This seminar presents a synthesis of research focused on leveraging these data sources for infectious disease prediction, spanning from classical statistical approaches to modern AI architectures.

Key Areas of Discussion:

• Statistical Methodologies: The presentation covers a family of models that combine autoregressive time series structure with penalized regression on Google search data. This includes extensions to spatial-temporal modeling, multi-disease settings, and COVID-19 adaptation.
• AI Architectures: Recent work on attention-based transformer architectures for time series forecasting will be discussed, highlighting methods for multivariate dependency modeling, in-context learning, and efficient linear attention.
• Practical Application: Drawing from ongoing participation in the CDC FluSight forecasting initiative, the session compares the strengths and limitations of both paradigms and shares practical lessons learned from real-time deployment.

The talk aims to offer perspective on when and how statistical rigor and deep learning flexibility each contribute to reliable disease forecasting.

About the Speaker

Dr. Shihao Yang is the Harold E. Smalley Early Career Professor and Assistant Professor in the School of Industrial and Systems Engineering at Georgia Tech. He completed his PhD in statistics and post-doc in Biomedical Informatics at Harvard University. Dr. Yang’s research focuses on data science, with special interest in time series, dynamical systems, and applications in infectious disease transmission forecasting.

Presenter: Alex Yang

Seminar Title

 LitForager: Exploring Multimodal Literature Foraging Strategies in Immersive Sensemaking 

Affiliation

IVI Lab (Immersive Visualization & Interaction Lab), School of Interactive Computing (Georgia Tech College of Computing)

Seminar Title

We 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. 

Participation & Visibility

We 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. 

Subscribe & Sign Up 

To join our mailing list and RSVP your attendance for these seminar series, please sign up here: RSVP HERE. Lunch will be served for those who RSVP.

Register Online to Attend

Abstract:

Bio: 

Optimization Meets Participation: Iterative School Zone Generation with LLMs

Abstract: 

In U.S. public school systems, geographic boundaries play a central role in shaping students’ 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 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.

Bio: 

Irene Lo is an assistant professor in the department of Management Science & 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.

Distributionally Robust Newsvendor on a Metric

Abstract:

We 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). 

We 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 "virtual-underage cost" 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's 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.   (This is joint work with Ayoub Foussoul)

Bio:

Vineet Goyal is a Professor in the Industrial Engineering and Operations Research Department at Columbia University where he joined in 2010. He received his Bachelor's 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  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.

The 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.

Professor 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.

Professor 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’s Alfredo Ang award for risk analysis and management of civil infrastructure, and the IASSAR Distinguished Research award.

Featuring Jon Duke, 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’s 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's 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.

Abstract: 

My team’s 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’s 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 "deployment bottleneck" 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).

This 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.

Bio:
Milind Tambe is the Gordon McKay Professor of Computer Science at Harvard University; concurrently, he is Principal Scientist and Director for “AI for Social Good” 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.

 

Abstract:

Many 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 Uncertainty Quantification (UQ)—a multidisciplinary framework drawing from statistics, applied mathematics, and engineering—to better design, test, and manufacture products under uncertainty.

This 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.

Bio 

Peter Chien is a Professor of Statistics and Industrial & Systems Engineering at the University of Wisconsin–Madison 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.
 

Presenter: Srikanth Tindivanam Varadharajan (Aerospace Engineering)

View Event Photos
 

Seminar Title: 

XR for the Sky: Enhancing UAV and UAM Operations 

Affiliation

Georgia Tech, College of Engineering : DR-CSE-AE, Labs: ASDL and  CCG (Aerospace Systems Design Laboratory and Contextual Computing Group) 

Participation & Visibility 

We 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. 

Subscribe & Sign Up 

To join our mailing list and RSVP your attendance for these seminar series, please sign up here: RSVP HERE. Lunch will be served for those who RSVP.

Veridical Data Science for Healthcare in the Age of AI

Georgia Tech Exhibition Hall
Kirkwood Room
Monday, March 30, 2026
3:30-4:30PM 
Reception to follow at ISyE Main Atrium

Abstract: Dr. Bin Yu, Keynote Speaker
 

Data 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.

We introduce veridical data science, grounded in three fundamental principles—Predictability, Computability, and Stability (PCS)—to 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.

About: Dr. Yu
 

Dr. Bin Yu is CDSS Chancellor's 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.

She 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).

Event Details

On Campus/In-Person (Georgia Tech Exhibition Hall)

• Wednesday, September 9, 2026 | 10am-2pm ET 

Students

We strongly encourage you to attend to seek full-time employment, internships, and projects (rather than waiting until the end of the semester).

Organizations

If 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 August 1st!

MORE INFORMATION AND EVENT REGISTRATION

Visit https://www.scl.gatech.edu/outreach/supplychainday.

Presenter: Seok Joon Kim 

Seminar Title

AHEAD of Time: Toward Robots That Behave Like Human Companions 

Affiliation 

George W. Woodruff School of Mechanical Engineering (Georgia Tech College of Engineering), Symbiotic and Augmented Intelligence Laboratory (SAIL)

Participation & Visibility 

We 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. 

Subscribe & Sign Up 

To join our mailing list and RSVP your attendance for these seminar series, please sign up here: RSVP HERE. Lunch will be served for those who RSVP.

Presenter: Prithiv Premkumar 
 

Seminar Title: 

Motion, Heart Rate, and Haptics: Using XR devices for Human Monitoring and Regulation 

Affiliation 

School of Interactive Computing (Georgia Tech College of Computing), Georgia Tech Sonification Lab

Participation & Visibility 

We 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. 

Subscribe & Sign Up 

To join our mailing list and RSVP your attendance for these seminar series, please sign up here: RSVP HERE. Lunch will be served for those who RSVP.

Presenter: Hanna Neroj

Seminar Title: 

Simulating the Future: Experience Prototyping for Emerging Technologies via Multimodal XR 

Affiliation

School of Psychology (Georgia Tech College of Sciences), Georgia Tech Sonification Lab

Participation & Visibility 

We 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. 

Subscribe & Sign Up 

To join our mailing list and RSVP your attendance for these seminar series, please sign up here: RSVP HERE. Lunch will be served for those who RSVP.

Presenter: Jorge Garcia

Seminar Title: 

Human-in-the-Loop and XR for Context-Rich Industrial Decision-Making 

Affiliation

Solving the Mysteries of Place Cells and Grid Cells by Representation Learning

Abstract:

The 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.

Bio:

Ying 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’s research spans generative AI, computer vision, computational neuroscience, and bioinformatics.

This 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.

Who Should Attend

This 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's role in transforming supply chain processes.

How You Will Benefit

• Enhance decision-making capabilities through GenAI-driven insights to optimize processes and boost efficiency.
• Acquire practical skills in prompt engineering and the use of generative AI models.
• Explore practical use cases that can be reapplied.
• Learn about good and bad use of GenAI for individuals, teams, and organizations.
• Become better equipped to effectively harness GenAI capabilities in supply chain activities and planning.

What Is Covered

• Foundational understanding of using GenAI in supply chain management
• Basics of GenAI
• Crafting effective AI prompts and their applications in optimizing warehouse layouts
• Predictive maintenance and supplier selection
• Elimination of redundant tasks through AI
• Ethical considerations, risk assessments, and strategy for AI adoption
• Practical strategies and real-world examples for implementing AI solutions effectively and making informed decisions

Making AI Impactful in Healthcare

Abstract:

There is increasing evidence that Machine Learning and Artificial intelligence algorithms can be used to enhance clinical care. In this talk, I address two critical aspects that can significantly improve the impact of such algorithms in healthcare practices: (1) moving beyond associations and creating algorithms capable of causal reasoning under ambiguity, and (2) a human-algorithm “centaur” 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) at Harvard, and discuss findings based on our various collaborations with the Mayo Clinic, Mass General Hospital, and some other public and private organizations.

Bio:

Soroush Saghafian (Wikipedia) is an Associate Professor at Harvard University and is the founder and director of Harvard’s Public Impact Analytics Science Lab (PIAS-Lab). He also serves as a core faculty or a faculty affiliate for (a) Harvard Data Science Initiative, (b) Harvard Mossavar-Rahmani Center for Business and Government, (c) Harvard Center for Health Decision Science, (d) Harvard Ph.D. Program in Health Policy, (e) Harvard Belfer Center for Science and International Affairs, (f) Harvard Center for Public Leadership, and (g) Harvard Ariadne Labs (a pioneer lab in health systems innovation), and holds appointments at Massachusetts General Hospital (MGH), Beth Israel Deaconess Medical Center (BIDMC), and Mayo Clinic. He is an expert in healthcare AI, analytics, and operations management, and has collaborated with a variety of hospitals. 

Dr. Saghafian's research has appeared numerous times in the news including in top national and international media outlets, and has been recognized through various awards, including the INFORMS MSOM Young Scholar Prize for “outstanding contributions to scholarship in operations management,” INFORMS MSOM Responsible Research Award (second place) for “contributing knowledge that may have implications for making the world a better place,” the Inaugural INFORMS Mehrotra Research Excellence Award “for significant contributions to the practice of health applications through operations research and management science modeling and methodologies,” INFORMS Computing Society Harvey Greenberg Award (honorable mention) “for research excellence in the field of computation and operations research applications, especially those in emerging application fields,” INFORMS Pierskalla Award “for the best research paper in healthcare,” INFORMS Franz Edelman Award (semi-finalist) “for achievement in advanced analytics, operations research, and management science,” and POMS College of Healthcare Best Paper Award. His forthcoming book with Cambridge University Press, “Insight-Driven Problem Solving: Analytics Science to Improve the World,” has been endorsed by top academic and industry figures [Full CV here].

Thursday, March 5, 2026 | 12-1pm ET

Register Online to Attend

Thursday, April 2, 2026 | 12-1pm ET

Register Online to Attend

January 23, 2026
10:30 AM – 4:00 PM 
ISyE Main Building 

Agenda: 

10:30 AM – Noon (ISyE Main Atrium):
XR Symposium: Invited Talks - Short talks and discussion on XR research and applications across campus

Noon – 1:00 PM (ISyE Main Atrium): 
Lunch & Networking: Informal networking in the ISyE Main Atrium

1:00 – 4:00 PM (ISyE Main 115)
XR Makerspace Open House & Demos: Hands-on demonstrations, guided tours, and conversations with XR Makerspace staff and researchers

Invited Speakers:

Bruce Walker, School of Psychology & School of Interactive Computing, Georgia Tech
Talk Title: Research With, About, and In Virtual Reality: A Sample of Sonification Lab XR R&D Projects

Bio: 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 & Perception, and Assistive Technology; and has consulted for NASA, state and federal governments, the military, and private companies.

Frederick Benaben, H. Milton Stewart School of Industrial & Systems Engineering, Georgia Tech
Talk Title: A Framework for Immersive Technologies in Engineering: An Illustrated Perspective

Bio: 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 “Digital Systems for Crisis Management and Security” 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.

Shreyas Kousik, George W. Woodruff School of Mechanical Engineering, Georgia Tech
Talk Title: Do Dangerous Things Safely in XR Humanoid Robot Teleoperation

Bio: 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.

Wayne Li, Colleges of Design and Engineering, Georgia Tech
Talk Title: Using XR for Automotive Interior Development (Hyundai Project)

Bio: 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's 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's design program, led interface development at Volkswagen of America's 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. 

Meryem Yilmaz Soylu, Center for 21st Century Universities, Georgia Tech
Talk Title: Designing Human-Centered XR for Learning, Reflection, and Skill Development

Bio: Meryem Yilmaz Soylu is a Research Scientist at Georgia Tech’s 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.

The free course will address how to prevent injury and illness due to workplace exposures in warehouses. 

Topics to Cover:

• Recognition of safety hazards in warehousing facilities and prevention strategies
• Powered Industrial Truck safety and responsibilities
• Ergonomic best practices and safe material handling
• Chemical and thermal stressor risk assessments and strategies

Available Sessions

• Tuesday, March 24, 1-5pm (register online)
• Wednesday, March 25, 8-12pm (register online)

Each session to be held at the below location:

Professional Administration Resource Building
Georgia Tech Savannah Campus
210 Technology Circle
Savannah, GA 31408

We will have the capability to provide on-site training. Training materials and course content will be offered at no cost to interested facilities. oshainfo.gatech.edu

The free course will address how to prevent injury and illness due to workplace exposures in warehouses. 

Topics to Cover:

• Recognition of safety hazards in warehousing facilities and prevention strategies
• Powered Industrial Truck safety and responsibilities
• Ergonomic best practices and safe material handling
• Chemical and thermal stressor risk assessments and strategies

Available Sessions

• Tuesday, March 24, 1-5pm (register online)
• Wednesday, March 25, 8-12pm (register online)

Each session to be held at the below location:

Professional Administration Resource Building
Georgia Tech Savannah Campus
210 Technology Circle
Savannah, GA 31408

We will have the capability to provide on-site training. Training materials and course content will be offered at no cost to interested facilities. oshainfo.gatech.edu

Leading with Purpose and Innovation: Manhattan Associates' Journey
ISyE Main
Thursday, February 26, 2026
3:30-4:30PM 
Reception to follow. 

RSVP here: https://eforms.isye.gatech.edu/2026-distinguished-lecture-series

Abstract: Eddie Capel, Keynote Speaker
 

Over the past 25 years, Manhattan Associates has played a pivotal role in transforming global supply chains, enabling many of the world’s largest retailers, brands, pharmaceutical distributors, and logistics providers to operate faster, smarter, and more reliably.

In this lecture, Eddie Capel, chairman of the board at Manhattan Associates, will reflect on Manhattan’s 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’s 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.

The session highlights how strategic technology choices, long-term R&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.

About: Eddie Capel, Keynote Speaker
 

Eddie 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.

Before 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.

This 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.

Beyond 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.

This 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.

Featuring Xiao Liu, PhD, David M. McKenney Family Associate Professor, H. Milton Stewart School of Industrial and Systems Engineering, Georgia Tech Center for Health and Humanitarian Systems. Dr. Liu's 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 & Information Systems division of IISE and as Program Chair for the 2025 IISE Annual Conference & Expo. Before returning to academia, he worked as a research staff member at the IBM Thomas J. Watson Research Center.

Offered online via Zoom. Please register to attend.

Abstract:

Bio:

Abstract: 

Frontiers and Applications at the Interface of Discrete Optimization and Interpretable Machine Learning

Abstract:

Modern 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.

Bio:

Brian 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).

Don’t miss this opportunity to explore cutting-edge AI applications in Medical and Health Care Systems.

https://sites.gatech.edu/ai-mhcs/

Featuring Lauren N. Steimle, PhD, Assistant Professor, H. Milton Stewart School of Industrial and Systems Engineering, Georgia Tech Center for Health and Humanitarian Systems. Dr. Steimle's 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.

Offered online via Zoom. Please register to attend.

Bridging Machine Learning and Optimization for Human-Centered AI

Abstract: 

From healthcare delivery to resilient power grid management, predictive and prescriptive analytics tools have the potential to improve decision-making for some of today’s 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.

Bio: 

Connor 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.

Thursday, February 5, 2026 | 12-1pm ET

Register Online to Attend

Every fall and spring semester, during the IISE Career Fair, companies across the nation come to Georgia Tech to recruit some of the nation’s top talent from our Bachelor’s and Master’s 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’s brightest students. This event is open to all majors, and recruiting for roles in Data Analytics, Supply Chain, Consulting, Operations, Finance, Business, Tech & more.

Employers

Recruit from the #1-Ranked Industrial Engineering Program in the nation at the IISE Career Fair. 

• Registration Deadline:  January 29, 2026
• Employer Registration Fee: $1000.00.
• [Click Here to Register Today]

Students

Explore internships and full-time opportunities with companies recruiting across Industrial & Systems Engineering and related fields. For more info & resume submission, visit https://www.gtiise.org/career-fair-info

Secure Your Spot Early for the Spring 2026 Career Fair! For questions, email iise@gatech.edu. 

Uncertainty quantification for black-box models with conditional guarantees

Abstract: 

A 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 Candès.

Bio: 

Isaac 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 Candès, 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. 

GPU-Accelerated Linear Programming and Beyond

Abstract: 

The 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.

Bio:

 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.

Abstract: 

Bio:

Don’t miss this opportunity to explore cutting-edge AI applications in Medical and Health Care Systems.

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Learn more about CHHS: Center for Health and Humanitarian Systems

This 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.

Who Should Attend

This 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's role in transforming supply chain processes.

How You Will Benefit

• Enhance decision-making capabilities through GenAI-driven insights to optimize processes and boost efficiency.
• Acquire practical skills in prompt engineering and the use of generative AI models.
• Explore practical use cases that can be reapplied.
• Learn about good and bad use of GenAI for individuals, teams, and organizations.
• Become better equipped to effectively harness GenAI capabilities in supply chain activities and planning.

What Is Covered

• Foundational understanding of using GenAI in supply chain management
• Basics of GenAI
• Crafting effective AI prompts and their applications in optimizing warehouse layouts
• Predictive maintenance and supplier selection
• Elimination of redundant tasks through AI
• Ethical considerations, risk assessments, and strategy for AI adoption
• Practical strategies and real-world examples for implementing AI solutions effectively and making informed decisions

Course Description

Many 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 “medium term” decision making including procurement and inventory policies, strategies for distribution and allocation of limited resources, and supply chain design.

Who Should Attend

This 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.

How You Will Benefit

• Provide immediate impact to your organization through applied and real-world case studies.
• Learn to bring NGOs, businesses, or government entities together to enhance collaboration, cooperation, and communication.
• Discover current trends and procedures to help your organization and team members get and stay ahead of the curve.

What Is Covered

• Procurement decisions
• Inventory management techniques for a single event versus ongoing operations under uncertainty
• Strategies for resource allocation geographically and over time

About the Course and the HHSCM Course Series

This course is the second in a 3-part virtually synchronous professional education program. Register and pay for all three required Health and Humanitarian Supply Chain Management Certificate courses and receive a discount of $400 off per course. Enter coupon code SCL-HHS at checkout with the Georgia Tech Professional Education website..  

Additionally, there are scholarships available for the certificate program. Apply at https://chhs.gatech.edu/course-scholarships by the noted deadline.  

Questions? Reach out to chhs@gatech.edu!

This 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.

Who Should Attend

This 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's role in transforming supply chain processes.

How You Will Benefit

• Enhance decision-making capabilities through GenAI-driven insights to optimize processes and boost efficiency.
• Acquire practical skills in prompt engineering and the use of generative AI models.
• Explore practical use cases that can be reapplied.
• Learn about good and bad use of GenAI for individuals, teams, and organizations.
• Become better equipped to effectively harness GenAI capabilities in supply chain activities and planning.

What Is Covered

• Foundational understanding of using GenAI in supply chain management
• Basics of GenAI
• Crafting effective AI prompts and their applications in optimizing warehouse layouts
• Predictive maintenance and supplier selection
• Elimination of redundant tasks through AI
• Ethical considerations, risk assessments, and strategy for AI adoption
• Practical strategies and real-world examples for implementing AI solutions effectively and making informed decisions

This landmark edition will begin in Albi, where the pre-conference on June 5–6 will bring together PhD students, researchers, and industry professionals for interactive training sessions, including the “My Thesis in 180 Seconds” challenge, and immersive learning experiences using cutting-edge Virtual Reality games. The event will then move to Bordeaux from June 8–10 for the main conference, themed “Supply Chain and Logistics Intelligence in the Era of the Physical Internet: Bridging High-Tech and Low-Tech Solutions.” 

Participants will enjoy a vibrant program featuring scientific and industrial sessions, inspiring keynotes, dynamic roundtables, exhibitions, company visits, and collaborative workshops designed to foster innovation and shape the future of logistics and supply chain networks.

And, because logistics is global, participants will be from all over the world including researchers, industrial and international institution members, local authorities and standardization committees.

About the Physical Internet Initiative
The 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 Benoit Montreuil in 2006, this ground breaking vision, revolutionizing current paradigms, has stirred great interest from scientific, industrial as well as governmental communities.

Thursday, January 8, 2026 | 12-1pm ET

Register Online to Attend

A Statistical Framework for Benchmarking Quantum Computers

Abstract: 

Bio: 

Heterogeneous Treatment Effects in Panel Data: Applications to the Healthy Incentives Program

Abstract:

In 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.

To 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.

Bio:

Emily 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.

Seeing the Forest for the Trees

Abstract:

Recent 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). 

This 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.

Unfortunately, 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.

Bio:

Anna 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&T Labs-Research. From 1998 to 2004, she was a member of technical staff at AT&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 & Data Science. In 2023, she left the Mathematics Department and is now in Statistics & 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).

Dr. Gilbert's 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.

Website: https://annacgilbert.github.io/cv/

From Democratizing Optimization with LLM to Improving LLM Performance with OR Techniques

Abstract:

Recent 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’s potent capabilities and its limited real-world uptake. We present the 4I framework—Insight, Interpretability, Interactivity, Improvisation—as 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.

We 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).

Bio:

David Simchi-Levi holds the MIT William Barton Rogers Professorship (named after the founder & 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.

Professor 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.

He 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.

Event Details

On Campus/In-Person (Georgia Tech Exhibition Hall)

• Wednesday, January 14, 2026 | 10am-2pm ET 

Students

We strongly encourage you to attend to seek full-time employment, internships, and projects (rather than waiting until the end of the semester).

Organizations

If 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!

MORE INFORMATION AND EVENT REGISTRATION

Visit https://www.scl.gatech.edu/outreach/supplychainday.

This course focuses on defining, executing, and improving the sales and operations planning (S&OP) process. Participants will be introduced to the appropriate stakeholders of S&OP, the importance of S&OP to corporate performance, S&OP cadence, and the use of decision support tools to bring S&OP to the next level. Business cases will be used to show concrete examples of companies where S&OP is effectively applied.

Who Should Attend

This course is designed for chief operating officers; supply chain, sales, marketing and finance management executives (directors, vice presidents, executive vice presidents); supply chain and logistics managers, consultants, supervisors, planners, and engineers; supply chain education and human resource management personnel, inventory and demand planners, and procurement and sourcing analysts and managers; and manufacturing planners, analysts, and managers.

How You Will Benefit

• Understand the need for an S&OP process in a company.
• Apply the principles that are the key to success of an S&OP process.

What You Will Learn

• S&OP process and technology
• S&OP implementation planning and execution
• S&OP stakeholder and communications planning
• S&OP business case and best practices
• S&OP process management

Gacha: A Simple Mechanism to Screen a Budget-Constrained Buyer

Abstract: 

A 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.,  Genshin Impact) 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 infinite) 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’s valuation is public, the Gacha mechanism achieves at least 63.2% of the optimal revenue. It becomes asymptotically optimal as the valuation grows large. Moreover, when the seller has almost no information about the buyer’s 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.

Bio: 

Zhenyu Hu is a Dean’s Chair Associate Professor at the Department of Analytics & 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&SOM. 

Thursday, December 4, 2025 | 12-1pm ET

This 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’ll use machine learning to conduct predictive analytics as you forecast future demand, develop inventory policies, perform customer segmentation and predictive maintenance. You’ll use Python and PowerBI to create and analyze regression, clustering, and classification models.

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. An optional pre-course webinar is typically held the Thursday before the course start date (July 6).

Who Should Attend

Experienced 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.

How You Will Benefit

• Understand the role of machine learning (ML) in Supply Chain Management (SCM)
• Apply advanced analytics techniques to build planning tools that can leverage large and real-time data sets
• Apply ML in demand forecasting and predictive maintenance
• Understand how to assess ML model performance, improve models, and pick the best model for a decision
• Use Python and PowerBI to build, analyze, and deploy ML models

What You Will Learn

• How ML relates to SCM
• ML algorithms such as regression trees, clustering techniques, decision trees, random forests, logistic regression
• Aspects of ML projects including parameter tuning, cross validation, and assess model performance
• Application of ML in demand forecasting for sales and operations planning (S&OP) and inventory management
• Application of ML in predictive maintenance
• Hands-on practice with these skills using data from the (fictional) Cardboard Company (CBC)

Abstract:

We 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^{−4}) to O(delta^{-2q}), assuming that the noise 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 O(delta^{−2-r}) 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.

Bio:

Luis Nunes Vicente is the Timothy J. Wilmott ’80 Endowed Faculty Professor and Chair of Lehigh University’s 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 “Introduction to Derivative-Free Optimization, MPS-SIAM Series on Optimization, SIAM, Philadelphia, 2009”. 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.

Course Description

Often 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.

Who Should Attend

This 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.

How You Will Benefit

• Identify opportunities for coordination within organizations and collaboration across organizations for increased efficiency and improved outcomes.
• Describe the strategic behavior of decision-makers and the impact of the market (or contract) structure on the participant's actions and the overall system dynamics.
• Define evaluation metrics in alignment with the system goals and structure system operations and incentives that address and evaluate these metrics.

What Is Covered

• How coordination and collaboration can improve supply chain efficiency and effectiveness
• How events, decisions and actions in one part of a system, such as a supply chain, impact other parts of the system
• System-wide inventory variability and costs mitigation and reduction
• Evaluation metrics

About the Course and the HHSCM Course Series

This course is the first in a 3-part virtually synchronous professional education program. Register and pay for all three required Health and Humanitarian Supply Chain Management Certificate courses and receive a discount of $400 off per course. Enter coupon code SCL-HHS at checkout with the Georgia Tech Professional Education website..  

Additionally, there are scholarships available for the certificate program. Apply at https://chhs.gatech.edu/course-scholarships by the noted deadline.  

Questions? Reach out to chhs@gatech.edu!

Course Description

Meeting 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.

Who Should Attend

This 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.

How You Will Benefit

• Provide immediate impact to your organization through learnings gained from applied and real-world case studies.
• Learn to bring NGOs, businesses, or government entities together to enhance collaboration, cooperation, and communication.
• Discover current trends and procedures to help organizations and team members get and stay ahead of the curve.
• Build a critical knowledge base to make tactical decisions around inventory, routing, and distribution.
• Deliver best practices to measure and evaluate the efficiency, impact, and outcomes of focused initiatives or ongoing logistics and supply chain operations.
• Transform the health and humanitarian sectors with increased capacity to participate in planning and strategic decision-making for effective supply-chain management.

What Is Covered

• Network modeling approaches
• Forecasting techniques
• Strategies for making decisions under uncertainty
• Other data-driven analytical approaches

About the Course and the HHSCM Course Series

This course is the first in a 3-part virtually synchronous professional education program. Register and pay for all three required Health and Humanitarian Supply Chain Management Certificate courses and receive a discount of $400 off per course. Enter coupon code SCL-HHS at checkout with the Georgia Tech Professional Education website..  

Additionally, there are scholarships available for the certificate program. Apply at https://chhs.gatech.edu/course-scholarships by the noted deadline.  

Questions? Reach out to chhs@gatech.edu!

Generative AI Enabled Semantic Communication

Abstract:

Semantic 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.

Bio: 

Zhu 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&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.

The requirement for high levels of customer service, increasing numbers of Stock Keeping Units (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 “ABC” classification of SKUs, which treats all those in a category as if they were identical. Instead, each decision, such as where to store or where to pick product, must be based on careful engineering and economic analysis.

Who Should Attend

This course is designed for supply chain and logistics consultants, supply chain engineers and analysts, facility engineers, and warehouse supervisors and team leaders.

How You Will Benefit

• Exchange space for time (or vice versa) to better meet business objectives.
• Understand when to use dedicated storage and when to use shared storage.
• Identify the most convenient locations in a warehouse based on an economic model.
• Identify patterns in customer orders and exploit these to speed fulfillment.
• Evaluate warehouse performance.
• Optimally size and stock a forward pick area.
• Understand the best practices in order-picking.

What You Will Learn

• Warehouse performance
• Modern warehouse trade-offs
• Size and stocking optimization
• Order-picking best practices
• Automation

Abstract:

Geometric 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.

Bio:

Dr. 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 "Domain-informed Machine Learning for Smart Manufacturing", 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.

Abstract:

Network 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.

Bio:

Dr. Peihua Qiu is the Dean’s 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–Madison 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 (2014–2016) and is the 2024 recipient of the Shewhart Medal.

Abstract: 

Distributed 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' 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’s 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. 

Bio: 

Ermin 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. Her team won the 2nd place in the GO-competition Challenge 1, an electricity grid optimization competition organized by Department of Energy. Wei's research interests include distributed optimization methods, convex optimization and analysis, smart grid, communication systems and energy networks and market economic analysis.

This course will demonstrate how warehouse operations are a key enabler to a successful supply chain implementation and the starting point for a transformation. It is critical to manage safety, quality and efficiency. Learn to leverage the lean supply chain modifications to improve customer responsiveness and reduce operating costs and in doing so contributing to a supply chain that creates a competitive advantage for a company. To accomplish this goal, we must bring lean principles into the warehouse and distribution center.

Who Should Attend

Supply chain professionals, logistics professionals, material managers, production control managers, transportation managers, warehousing managers and purchasing managers

How You Will Benefit

Upon completion of this course, you will be able to:

• Isolate the key elements of lean thinking to be used in the warehouse
• Value stream map warehouse operations
• Utilize lean tools to reduce waste in the warehouse
• Create a warehouse operation based on visual management and real time problem solving
• Reduce inventories in warehouse operations
• Create collaboration between warehousing and other functional areas

Benefits:

• Reduce inventories in the warehouse
• Reduce warehousing costs by as much as 25%
• Implement lean in the warehouse
• Create logistics collaboration between warehousing and other functional areas

What is Covered

• Lean Warehouse Overview
• Supply Chain Implementation Framework
• Lean Storage Planning Approach
• Application of a Lean Storage Location Sizing Method
• JIT Implementation Approach
• How To Develop Standard Work Batches
• Generation of an Operational Diagram
• Creation of a Daily Operational Work Load Plan
• Development of a Progress Control Board

Summary:
This seminar examines why exponential quantum advantages for NP-hard discrete optimization remain elusive and explores potential explanations and workarounds through the Maximum Cut problem.

Abstract:
Quantum 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.

Speaker Bio:
Ojas 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 (far-qu.sandia.gov), 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.

Thursday, November 6, 2025 | 12-1pm ET

Register Online to Attend

Title: 

New Applications and Algorithms of Distributionally Robust Optimization in AI

Abstract:

In 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–image pretraining (CLIP) models. I will introduce a new learning framework, DRRHO risk minimization, 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.

Bio: