:00:00-04:00","gmt_time_start":"2024-03-12 15:00:00","gmt_time_end":"2024-03-12 16:00:00","gmt_time_end_last":"2024-03-12 16:00:00","rrule":null,"timezone":"America\/New_York"},"location":"ISyE Main, 755 Ferst Drive, Atlanta, G A30332","extras":[],"hg_media":{"673086":{"id":"673086","type":"image","title":"Heather Rocker","body":"\u003Cp\u003EHeather Rocker\u003C\/p\u003E\r\n","created":"1707948141","gmt_created":"2024-02-14 22:02:21","changed":"1707948139","gmt_changed":"2024-02-14 22:02:19","alt":"Heather Rocker","file":{"fid":"256440","name":"Heather Rocker.jpg","image_path":"\/sites\/default\/files\/2024\/02\/14\/Heather%20Rocker.jpg","image_full_path":"http:\/\/hg.gatech.edu\/\/sites\/default\/files\/2024\/02\/14\/Heather%20Rocker.jpg","mime":"image\/jpeg","size":2185001,"path_740":"http:\/\/hg.gatech.edu\/sites\/default\/files\/styles\/740xx_scale\/public\/2024\/02\/14\/Heather%20Rocker.jpg?itok=DCLGAjD9"}}},"media_ids":["673086"],"related_links":[{"url":"https:\/\/eforms.isye.gatech.edu\/form\/case-rsvp","title":"Register now"}],"groups":[{"id":"660354","name":"Center for Academics, Success, and Equity"},{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[{"id":"193509","name":"WISyE"},{"id":"193512","name":"Women in Industrial \u0026 Systems Engineering"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"10377","name":"Career\/Professional development"},{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"174045","name":"Graduate students"},{"id":"177814","name":"Postdoc"},{"id":"78771","name":"Public"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[{"value":"\u003Cp\u003EDr. Nicoly Y. Myles\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDirector of the Center for Academics, Success, \u0026amp; Equity (CASE)\u003C\/p\u003E\r\n\r\n\u003Cp\u003Enicoly.myles@gatech.edu\u003C\/p\u003E\r\n","format":"limited_html"}],"email":[],"slides":[],"orientation":[],"userdata":""}},"670540":{"#nid":"670540","#data":{"type":"event","title":"SCL Course: Machine Learning Applications for Supply Chain Planning (Virtual\/Instructor-led)","body":[{"value":"\u003Ch3\u003E\u003Cstrong\u003ECourse Description\u003C\/strong\u003E\u003C\/h3\u003E\r\n\r\n\u003Cp\u003EThis course is the third in the four-course Supply Chain Analytics Professional certificate program. It introduces the field of machine learning, an area where algorithms learn patterns from data to support proactive decision making, as it applies to supply chain management. You\u2019ll use machine learning to conduct predictive analytics as you forecast future demand, develop inventory policies, perform customer segmentation and predictive maintenance. You\u2019ll use Python and PowerBI to create and analyze regression, clustering, and classification models.\u003C\/p\u003E\r\n\r\n\u003Cp\u003EThe course is comprised of (4) half-day online instructor-led LIVE group webinars and pre-work (e.g. installing and testing software on your computer, testing connectivity with LMS and meeting software, etc.) to be completed\u0026nbsp;before the first day of the course. An optional pre-course webinar is typically held the Thursday\u0026nbsp;before the course start date (July 6).\u003C\/p\u003E\r\n\r\n\u003Ch3\u003E\u003Cstrong\u003EWho Should Attend\u003C\/strong\u003E\u003C\/h3\u003E\r\n\r\n\u003Cp\u003EExperienced business professionals who perform or want to perform analytics to improve their supply chain management processes. They want to tackle strategic goals and to perform leading edge analytics projects that address the full complexity of supply chains.\u003C\/p\u003E\r\n\r\n\u003Ch3\u003E\u003Cstrong\u003EHow You Will Benefit\u003C\/strong\u003E\u003C\/h3\u003E\r\n\r\n\u003Cul\u003E\r\n\t\u003Cli\u003EUnderstand the role of machine learning (ML) in Supply Chain Management (SCM)\u003C\/li\u003E\r\n\t\u003Cli\u003EApply advanced analytics techniques to build planning tools that can leverage large and real-time data sets\u003C\/li\u003E\r\n\t\u003Cli\u003EApply ML in demand forecasting and predictive maintenance\u003C\/li\u003E\r\n\t\u003Cli\u003EUnderstand how to assess ML model performance, improve models, and pick the best model for a decision\u003C\/li\u003E\r\n\t\u003Cli\u003EUse Python and PowerBI to build, analyze, and deploy ML models\u003C\/li\u003E\r\n\u003C\/ul\u003E\r\n\r\n\u003Ch3\u003E\u003Cstrong\u003EWhat Is Covered\u003C\/strong\u003E\u003C\/h3\u003E\r\n\r\n\u003Cul\u003E\r\n\t\u003Cli\u003EHow ML relates to SCM\u003C\/li\u003E\r\n\t\u003Cli\u003EML algorithms such as regression trees, clustering techniques, decision trees, random forests, logistic regression\u003C\/li\u003E\r\n\t\u003Cli\u003EAspects of ML projects including parameter tuning, cross validation, and assess model performance\u003C\/li\u003E\r\n\t\u003Cli\u003EApplication of ML in demand forecasting for sales and operations planning (S\u0026amp;OP) and inventory management\u003C\/li\u003E\r\n\t\u003Cli\u003EApplication of ML in predictive maintenance\u003C\/li\u003E\r\n\t\u003Cli\u003EHands-on practice with these skills using data from the (fictional) Cardboard Company (CBC)\u003C\/li\u003E\r\n\u003C\/ul\u003E\r\n","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EAn introduction to\u0026nbsp;the field of machine learning as it applies to supply chain management. You\u2019ll then use machine learning to conduct predictive analytics as you forecast future demand, develop inventory policies, perform customer segmentation and predictive maintenance.\u003C\/p\u003E\r\n","format":"limited_html"}],"field_summary_sentence":[{"value":"Learn to use machine learning to conduct predictive analytics as you forecast future demand, develop inventory policies, perform customer segmentation and predictive maintenance."}],"uid":"27233","created_gmt":"2023-10-20 14:40:20","changed_gmt":"2024-02-16 13:17:59","author":"Andy Haleblian","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2024-09-16T13:00:00-04:00","event_time_end":"2024-09-19T17:00:00-04:00","event_time_end_last":"2024-09-19T17:00:00-04:00","gmt_time_start":"2024-09-16 17:00:00","gmt_time_end":"2024-09-19 21:00:00","gmt_time_end_last":"2024-09-19 21:00:00","rrule":null,"timezone":"America\/New_York"},"location":"Online\/Virtually-led","extras":[],"related_links":[{"url":"https:\/\/www.scl.gatech.edu\/education\/professional-education\/course\/scaml","title":"Course webpage within the SCL website"}],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"},{"id":"1243","name":"The Supply Chain and Logistics Institute (SCL)"}],"categories":[],"keywords":[{"id":"7251","name":"analytics"},{"id":"167074","name":"Supply Chain"},{"id":"122741","name":"physical internet"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"10377","name":"Career\/Professional development"}],"invited_audience":[{"id":"78771","name":"Public"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[{"value":"\u003Cp\u003E\u003Ca href=\u0022mailto:info@scl.gatech.edu\u0022\u003Einfo@scl.gatech.edu\u003C\/a\u003E\u003C\/p\u003E\r\n","format":"limited_html"}],"email":[],"slides":[],"orientation":[],"userdata":""}},"670537":{"#nid":"670537","#data":{"type":"event","title":"SCL Course: Creating Business Value with Statistical Analysis (Virtual\/Instructor-led)","body":[{"value":"\u003Ch3\u003E\u003Cstrong\u003ECourse Description\u003C\/strong\u003E\u003C\/h3\u003E\r\n\r\n\u003Cp\u003EThis course is the second in the four-course Supply Chain Analytics Professional certificate program. It emphasizes operational performance metrics to align supply chain management with strategic business goals. You\u2019ll learn several statistics concepts (e.g. variance analysis, hypothesis testing, forecasting methods) along with inventory management models. You\u2019ll use diagnostic analytics with PowerBI and Python to conduct demand and service profiling, undertake root cause analysis, and use time series forecasting in inventory management.\u003C\/p\u003E\r\n\r\n\u003Cp\u003EThe online version of the course is comprised of (4) half-day online instructor-led LIVE group webinars and pre-work (e.g. installing and testing software on your computer, testing connectivity with LMS and meeting software, etc.) to be completed before the first day of the course.\u003C\/p\u003E\r\n\r\n\u003Ch3\u003E\u003Cstrong\u003EWho Should Attend\u003C\/strong\u003E\u003C\/h3\u003E\r\n\r\n\u003Cp\u003EExperienced business professionals who perform or want to perform analytics to improve their supply chain management processes. They want to tackle strategic goals and to perform leading edge analytics projects that address the full complexity of supply chains.\u003C\/p\u003E\r\n\r\n\u003Ch3\u003E\u003Cstrong\u003EHow You Will Benefit\u003C\/strong\u003E\u003C\/h3\u003E\r\n\r\n\u003Cul\u003E\r\n\t\u003Cli\u003EUnderstand why and how to align Supply Chain Management (SCM) strategy with business strategy\u003C\/li\u003E\r\n\t\u003Cli\u003ELearn statistics techniques as they relate to SCM\u003C\/li\u003E\r\n\t\u003Cli\u003EUnderstand inventory management models and how to apply statistics techniques to them\u003C\/li\u003E\r\n\t\u003Cli\u003ECreate time series forecasts based on SCM data\u003C\/li\u003E\r\n\t\u003Cli\u003EUtilize Python and PowerBI to perform statistical analyses, create time series forecasts and visualize results\u003C\/li\u003E\r\n\u003C\/ul\u003E\r\n\r\n\u003Ch3\u003E\u003Cstrong\u003EWhat Is Covered\u003C\/strong\u003E\u003C\/h3\u003E\r\n\r\n\u003Cul\u003E\r\n\t\u003Cli\u003EThe importance of aligning SCM and business strategy\u003C\/li\u003E\r\n\t\u003Cli\u003EHow to ask the right business questions as they relate to SCM\u003C\/li\u003E\r\n\t\u003Cli\u003EHow to use statistics to identify issues, compare data, and forecast decision outcomes\u003C\/li\u003E\r\n\t\u003Cli\u003EStatistical\u0026nbsp;concepts including variance analysis and hypothesis testing\u003C\/li\u003E\r\n\t\u003Cli\u003EInventory management models\u003C\/li\u003E\r\n\t\u003Cli\u003EApplying statistics to inventory management models\u003C\/li\u003E\r\n\t\u003Cli\u003EForecasting techniques including time series forecasting\u003C\/li\u003E\r\n\t\u003Cli\u003EHands-on practice with these skills using data from the (fictional) Cardboard Company (CBC)\u003C\/li\u003E\r\n\u003C\/ul\u003E\r\n","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003ELearn statistics concepts (e.g. variance analysis, hypothesis testing, forecasting methods) and inventory management models to improve operational performance metrics and align supply chain management with strategic business goals.\u003C\/p\u003E\r\n","format":"limited_html"}],"field_summary_sentence":[{"value":"Learn statistics concepts (e.g. variance analysis, hypothesis testing, forecasting methods) and inventory management models."}],"uid":"27233","created_gmt":"2023-10-20 14:03:32","changed_gmt":"2024-02-16 13:13:19","author":"Andy Haleblian","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2024-05-13T13:00:00-04:00","event_time_end":"2024-05-16T17:00:00-04:00","event_time_end_last":"2024-05-16T17:00:00-04:00","gmt_time_start":"2024-05-13 17:00:00","gmt_time_end":"2024-05-16 21:00:00","gmt_time_end_last":"2024-05-16 21:00:00","rrule":null,"timezone":"America\/New_York"},"location":"Online","extras":[],"related_links":[{"url":"https:\/\/www.scl.gatech.edu\/education\/professional-education\/course\/scabv","title":"Course detail within the SCL website"}],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"},{"id":"1243","name":"The Supply Chain and Logistics Institute (SCL)"}],"categories":[],"keywords":[{"id":"7251","name":"analytics"},{"id":"167074","name":"Supply Chain"},{"id":"122741","name":"physical internet"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"10377","name":"Career\/Professional development"}],"invited_audience":[{"id":"78771","name":"Public"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[{"value":"\u003Cp\u003E\u003Ca href=\u0022mailto:info@scl.gatech.edu\u0022\u003Einfo@scl.gatech.edu\u003C\/a\u003E\u003C\/p\u003E\r\n","format":"limited_html"}],"email":[],"slides":[],"orientation":[],"userdata":""}},"672962":{"#nid":"672962","#data":{"type":"event","title":"ISyE Seminar Speaker - Dr. Juan Wachs","body":[{"value":"\u003Cp\u003E\u003Cstrong\u003ETitle:\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003EGore Robots: From Blood and Guts to Bits and Bytes\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003EAbstract:\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003ERobots can already solve sophisticated problems ranging from playing games, autonomous driving, and dancing\u2014given enough observational of data for training. The core of such success resides in efficient algorithms, compliant hardware and robust computing, all implemented using carefully curated data collected before the training phase. Thus, robots learn in a \u201csterile\u201d domain, under clean, controlled and to some extent supervised environments. As the target domain changes, however, moving to more quotidian scenarios, robots struggle to perform well. It is hard to think of an autonomous car trained in Silicon Valley being able to successfully navigate the crowded streets of New Delhi. Ideally, we would like to see human and robot hybrid teams that can learn while immersed in a non-sterile setting, where one takes the lead where it excels and ask for help when in need. To address this hurdle, my work in the area of robotics and autonomous systems focuses on transferring skills and knowledge from controlled settings to the wild. In this talk, I emphasize strategies and techniques to address fundamental challenges in emergency medicine. Specifically, I will discuss work related to surgical assistants, telesurgery, and skill augmentation. While medicine is the main domain of the research discussed, the outcomes and findings are applicable to the range of field robotics. Progress in these directions will contribute to the public purpose of creating the knowledge for developing robots that are more accessible, effective and sensitive to social needs.\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003EBio:\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDr. Juan Wachs is a Professor and University Faculty Scholar in the Industrial Engineering School at Purdue University, Professor of Biomedical Engineering (by courtesy), an Adjunct Associate Professor of Surgery at IU School of Medicine, and Adjunct Professor at Johns Hopkins University. He is currently serving at NSF as a Program Director for robotics and AI programs at CISE. He is also the director of the Intelligent Systems and Assistive Technologies (ISAT) Lab at Purdue, and he is affiliated with the Regenstrief Center for Healthcare Engineering. He completed postdoctoral training at the Naval Postgraduate School\u2019s MOVES Institute under a National Research Council Fellowship from the National Academies of Sciences. Dr. Wachs received his B.Ed.Tech in Electrical Education in ORT Academic College, at the Hebrew University of Jerusalem campus. His M.Sc and Ph.D in Industrial Engineering and Management from the Ben-Gurion University of the Negev, Israel. He is the recipient of the 2013 Air Force Young Investigator Award, and the 2015 Helmsley Senior Scientist Fellow, and 2016 Fulbright U.S. Scholar, the James A. and Sharon M. Tompkins Rising Star Associate Professor, 2017, and the ACM Distinguished Speaker 2018. Since 2020 he has been elected University Faculty Scholar. He is also the Associate Editor of IEEE Transactions in Human-Machine Systems, Frontiers in Robotics and AI.\u003C\/p\u003E\r\n","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EAbstract:\u003C\/p\u003E\r\n\r\n\u003Cp\u003ERobots can already solve sophisticated problems ranging from playing games, autonomous driving, and dancing\u2014given enough observational of data for training. The core of such success resides in efficient algorithms, compliant hardware and robust computing, all implemented using carefully curated data collected before the training phase. Thus, robots learn in a \u201csterile\u201d domain, under clean, controlled and to some extent supervised environments. As the target domain changes, however, moving to more quotidian scenarios, robots struggle to perform well. It is hard to think of an autonomous car trained in Silicon Valley being able to successfully navigate the crowded streets of New Delhi. Ideally, we would like to see human and robot hybrid teams that can learn while immersed in a non-sterile setting, where one takes the lead where it excels and ask for help when in need. To address this hurdle, my work in the area of robotics and autonomous systems focuses on transferring skills and knowledge from controlled settings to the wild. In this talk, I emphasize strategies and techniques to address fundamental challenges in emergency medicine. Specifically, I will discuss work related to surgical assistants, telesurgery, and skill augmentation. While medicine is the main domain of the research discussed, the outcomes and findings are applicable to the range of field robotics. Progress in these directions will contribute to the public purpose of creating the knowledge for developing robots that are more accessible, effective and sensitive to social needs.\u003Cbr \/\u003E\r\n\u0026nbsp;\u003C\/p\u003E\r\n","format":"limited_html"}],"field_summary_sentence":[{"value":"Gore Robots: From Blood and Guts to Bits and Bytes"}],"uid":"36374","created_gmt":"2024-02-15 13:37:40","changed_gmt":"2024-02-15 13:37:39","author":"mwelch39","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2024-03-01T11:30:00-05:00","event_time_end":"2024-03-01T12:30:00-05:00","event_time_end_last":"2024-03-01T12:30:00-05:00","gmt_time_start":"2024-03-01 16:30:00","gmt_time_end":"2024-03-01 17:30:00","gmt_time_end_last":"2024-03-01 17:30:00","rrule":null,"timezone":"America\/New_York"},"location":" ISyE Groseclose 402","extras":[],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[],"invited_audience":[{"id":"174045","name":"Graduate students"},{"id":"177814","name":"Postdoc"},{"id":"78771","name":"Public"},{"id":"78751","name":"Undergraduate students"},{"id":"78761","name":"Faculty\/Staff"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"672878":{"#nid":"672878","#data":{"type":"event","title":"ISyE Seminar Speaker - Jose Blanchet","body":[{"value":"\u003Cp\u003E\u003Cstrong\u003ETitle:\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003EOn the Foundations of Distributionally Robust Reinforcement Learning\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003EAbstract:\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003EMotivated by the need for a robust policy in the face of environment shifts between training and the deployment, we contribute to the theoretical foundation of distributionally robust reinforcement learning (DRRL). This is accomplished through a comprehensive modeling framework centered around distributionally robust Markov decision processes (DRMDPs). This framework obliges the decision maker to choose an optimal policy under the worst-case distributional shift orchestrated by an adversary. By unifying and extending existing formulations, we construct DRMDPs that embraces various modeling attributes for both the decision maker and the adversary. These attributes include adaptability granularity, exploring history-dependent, Markov, and Markov time-homogeneous decision maker and adversary dynamics. Additionally, we delve into the flexibility of shifts induced by the adversary, examining so-called SA and S-rectangularity. We investigate conditions for the existence or absence of the dynamic programming principle (DPP). From an algorithmic standpoint, the existence of DPP holds significant implications, as the vast majority of existing data and computationally efficiency RL algorithms are reliant on the DPP. We also offer counterexamples for settings in which a DPP with full generality is absent.\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003E(Joint work with Shengbo Wang, Nian Si, Zhengyuan Zhou).\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003EBio:\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003EJose Blanchet is a Professor of Management Science and Engineering (MS\u0026amp;E) at Stanford. Prior to joining MS\u0026amp;E, he was a professor at Columbia (Industrial Engineering and Operations Research, and Statistics, 2008-2017), and before that he taught at Harvard (Statistics, 2004-2008). Jose is a recipient of the 2010 Erlang Prize and several best publication awards in areas such as applied probability, simulation, operations management, and revenue management. He also received a Presidential Early Career Award for Scientists and Engineers in 2010. He worked as an analyst in Protego Financial Advisors, a leading investment bank in Mexico. He has research interests in applied probability and Monte Carlo methods. He is the Area Editor of Stochastic Models in Mathematics of Operations Research. He has served on the editorial board of Advances in Applied Probability, Bernoulli, Extremes, Insurance: Mathematics and Economics, Journal of Applied Probability, Queueing Systems: Theory and Applications, and Stochastic Systems, among others.\u003Cbr \/\u003E\r\n\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003EAbstract:\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003EMotivated by the need for a robust policy in the face of environment shifts between training and the deployment, we contribute to the theoretical foundation of distributionally robust reinforcement learning (DRRL). This is accomplished through a comprehensive modeling framework centered around distributionally robust Markov decision processes (DRMDPs). This framework obliges the decision maker to choose an optimal policy under the worst-case distributional shift orchestrated by an adversary. By unifying and extending existing formulations, we construct DRMDPs that embraces various modeling attributes for both the decision maker and the adversary. These attributes include adaptability granularity, exploring history-dependent, Markov, and Markov time-homogeneous decision maker and adversary dynamics. Additionally, we delve into the flexibility of shifts induced by the adversary, examining so-called SA and S-rectangularity. We investigate conditions for the existence or absence of the dynamic programming principle (DPP). From an algorithmic standpoint, the existence of DPP holds significant implications, as the vast majority of existing data and computationally efficiency RL algorithms are reliant on the DPP. We also offer counterexamples for settings in which a DPP with full generality is absent.\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003E(Joint work with Shengbo Wang, Nian Si, Zhengyuan Zhou).\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n","format":"limited_html"}],"field_summary_sentence":[{"value":"On the Foundations of Distributionally Robust Reinforcement Learning "}],"uid":"36374","created_gmt":"2024-02-12 15:50:29","changed_gmt":"2024-02-12 15:50:57","author":"mwelch39","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2024-02-23T11:30:00-05:00","event_time_end":"2024-02-23T12:30:00-05:00","event_time_end_last":"2024-02-23T12:30:00-05:00","gmt_time_start":"2024-02-23 16:30:00","gmt_time_end":"2024-02-23 17:30:00","gmt_time_end_last":"2024-02-23 17:30:00","rrule":null,"timezone":"America\/New_York"},"location":" ISyE Groseclose 402","extras":[],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"177814","name":"Postdoc"},{"id":"78771","name":"Public"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"672395":{"#nid":"672395","#data":{"type":"event","title":"ISyE Seminar - Jonathan Stallrich","body":[{"value":"\u003Ch3\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cstrong\u003E\u003Cspan\u003E\u003Cspan\u003ETitle: \u003C\/span\u003E\u003C\/span\u003E\u003C\/strong\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/h3\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003EStatistical Methods for $mall Data Problems\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cstrong\u003E\u003Cspan\u003E\u003Cspan\u003EAbstract: \u003C\/span\u003E\u003C\/span\u003E\u003C\/strong\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003EWith data collection, storage, and management becoming faster and cheaper, statisticians and data scientists have developed important statistical and computational methods for Big Data problems. However, there are still many significant problems that involve small quantities of data that are \u201cbig\u201d in terms of monetary or temporal cost. These $mall (small) data problems are best solved with careful planning of both the data collection and analysis methods. In this talk, I will discuss some examples of these problems and new statistical methods to tackle them. The talk will primarily focus on new screening experiments that jointly target efficient estimation of both the main effects of the manipulated factors and the process variance. I will then survey new ideas for problems that have received less attention in the literature, including optimal designs for penalized estimation, screening designs for generalized linear models, and design and analysis for screening under functional linear models.\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cstrong\u003E\u003Cspan\u003E\u003Cspan\u003EBio:\u003C\/span\u003E\u003C\/span\u003E\u003C\/strong\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003EJon Stallrich is an Associate Professor in the Department of Statistics at NC State University. He earned his Ph.D. in Statistics from Virginia Tech in 2014. \u003Cspan\u003EHe has focused his career on innovating and teaching practical statistical methods under the tenet that data collection and analysis procedures should be jointly determined to make efficient statistical conclusions. \u003C\/span\u003EJon\u2019s research interests include design and analysis of screening experiments, computer experiments, functional data analysis, and variable selection. \u003Cspan\u003EHis methodological research is often motivated by interdisciplinary collaborations with researchers from industrial and systems engineering, biomedical engineering, material science, toxicology, and computer science.\u003C\/span\u003E In 2021, he and his coauthors were awarded the ASA\u2019s Statistics in Physical Engineering Sciences Award for the paper, \u201cOptimal EMG placement for a robotic prosthesis controller with sequential, adaptive functional estimation.\u201d He is currently serving as Chair of the ASA\u2019s Section on Physical and Engineering Sciences and General Conference Chair of the 2024 Fall Technical Conference to be held in Nashville, TN.\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Ch3\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cstrong\u003E\u003Cspan\u003E\u003Cspan\u003EAbstract: \u003C\/span\u003E\u003C\/span\u003E\u003C\/strong\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/h3\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003EWith data collection, storage, and management becoming faster and cheaper, statisticians and data scientists have developed important statistical and computational methods for Big Data problems. However, there are still many significant problems that involve small quantities of data that are \u201cbig\u201d in terms of monetary or temporal cost. These $mall (small) data problems are best solved with careful planning of both the data collection and analysis methods. In this talk, I will discuss some examples of these problems and new statistical methods to tackle them. The talk will primarily focus on new screening experiments that jointly target efficient estimation of both the main effects of the manipulated factors and the process variance. I will then survey new ideas for problems that have received less attention in the literature, including optimal designs for penalized estimation, screening designs for generalized linear models, and design and analysis for screening under functional linear models.\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n","format":"limited_html"}],"field_summary_sentence":[{"value":"Statistical Methods for $mall Data Problems"}],"uid":"34977","created_gmt":"2024-01-23 19:56:20","changed_gmt":"2024-01-23 19:56:20","author":"Julie Smith","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2024-02-06T11:00:00-05:00","event_time_end":"2024-02-06T12:00:00-05:00","event_time_end_last":"2024-02-06T12:00:00-05:00","gmt_time_start":"2024-02-06 16:00:00","gmt_time_end":"2024-02-06 17:00:00","gmt_time_end_last":"2024-02-06 17:00:00","rrule":null,"timezone":"America\/New_York"},"location":"ISyE Groseclose 402","extras":[],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"177814","name":"Postdoc"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"672104":{"#nid":"672104","#data":{"type":"event","title":"ISyE Seminar - Ying Jin","body":[{"value":"\u003Ch3\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cstrong\u003E\u003Cspan\u003E\u003Cspan\u003ETitle:\u0026nbsp;\u003C\/span\u003E\u003C\/span\u003E\u003C\/strong\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/h3\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003EModel-free selective inference: from calibrated uncertainty to trusted decisions\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Ch3\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cstrong\u003E\u003Cspan\u003E\u003Cspan\u003EAbstract:\u003C\/span\u003E\u003C\/span\u003E\u003C\/strong\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/h3\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003EAI has shown great potential in accelerating decision-making and scientific discovery pipelines such as drug discovery, marketing, and healthcare. In many applications, predictions from black-box models are used to shortlist candidates whose unknown outcomes satisfy a desired property, e.g., drugs with high binding affinities to a disease target. To ensure the reliability of high-stakes decisions, uncertainty quantification tools such as conformal prediction have been increasingly adopted to understand the variability in black-box predictions. However, we find that the on-average guarantee of conformal prediction can be insufficient for its deployment in decision making which usually has a selective nature.\u0026nbsp;\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003EIn this talk, I will introduce a model-free selective inference framework that allows to select reliable decisions with the assistance of any black box prediction model. Our framework identifies candidates whose unobserved outcomes exceed user-specified values while controlling the average proportion of falsely selected units (FDR), without any modeling assumptions. Given a set of exchangeable training data, our method constructs conformal p-values that quantify the confidence in large outcomes; it then determines a data-dependent threshold for the p-values as a criterion for drawing confident decisions. In addition, I will discuss new ideas to further deal with covariate shifts between training and new samples. We show that in several drug discovery tasks, our methods narrow down the drug candidates to a manageable size of promising ones while controlling the proportion of falsely discovered. In a causal inference dataset, our methods identify students who benefit from an educational intervention, providing new insights for causal effects.\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Ch3\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cstrong\u003E\u003Cspan\u003E\u003Cspan\u003EBio:\u003C\/span\u003E\u003C\/span\u003E\u003C\/strong\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/h3\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003EYing Jin is a fifth-year PhD student at Department of Statistics, Stanford University, advised by Emmanuel Cand\u00e8s and Dominik Rothenh\u00e4usler. Prior to this, she obtained B.S. in Mathematics from Tsinghua University. Her research focuses on devising modern statistical methodology that enables trusted inference and decisions with minimal assumptions, as well as its deployment in real applications, covering conformal inference, multiple testing, causal inference, distribution robustness, and data-driven decision-making.\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Ch3\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cstrong\u003E\u003Cspan\u003E\u003Cspan\u003EAbstract:\u003C\/span\u003E\u003C\/span\u003E\u003C\/strong\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/h3\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003EAI has shown great potential in accelerating decision-making and scientific discovery pipelines such as drug discovery, marketing, and healthcare. In many applications, predictions from black-box models are used to shortlist candidates whose unknown outcomes satisfy a desired property, e.g., drugs with high binding affinities to a disease target. To ensure the reliability of high-stakes decisions, uncertainty quantification tools such as conformal prediction have been increasingly adopted to understand the variability in black-box predictions. However, we find that the on-average guarantee of conformal prediction can be insufficient for its deployment in decision making which usually has a selective nature.\u0026nbsp;\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003EIn this talk, I will introduce a model-free selective inference framework that allows to select reliable decisions with the assistance of any black box prediction model. Our framework identifies candidates whose unobserved outcomes exceed user-specified values while controlling the average proportion of falsely selected units (FDR), without any modeling assumptions. Given a set of exchangeable training data, our method constructs conformal p-values that quantify the confidence in large outcomes; it then determines a data-dependent threshold for the p-values as a criterion for drawing confident decisions. In addition, I will discuss new ideas to further deal with covariate shifts between training and new samples. We show that in several drug discovery tasks, our methods narrow down the drug candidates to a manageable size of promising ones while controlling the proportion of falsely discovered. In a causal inference dataset, our methods identify students who benefit from an educational intervention, providing new insights for causal effects.\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n","format":"limited_html"}],"field_summary_sentence":[{"value":"Model-free selective inference: from calibrated uncertainty to trusted decisions"}],"uid":"34977","created_gmt":"2024-01-16 14:02:28","changed_gmt":"2024-01-16 14:02:28","author":"Julie Smith","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2024-02-08T11:00:00-05:00","event_time_end":"2024-02-08T12:00:00-05:00","event_time_end_last":"2024-02-08T12:00:00-05:00","gmt_time_start":"2024-02-08 16:00:00","gmt_time_end":"2024-02-08 17:00:00","gmt_time_end_last":"2024-02-08 17:00:00","rrule":null,"timezone":"America\/New_York"},"location":"ISyE Groseclose 402","extras":[],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"177814","name":"Postdoc"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"670992":{"#nid":"670992","#data":{"type":"event","title":"SCL January 2024 Supply Chain Day","body":[{"value":"\u003Cp\u003EGeorgia Tech Supply Chain students and employers, please join us for our first fall Supply Chain Day!\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Ch3\u003E\u003Cstrong\u003EEvent Details\u003C\/strong\u003E\u003C\/h3\u003E\r\n\r\n\u003Ch4\u003EOn Campus\/In-Person (Georgia Tech Exhibition Hall)\u003C\/h4\u003E\r\n\r\n\u003Cul\u003E\r\n\t\u003Cli\u003E\u003Cstrong\u003EWednesday, January 24, 2024 | 11am - 2pm ET\u003C\/strong\u003E\u003C\/li\u003E\r\n\u003C\/ul\u003E\r\n\r\n\u003Ch3\u003EStudents\u003C\/h3\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003EWe strongly encourage you to attend to seek full-time employment\u003C\/strong\u003E, \u003Cstrong\u003Einternships, and projects\u003C\/strong\u003E (rather than waiting until the end of the semester).\u003C\/p\u003E\r\n\r\n\u003Ch3\u003EOrganizations\u003C\/h3\u003E\r\n\r\n\u003Cp\u003EIf you are interested in hosting a table for the upcoming session, please let us know after reviewing the below information within our website.\u003C\/p\u003E\r\n\r\n\u003Ch4\u003EMORE INFORMATION AND EVENT REGISTRATION\u003C\/h4\u003E\r\n\r\n\u003Cp\u003EVisit\u0026nbsp;\u003Cstrong\u003E\u003Ca href=\u0022https:\/\/www.scl.gatech.edu\/outreach\/supplychainday\u0022\u003Ehttps:\/\/www.scl.gatech.edu\/outreach\/supplychainday\u003C\/a\u003E\u003C\/strong\u003E.\u003C\/p\u003E\r\n","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EGeorgia Tech Supply Chain\u0026nbsp;students and employers, please join us for our spring Supply Chain Day! We will be hosting an On Campus session\u0026nbsp;Wednesday, January 24, 2024 from 11am-2pm ET at the Georgia Tech Exhibition Hall.\u003C\/p\u003E\r\n","format":"limited_html"}],"field_summary_sentence":[{"value":"Supply chain and logistics career fair where industry supply chain representatives meet Georgia Tech students."}],"uid":"27233","created_gmt":"2023-11-09 20:11:22","changed_gmt":"2024-01-09 20:48:18","author":"Andy Haleblian","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2024-01-24T11:00:00-05:00","event_time_end":"2024-01-24T14:00:00-05:00","event_time_end_last":"2024-01-24T14:00:00-05:00","gmt_time_start":"2024-01-24 16:00:00","gmt_time_end":"2024-01-24 19:00:00","gmt_time_end_last":"2024-01-24 19:00:00","rrule":null,"timezone":"America\/New_York"},"location":"Georgia Tech Exhibition Hall","extras":[],"hg_media":{"672334":{"id":"672334","type":"image","title":"SCL January 2024 Supply Chain Day","body":null,"created":"1699557302","gmt_created":"2023-11-09 19:15:02","changed":"1699557302","gmt_changed":"2023-11-09 19:15:02","alt":"January 24, 2024 Supply Chain Day banner","file":{"fid":"255559","name":"Supply Chain Day Supply Chain and Logistics Career Fair.png","image_path":"\/sites\/default\/files\/2023\/11\/09\/Supply%20Chain%20Day%20Supply%20Chain%20and%20Logistics%20Career%20Fair.png","image_full_path":"http:\/\/hg.gatech.edu\/\/sites\/default\/files\/2023\/11\/09\/Supply%20Chain%20Day%20Supply%20Chain%20and%20Logistics%20Career%20Fair.png","mime":"image\/png","size":1037693,"path_740":"http:\/\/hg.gatech.edu\/sites\/default\/files\/styles\/740xx_scale\/public\/2023\/11\/09\/Supply%20Chain%20Day%20Supply%20Chain%20and%20Logistics%20Career%20Fair.png?itok=cUwvpUm1"}}},"media_ids":["672334"],"related_links":[{"url":"https:\/\/www.scl.gatech.edu\/outreach\/supplychainday","title":"Register online to attend (for Georgia Tech students)"},{"url":"https:\/\/www.scl.gatech.edu","title":"Supply Chain and Logistics Institute website"}],"groups":[{"id":"1250","name":"Center for Health and Humanitarian Systems (CHHS)"},{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"},{"id":"1243","name":"The Supply Chain and Logistics Institute (SCL)"}],"categories":[],"keywords":[{"id":"780","name":"employment"},{"id":"9845","name":"GTSCL"},{"id":"233","name":"Logistics"},{"id":"167074","name":"Supply Chain"},{"id":"1996","name":"Recruiting"},{"id":"5172","name":"career day"},{"id":"122741","name":"physical internet"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"10377","name":"Career\/Professional development"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"177814","name":"Postdoc"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[{"value":"\u003Cp\u003Eevent@scl.gatech.edu\u003C\/p\u003E\r\n","format":"limited_html"}],"email":[],"slides":[],"orientation":[],"userdata":""}},"671900":{"#nid":"671900","#data":{"type":"event","title":"ISyE Seminar - Brandon Pitts ","body":[{"value":"\u003Ch3\u003E\u003Cstrong\u003ETitle:\u003C\/strong\u003E\u003C\/h3\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003ETechnology \u2026 Here, There, and Everywhere:\u0026nbsp;\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003EThe Need to Understand Human Interactions with Emerging Technologies \u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Ch3\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cstrong\u003EAbstract: \u003C\/strong\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/h3\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003EEmerging technologies are being developed at an unprecedented rate and are altering human behavior in new ways. This trend is expected to continue for the foreseeable future as society seeks to leverage the capabilities of machines and artificial intelligence (AI) to their fullest extent. Intelligent technologies offer many potential benefits, but also come with unique challenges. For example, some systems require shared responsibilities, occasional but unpredictable human intervention, and rapid real-time human decision-making. In addition, many technologies are being used by a diverse user demographic with varying levels of experience with technology, expectations, and abilities. In this presentation, Dr. Pitts will share insights from a series of research projects in his lab aimed at evaluating various pervasive technologies in transportation, work, and leisure environments and for different types of users. Findings from his research are contributing to the critical discussion on how to (re)design interfaces to support effective human-technology collaborations across many broad applications. This work is also helping to inform theories on human perception and performance, promote safety and efficiency in complex environments, and shape policies on universal design and accessibility.\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Ch3\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cstrong\u003EBio:\u003C\/strong\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/h3\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003EDr. Brandon Pitts is an Assistant Professor in the School of Industrial Engineering and a Faculty Associate with the Center on Aging and the Life Course (CALC) at Purdue University in West Lafayette, IN. He received his Ph.D. in Industrial and Operations Engineering from the University of Michigan (UM), Ann Arbor in 2016. Prior to his faculty appointment, he was a Research Fellow in the UM Center for Healthcare Engineering and Patient Safety (CHEPS). Dr. Pitts\u2019 research areas include human factors engineering, human-automation\/AI interaction, cyber-human-physical systems, interface design, and gerontechnology in complex transportation and work environments, i.e., driving and aviation. His research has been funded by several sponsors, such as the National Science Foundation (NSF), Department of Transportation (DOT), Federal Aviation Administration (FAA), National Institute on Disability, Independent Living, and Rehabilitation Research (NIDILRR), and Ford Motor Company. Dr. Pitts has also received several honors and recognitions, including the 2023 Stephanie Binder Young Professional Award, a 2023 NSF CAREER award, and 1st place in the 2022 U.S. DOT Inclusive Design Challenge (IDC) for his team\u2019s EASI RIDER autonomous vehicle innovation. \u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Ch3\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cstrong\u003EAbstract: \u003C\/strong\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/h3\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003EEmerging technologies are being developed at an unprecedented rate and are altering human behavior in new ways. This trend is expected to continue for the foreseeable future as society seeks to leverage the capabilities of machines and artificial intelligence (AI) to their fullest extent. Intelligent technologies offer many potential benefits, but also come with unique challenges. For example, some systems require shared responsibilities, occasional but unpredictable human intervention, and rapid real-time human decision-making. In addition, many technologies are being used by a diverse user demographic with varying levels of experience with technology, expectations, and abilities. In this presentation, Dr. Pitts will share insights from a series of research projects in his lab aimed at evaluating various pervasive technologies in transportation, work, and leisure environments and for different types of users. Findings from his research are contributing to the critical discussion on how to (re)design interfaces to support effective human-technology collaborations across many broad applications. This work is also helping to inform theories on human perception and performance, promote safety and efficiency in complex environments, and shape policies on universal design and accessibility.\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n","format":"limited_html"}],"field_summary_sentence":[{"value":"Technology \u2026 Here, There, and Everywhere:  The Need to Understand Human Interactions with Emerging Technologies "}],"uid":"34977","created_gmt":"2024-01-05 17:24:26","changed_gmt":"2024-01-05 17:24:26","author":"Julie Smith","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2024-01-23T11:00:00-05:00","event_time_end":"2024-01-23T12:00:00-05:00","event_time_end_last":"2024-01-23T12:00:00-05:00","gmt_time_start":"2024-01-23 16:00:00","gmt_time_end":"2024-01-23 17:00:00","gmt_time_end_last":"2024-01-23 17:00:00","rrule":null,"timezone":"America\/New_York"},"location":"ISyE Groseclose 402","extras":[],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"177814","name":"Postdoc"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"671862":{"#nid":"671862","#data":{"type":"event","title":"ISyE Seminar - Bento Natura ","body":[{"value":"\u003Ch3\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cstrong\u003ETitle:\u003C\/strong\u003E\u003C\/span\u003E\u003C\/span\u003E\u003Cspan\u003E\u003Cspan\u003E\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003C\/span\u003E\u003C\/span\u003E\u003C\/h3\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003ERecent Advances in Strongly Polynomial Algorithms for Linear Programming\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Ch3\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cstrong\u003EAbstract:\u003C\/strong\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/h3\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003EWhereas ellipsoid methods and interior point methods provide polynomial-time linear programming algorithms, the running time bounds depend on bit-complexity or condition measures that can be unbounded in the problem dimension. This is in contrast with the simplex method that always admits an exponential bound.\u0026nbsp;\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003EAn important unresolved question in operations research, theoretical computer science, and related fields, concerns the existence of a strongly polynomial algorithm for linear programming. Such an algorithm\u0027s running time would solely depend on the problem\u0027s dimension and the number of constraints, independent of any additional condition numbers. This question, first articulated by Megiddo in the 1980s, has gained prominence as Smale\u0027s 9th problem.\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003EIn the first part of our talk, we introduce a new polynomial-time path-following interior point method where the number of iterations admits a combinatorial upper bound that is exponential in the number of constraints. More precisely, the iteration count of our algorithm is at most a small polynomial factor times the segment count of any piecewise linear trajectory within a wide neighborhood of the central path. Notably, it parallels the iteration count of any path-following interior point method, with an adjustment for this polynomial factor.\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003EIn the second part of our talk, we give a strongly polynomial algorithm for minimum cost generalized flow, and hence all linear programs with at most two nonzero entries per row, or at most two nonzero entries per column. This provides a next milestone towards answering Smale\u2019s 9th problem.\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Ch3\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cstrong\u003EBio:\u003C\/strong\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/h3\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003EBento Natura is a Ronald J. and Carol T. Beerman\/ARC Postdoctoral Fellow in ISyE at Georgia Tech. He obtained his PhD in the Department of Mathematics at the London School of Economics, where he was supervised by L\u00e1szl\u00f3 V\u00e9gh. His doctoral thesis earned him the departmental Dissertation Prize and placed as a runner-up for the PhD Prize awarded by the OR Society of the United Kingdom. Prior to his PhD, Bento earned Bachelor\u0027s and Master\u0027s degrees in Mathematics from the University of Bonn, under the supervision of Stephan Held and Jens Vygen. Bento\u0027s current research interests are centered on algorithms, optimization, and game theory.\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Ch3\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cstrong\u003EAbstract:\u003C\/strong\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/h3\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003EWhereas ellipsoid methods and interior point methods provide polynomial-time linear programming algorithms, the running time bounds depend on bit-complexity or condition measures that can be unbounded in the problem dimension. This is in contrast with the simplex method that always admits an exponential bound.\u0026nbsp;\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003EAn important unresolved question in operations research, theoretical computer science, and related fields, concerns the existence of a strongly polynomial algorithm for linear programming. Such an algorithm\u0027s running time would solely depend on the problem\u0027s dimension and the number of constraints, independent of any additional condition numbers. This question, first articulated by Megiddo in the 1980s, has gained prominence as Smale\u0027s 9th problem.\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003EIn the first part of our talk, we introduce a new polynomial-time path-following interior point method where the number of iterations admits a combinatorial upper bound that is exponential in the number of constraints. More precisely, the iteration count of our algorithm is at most a small polynomial factor times the segment count of any piecewise linear trajectory within a wide neighborhood of the central path. Notably, it parallels the iteration count of any path-following interior point method, with an adjustment for this polynomial factor.\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003EIn the second part of our talk, we give a strongly polynomial algorithm for minimum cost generalized flow, and hence all linear programs with at most two nonzero entries per row, or at most two nonzero entries per column. This provides a next milestone towards answering Smale\u2019s 9th problem.\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n","format":"limited_html"}],"field_summary_sentence":[{"value":"Recent Advances in Strongly Polynomial Algorithms for Linear Programming"}],"uid":"34977","created_gmt":"2024-01-04 13:14:21","changed_gmt":"2024-01-04 13:14:21","author":"Julie Smith","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2024-01-18T11:00:00-05:00","event_time_end":"2024-01-18T12:00:00-05:00","event_time_end_last":"2024-01-18T12:00:00-05:00","gmt_time_start":"2024-01-18 16:00:00","gmt_time_end":"2024-01-18 17:00:00","gmt_time_end_last":"2024-01-18 17:00:00","rrule":null,"timezone":"America\/New_York"},"location":"ISyE Groseclose 402","extras":[],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"177814","name":"Postdoc"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"671677":{"#nid":"671677","#data":{"type":"event","title":"ISyE Seminar - Sen Na","body":[{"value":"\u003Ch3\u003ETitle:\u003C\/h3\u003E\r\n\r\n\u003Cp\u003EPracticality meets Optimality: Real-Time Statistical Inference under Complex Constraints\u003C\/p\u003E\r\n\r\n\u003Ch3\u003EAbstract:\u003C\/h3\u003E\r\n\r\n\u003Cp\u003EConstrained estimation problems are prevalent in statistics, machine learning, and engineering. These problems\u0026nbsp;encompass constrained generalized linear models, constrained deep neural networks, physics-inspired machine\u0026nbsp;learning, algorithmic fairness, and optimal control. However, existing estimation methods under hard constraints\u0026nbsp;rely on either projection or regularization, which may theoretically exhibit optimal efficiency but are impractical\u0026nbsp;or unreasonably fail in reality. This talk aims to bridge the significant gap between practice and theory\u0026nbsp;for constrained estimation problems.\u003C\/p\u003E\r\n\r\n\u003Cp\u003EI will begin by introducing the critical methodology used to bridge the gap, called Stochastic Sequential Quadratic\u0026nbsp;Programming. We will see that SQP methods serve as the workhorse for modern scientific machine learning problems\u0026nbsp;and can resolve the failure modes of prevalent regularization-based methods. I will demonstrate how to make\u0026nbsp;SQP adaptive and scalable using various modern techniques, such as stochastic line search, trust region, and dimension\u0026nbsp;reduction.\u003C\/p\u003E\r\n\r\n\u003Cp\u003EAdditionally, I will show how to further enhance SQP to handle inequality constraints online.\u003Cbr \/\u003E\r\nFollowing the methodology, I will present some selective theories, emphasizing the consistency and efficiency\u003Cbr \/\u003E\r\nof the SQP methods. Specifically, I will show that online SQP iterates asymptotically exhibit normal behavior\u003Cbr \/\u003E\r\nwith a mean of zero and optimal covariance in the H\u00e1jek and Le Cam sense. Significantly, the covariance does\u003Cbr \/\u003E\r\nnot deteriorate even when we apply modern techniques driven by practical concerns. The talk concludes with\u003Cbr \/\u003E\r\nexperiments on both synthetic and real datasets.\u003C\/p\u003E\r\n\r\n\u003Ch3\u003EBio:\u003C\/h3\u003E\r\n\r\n\u003Cp\u003ESen Na is currently a postdoctoral researcher in the Department of Statistics and the International Computer\u003Cbr \/\u003E\r\nScience Institute at UC Berkeley. He received a Ph.D. degree in statistics from the University of Chicago.\u003Cbr \/\u003E\r\nSen Na\u2019s primary research interests lie in the mathematical foundations of data science, encompassing high dimensional\u0026nbsp;statistics, computational statistics, sequential decision-making, and large-scale and stochastic\u003Cbr \/\u003E\r\nnonlinear optimization. Additionally, he is passionate about various applications of machine learning methods in\u0026nbsp;scientific fields such as biology, neuroscience, physics, and engineering. Sen Na\u2019s research has been recognized\u0026nbsp;by the prestigious Harper Dissertation Fellowship from UChicago, and he has been selected as one of the\u0026nbsp;Young Researchers in ORIE by Cornell University.\u003C\/p\u003E\r\n","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Ch3\u003EAbstract:\u003C\/h3\u003E\r\n\r\n\u003Cp\u003EConstrained estimation problems are prevalent in statistics, machine learning, and engineering. These problems\u003Cbr \/\u003E\r\nencompass constrained generalized linear models, constrained deep neural networks, physics-inspired machine\u003Cbr \/\u003E\r\nlearning, algorithmic fairness, and optimal control. However, existing estimation methods under hard constraints\u003Cbr \/\u003E\r\nrely on either projection or regularization, which may theoretically exhibit optimal efficiency but are impractical\u003Cbr \/\u003E\r\nor unreasonably fail in reality. This talk aims to bridge the significant gap between practice and theory\u003Cbr \/\u003E\r\nfor constrained estimation problems.\u003C\/p\u003E\r\n\r\n\u003Cp\u003EI will begin by introducing the critical methodology used to bridge the gap, called Stochastic Sequential Quadratic\u003Cbr \/\u003E\r\nProgramming. We will see that SQP methods serve as the workhorse for modern scientific machine learning problems\u0026nbsp;and can resolve the failure modes of prevalent regularization-based methods. I will demonstrate how to make\u0026nbsp;SQP adaptive and scalable using various modern techniques, such as stochastic line search, trust region, and dimension\u0026nbsp;reduction. Additionally, I will show how to further enhance SQP to handle inequality constraints online.\u003C\/p\u003E\r\n\r\n\u003Cp\u003EFollowing the methodology, I will present some selective theories, emphasizing the consistency and efficiency\u003Cbr \/\u003E\r\nof the SQP methods. Specifically, I will show that online SQP iterates asymptotically exhibit normal behavior\u003Cbr \/\u003E\r\nwith a mean of zero and optimal covariance in the H\u00e1jek and Le Cam sense. Significantly, the covariance does\u003Cbr \/\u003E\r\nnot deteriorate even when we apply modern techniques driven by practical concerns. The talk concludes with\u003Cbr \/\u003E\r\nexperiments on both synthetic and real datasets.\u003Cbr \/\u003E\r\n\u0026nbsp;\u003C\/p\u003E\r\n","format":"limited_html"}],"field_summary_sentence":[{"value":"Practicality meets Optimality: Real-Time Statistical Inference under Complex Constraints"}],"uid":"34977","created_gmt":"2023-12-21 14:32:47","changed_gmt":"2023-12-21 14:35:28","author":"Julie Smith","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2024-01-16T11:00:00-05:00","event_time_end":"2024-01-16T12:00:00-05:00","event_time_end_last":"2024-01-16T12:00:00-05:00","gmt_time_start":"2024-01-16 16:00:00","gmt_time_end":"2024-01-16 17:00:00","gmt_time_end_last":"2024-01-16 17:00:00","rrule":null,"timezone":"America\/New_York"},"location":"ISyE Groseclose 402","extras":[],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"177814","name":"Postdoc"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"671660":{"#nid":"671660","#data":{"type":"event","title":"ISyE Seminar - Mohsen Moghaddam","body":[{"value":"\u003Ch3\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cstrong\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003ETitle:\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/strong\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/h3\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003EThe Human-Tech Duo: Augmenting Learning and Creativity with AI and Spatial Computing\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Ch3\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cstrong\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003EAbstract:\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/strong\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/h3\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003EHuman-centered computing holds the promise of enabling a symbiotic relationship between humans and technology, prioritizing augmentation over substitution with software and machines. How can we realize this vision in industrial workplaces by harnessing AI and extended reality (XR), coupled with new sources of data? In this talk, I will present two research examples addressing this question. The first involves helping industrial workers learn complex psychomotor and cognitive tasks, such as inspection and assembly, more effectively. I will discuss how AI-powered XR can accelerate the progression from novice to expert through adaptive interventions tailored to individual needs, skills, and knowledge. I will introduce a new intelligent XR framework facilitating this through online activity understanding, error detection and prevention, expertise modeling, and content generation. The second example focuses on enhancing the creativity of product designers. I will discuss how AI can transform the front-end of product design by automating the analysis of user data and translating it into creative and user-centered concepts. I will present our current research on large-scale need finding from user reviews, simulation of usage contexts in XR for latent need elicitation, design concept generation and evaluation, and interfaces supporting designer exploration. In concluding the talk, I will present an overarching research agenda and discuss multiple avenues for future research on human-technology teaming within diverse industrial contexts.\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Ch3\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cstrong\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003EBio:\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/strong\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/h3\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003EMohsen Moghaddam is an Assistant Professor of Mechanical and Industrial Engineering, affiliated with the Khoury College of Computer Sciences, and serves as the Director of the Symbiotic and Augmented Intelligence Lab (SAIL) at Northeastern University. He is also a Visiting Scholar at the Next Level Lab, Harvard University. He earned his PhD in Industrial Engineering from Purdue University and served as a Postdoctoral Associate at the GE-Purdue Partnership in Research and Innovation in Advanced Manufacturing before joining Northeastern. His research focuses on exploring human-centered computational models, algorithms, and tools at the intersection of AI and spatial computing to enhance learning and creativity in various cognitive and psychomotor tasks within industrial settings. In addition to his academic pursuits, he co-founded ADA Tech (Advanced Design Augmentation Technologies) and currently serves as its Acting CTO, leading use-inspired research on AI tools that empower designers to create diverse, innovative, and user-centered products. His research is sponsored by NSF, DARPA, the U.S. Navy, Northeastern University, and industry.\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Ch3\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cstrong\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003EAbstract:\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/strong\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/h3\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003EHuman-centered computing holds the promise of enabling a symbiotic relationship between humans and technology, prioritizing augmentation over substitution with software and machines. How can we realize this vision in industrial workplaces by harnessing AI and extended reality (XR), coupled with new sources of data? In this talk, I will present two research examples addressing this question. The first involves helping industrial workers learn complex psychomotor and cognitive tasks, such as inspection and assembly, more effectively. I will discuss how AI-powered XR can accelerate the progression from novice to expert through adaptive interventions tailored to individual needs, skills, and knowledge. I will introduce a new intelligent XR framework facilitating this through online activity understanding, error detection and prevention, expertise modeling, and content generation. The second example focuses on enhancing the creativity of product designers. I will discuss how AI can transform the front-end of product design by automating the analysis of user data and translating it into creative and user-centered concepts. I will present our current research on large-scale need finding from user reviews, simulation of usage contexts in XR for latent need elicitation, design concept generation and evaluation, and interfaces supporting designer exploration. In concluding the talk, I will present an overarching research agenda and discuss multiple avenues for future research on human-technology teaming within diverse industrial contexts.\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n","format":"limited_html"}],"field_summary_sentence":[{"value":"The Human-Tech Duo: Augmenting Learning and Creativity with AI and Spatial Computing"}],"uid":"34977","created_gmt":"2023-12-20 16:10:56","changed_gmt":"2023-12-20 16:10:56","author":"Julie Smith","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2024-01-25T11:00:00-05:00","event_time_end":"2024-01-25T12:00:00-05:00","event_time_end_last":"2024-01-25T12:00:00-05:00","gmt_time_start":"2024-01-25 16:00:00","gmt_time_end":"2024-01-25 17:00:00","gmt_time_end_last":"2024-01-25 17:00:00","rrule":null,"timezone":"America\/New_York"},"location":"ISyE Groseclose 402","extras":[],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"177814","name":"Postdoc"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"671659":{"#nid":"671659","#data":{"type":"event","title":"ISyE Seminar - Anirudh Sridhar ","body":[{"value":"\u003Ch3\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003ETitle: \u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/h3\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003EModeling and Mitigation of Network Cascades\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Ch3\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003EAbstract:\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/h3\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003EAs our world becomes more connected through physical, technological and socioeconomic networks, it becomes more vulnerable to failures in these networks. These failures, which often rapidly diffuse across the network, have dealt serious societal damage in the form of epidemics, computer viruses, and misinformation. It is therefore imperative to understand how such \u0022cascading failures\u0022 propagate, and how interventions can be designed to mitigate their effects.\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003EIn the first part of this talk, I will discuss recent progress in modeling network cascades. A fundamental hurdle in the analysis of cascades is their inherent high-dimensional structure, caused by the complexity of the underlying network as well as the stochasticity of the cascade dynamics. This motivates the use of simpler approximations for understanding the evolution of the cascade. I will touch upon new results along these lines related to mean-field models, mask-wearing, and multi-strain models with mutations.\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003EIn the second part of this talk, I will develop algorithms for the real-time localization of network cascades. Specifically, our goal is to identify the cascade source before too many vertices in the network are affected by the cascade. The cascade is assumed to spread according to a Susceptible-Infected process from an unknown source in a network. While the propagation is not directly observable, noisy information about its spread can be gathered through multiple rounds of error-prone diagnostic testing. Using this data model, we devise a novel adaptive procedure inspired by classical multi-hypothesis sequential probability ratio tests (MSPRTs) which provably localizes the cascade before a negligible fraction of the network is affected. In certain cases, our method is optimal, i.e. no other algorithm can localize the cascade using substantially fewer rounds of testing. Based on joint work with Tirza Routtenberg and H. Vincent Poor.\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Ch3\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003EBio:\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/h3\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003EAnirudh Sridhar (Ani) is a postdoctoral associate at MIT\u0027s Department of Mathematics. Previously, he completed his PhD from Princeton\u0027s Department of Electrical and Computer Engineering, where he was advised by H. Vincent Poor and Mikl\u00f3s R\u00e1cz. Broadly, Ani\u0027s research develops statistical methods for the analysis of networks, with a focus on information-theoretic characterizations. His awards include the Yan Huo *94 Graduate Fellowship in Electrical Engineering from Princeton University in 2022 and a Spotlight Presentation at NeurIPS 2021. He was also a finalist for the Informs-APS Best Student Paper Award in 2020.\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Ch3\u003E\u003Cstrong\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003EAbstract:\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/strong\u003E\u003C\/h3\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003EAs our world becomes more connected through physical, technological and socioeconomic networks, it becomes more vulnerable to failures in these networks. These failures, which often rapidly diffuse across the network, have dealt serious societal damage in the form of epidemics, computer viruses, and misinformation. It is therefore imperative to understand how such \u0022cascading failures\u0022 propagate, and how interventions can be designed to mitigate their effects.\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003EIn the first part of this talk, I will discuss recent progress in modeling network cascades. A fundamental hurdle in the analysis of cascades is their inherent high-dimensional structure, caused by the complexity of the underlying network as well as the stochasticity of the cascade dynamics. This motivates the use of simpler approximations for understanding the evolution of the cascade. I will touch upon new results along these lines related to mean-field models, mask-wearing, and multi-strain models with mutations.\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003EIn the second part of this talk, I will develop algorithms for the real-time localization of network cascades. Specifically, our goal is to identify the cascade source before too many vertices in the network are affected by the cascade. The cascade is assumed to spread according to a Susceptible-Infected process from an unknown source in a network. While the propagation is not directly observable, noisy information about its spread can be gathered through multiple rounds of error-prone diagnostic testing. Using this data model, we devise a novel adaptive procedure inspired by classical multi-hypothesis sequential probability ratio tests (MSPRTs) which provably localizes the cascade before a negligible fraction of the network is affected. In certain cases, our method is optimal, i.e. no other algorithm can localize the cascade using substantially fewer rounds of testing. Based on joint work with Tirza Routtenberg and H. Vincent Poor.\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n","format":"limited_html"}],"field_summary_sentence":[{"value":"Modeling and Mitigation of Network Cascades"}],"uid":"34977","created_gmt":"2023-12-20 16:07:08","changed_gmt":"2023-12-20 16:07:08","author":"Julie Smith","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2024-01-09T11:00:00-05:00","event_time_end":"2024-01-09T12:00:00-05:00","event_time_end_last":"2024-01-09T12:00:00-05:00","gmt_time_start":"2024-01-09 16:00:00","gmt_time_end":"2024-01-09 17:00:00","gmt_time_end_last":"2024-01-09 17:00:00","rrule":null,"timezone":"America\/New_York"},"location":"ISyE Groseclose 402","extras":[],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"177814","name":"Postdoc"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"671508":{"#nid":"671508","#data":{"type":"event","title":"ISyE Seminar - Shuangning Li","body":[{"value":"\u003Ch3\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cstrong\u003E\u003Cspan\u003E\u003Cspan\u003ETitle\u003C\/span\u003E\u003C\/span\u003E\u003C\/strong\u003E\u003Cspan\u003E\u003Cspan\u003E: \u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/h3\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003EModeling\u0026nbsp;Interference\u0026nbsp;for Policy Evaluation in Stochastic Systems\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Ch3\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cstrong\u003E\u003Cspan\u003EAbstract\u003C\/span\u003E\u003C\/strong\u003E\u003Cspan\u003E:\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/h3\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003EInterference is a phenomenon where the treatment of one unit may affect the outcomes of other units. It is a major consideration for accurate policy evaluation in stochastic systems. Although this may seem intractable in a non-parametric causal inference setting, I will demonstrate that in many problems, some lightweight modeling can significantly aid in capturing and quantifying these interference effects. In particular, I will discuss two different forms of interference:\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E(1) Network interference. In the network interference model, units are represented as vertices on an exposure graph (for example, a social network). In this model, the treatment assigned to one unit may affect the outcomes of other units connected to it through edges in the graph. I will discuss large-sample asymptotics for treatment effect estimation under network interference, where the exposure graph is a random draw from a graphon.\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E(2) Congestion induced interference. In service systems, stochastic congestion can arise from temporarily limited supply and\/or demand. Such congestion gives rise to interference between the waiting customers, and analytic strategies that do not account for this interference may be biased. I will discuss the potential of using knowledge about the congestion mechanism to design and analyze experiments in the presence of stochastic congestion.\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Ch3\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cstrong\u003E\u003Cspan\u003EBio\u003C\/span\u003E\u003C\/strong\u003E\u003Cspan\u003E: \u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/h3\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003EI am currently a postdoctoral fellow working with Professor Susan Murphy in the Department of Statistics at Harvard University. Prior to this, I earned my Ph.D. from the Department of Statistics at Stanford University, where I was advised by Professors Emmanuel Cand\u00e8s and Stefan Wager.\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Ch3\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cstrong\u003E\u003Cspan\u003EAbstract\u003C\/span\u003E\u003C\/strong\u003E\u003Cspan\u003E: \u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/h3\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003EInterference is a phenomenon where the treatment of one unit may affect the outcomes of other units. It is a major consideration for accurate policy evaluation in stochastic systems. Although this may seem intractable in a non-parametric causal inference setting, I will demonstrate that in many problems, some lightweight modeling can significantly aid in capturing and quantifying these interference effects. In particular, I will discuss two different forms of interference:\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E(1) Network interference. In the network interference model, units are represented as vertices on an exposure graph (for example, a social network). In this model, the treatment assigned to one unit may affect the outcomes of other units connected to it through edges in the graph. I will discuss large-sample asymptotics for treatment effect estimation under network interference, where the exposure graph is a random draw from a graphon.\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E(2) Congestion induced interference. In service systems, stochastic congestion can arise from temporarily limited supply and\/or demand. Such congestion gives rise to interference between the waiting customers, and analytic strategies that do not account for this interference may be biased. I will discuss the potential of using knowledge about the congestion mechanism to design and analyze experiments in the presence of stochastic congestion.\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n","format":"limited_html"}],"field_summary_sentence":[{"value":"Modeling Interference for Policy Evaluation in Stochastic Systems"}],"uid":"34977","created_gmt":"2023-12-11 14:17:46","changed_gmt":"2023-12-11 14:17:46","author":"Julie Smith","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2024-01-11T11:00:00-05:00","event_time_end":"2024-01-11T12:00:00-05:00","event_time_end_last":"2024-01-11T12:00:00-05:00","gmt_time_start":"2024-01-11 16:00:00","gmt_time_end":"2024-01-11 17:00:00","gmt_time_end_last":"2024-01-11 17:00:00","rrule":null,"timezone":"America\/New_York"},"location":"ISyE Groseclose 402","extras":[],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"177814","name":"Postdoc"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}},"671434":{"#nid":"671434","#data":{"type":"event","title":"Webinar - Reducing Food Waste in the Supply Chain: Greenhouse Gas Benefit","body":[{"value":"\u003Cp\u003E\u003Cstrong\u003ESession Overview\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003EReducing crop and food waste is widely understood to have environmental and economic benefits. However, at different points along the supply chain, and for different approaches to reducing food waste, the benefits may be larger or smaller, or in some cases even negative. General life cycle assessment concepts will be explained with examples of the food supply chain and how it might change under a range of waste reduction scenarios. Examples of how to quantify and characterize the greenhouse gas benefits of reducing crop and food waste will be discussed, with suggestions for greater efficiency and improvement.\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003EAbout Dr. Valerie M. Thomas\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003EValerie Thomas is the Anderson-Interface Chair of Natural Systems and Professor in the H. Milton School of Industrial and Systems Engineering, with a joint appointment in the School of Public Policy. Dr. Thomas\u0027s research interests are energy and materials efficiency, sustainability, industrial ecology, technology assessment, international security, and science and technology policy. Current research projects include low carbon transportation fuels, carbon capture, building construction, and electricity system development. Dr. Thomas is a Fellow of the American Association for the Advancement of Science, and of the American Physical Society. She has been an American Physical Society Congressional Science Fellow, a Member of the U.S. EPA Science Advisory Board, and a Member of the USDA\/DOE Biomass Research and Development Technical Advisory Committee. She has worked at Princeton University in the Princeton Environmental Institute and in the Center for Energy and Environmental Studies, and at Carnegie Mellon University in the Department of Engineering and Public Policy. Dr. Thomas received a B. A. in physics from Swarthmore College and a Ph.D. in theoretical physics from Cornell University.\u003C\/p\u003E\r\n","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EReducing crop and food waste is widely understood to have environmental and economic benefits. However, at different points along the supply chain, and for different approaches to reducing food waste, the benefits may be larger or smaller, or in some cases even negative.\u003C\/p\u003E\r\n","format":"limited_html"}],"field_summary_sentence":[{"value":"Featuring Dr. Valerie Thomas, Anderson-Interface Chair of Natural Systems and Professor"}],"uid":"27233","created_gmt":"2023-12-06 12:48:23","changed_gmt":"2023-12-06 13:03:48","author":"Andy Haleblian","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2023-12-07T14:00:00-05:00","event_time_end":"2023-12-07T15:00:00-05:00","event_time_end_last":"2023-12-07T15:00:00-05:00","gmt_time_start":"2023-12-07 19:00:00","gmt_time_end":"2023-12-07 20:00:00","gmt_time_end_last":"2023-12-07 20:00:00","rrule":null,"timezone":"America\/New_York"},"location":"Online via Microsoft Teams","extras":[],"hg_media":{"672510":{"id":"672510","type":"image","title":"ECU: Reducing Food Waste in the Supply Chain","body":null,"created":"1701866573","gmt_created":"2023-12-06 12:42:53","changed":"1701866614","gmt_changed":"2023-12-06 12:43:34","alt":"Reducing Food Waste in the Supply Chain Seminar flyer","file":{"fid":"255759","name":"VThomasReductingFoodWasteSeminar.png","image_path":"\/sites\/default\/files\/2023\/12\/06\/VThomasReductingFoodWasteSeminar.png","image_full_path":"http:\/\/hg.gatech.edu\/\/sites\/default\/files\/2023\/12\/06\/VThomasReductingFoodWasteSeminar.png","mime":"image\/png","size":1289720,"path_740":"http:\/\/hg.gatech.edu\/sites\/default\/files\/styles\/740xx_scale\/public\/2023\/12\/06\/VThomasReductingFoodWasteSeminar.png?itok=FbDPKxuB"}}},"media_ids":["672510"],"related_links":[{"url":"https:\/\/www.isye.gatech.edu\/users\/valerie-thomas","title":"ISyE Profile - Dr. Valerie Thomas"},{"url":"https:\/\/GO.ECU.EDU\/CSE3WEBINAR\/REDUCINGFOODWASTEGHGBENEFITS","title":"Register Online for Microsoft Teams link"}],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"},{"id":"1243","name":"The Supply Chain and Logistics Institute (SCL)"}],"categories":[],"keywords":[{"id":"53321","name":"Food Supply Chain"},{"id":"174066","name":"greenhouse gases"},{"id":"143871","name":"Physical Internet Center"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"78771","name":"Public"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}}}