Many of today's most important applications rely on high-performance manipulation of matrices, tensors, and general arrays of data. The neural networks at the heart of AI language models are a prime example. The AI software stack relies on kernels (e.g., for matrix-matrix product) that are specifically designed to deliver throughput close to the hardware's theoretical peak. However, writing kernels that can deliver maximum performance is extremely challenging. In this talk, I will discuss some of the work we have done to simplify the task of authoring such kernels. This includes new abstractions to allow for algebraic manipulation of array layouts and new programming models for more easily expressing the kernels themselves.
Michael Garland has been a researcher at NVIDIA since 2006. He joined the company as one of the founding members of NVIDIA Research and is currently Senior Director of Programming Systems Research. He leads a research group focused on developing technologies that will help programmers take advantage of modern high-performance machines. Their work spans the software stack with a particular focus on parallel algorithms, programming languages, compilers and runtime systems, and low-level hardware/software interfaces. He and his team have both published numerous academic articles and contributed to many software packages in broad use by CUDA developers.
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.
The talk aims to offer perspective on when and how statistical rigor and deep learning flexibility each contribute to reliable disease forecasting.
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.
To attend, please register online via Zoom.
]]>AHEAD of Time: Toward Robots That Behave Like Human Companions
George W. Woodruff School of Mechanical Engineering (Georgia Tech College of Engineering), Symbiotic and Augmented Intelligence Laboratory (SAIL)
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.
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.
]]>LitForager: Exploring Multimodal Literature Foraging Strategies in Immersive Sensemaking
IVI Lab (Immersive Visualization & Interaction Lab), School of Interactive Computing (Georgia Tech College of Computing)
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.
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.
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.
]]>Drawing on her research in pricing, revenue management, and market design, Dr. Wang explores how leaders can make better pricing and selling decisions when market data is limited, noisy, or constantly changing. Her work focuses on simple, practical strategies that remain effective under uncertainty, helping organizations improve revenue outcomes and design more reliable market mechanisms. This session will offer a timely look at how rigorous analytics can guide smarter decisions in imperfect real-world conditions.
Featuring Dr. Shixin Wang, Tennenbaum Early Career Professor and Assistant Professor in the H. Milton Stewart School of Industrial and Systems Engineering (ISyE) at Georgia Institute of Technology. Prior to joining Georgia Tech, Dr. Wang was an Assistant Professor in the Department of Decisions, Operations and Technology at The Chinese University of Hong Kong (CUHK) Business School from 2021 to 2025. Her research interests lie in developing simple and robust pricing policies in revenue management, and designing sparse and reliable networks in supply chain and service systems.
Classical complexity theory characterizes the difficulty of optimization problems through global worst-case parameters---Lipschitz constants, smoothness, dimension---and derives bounds that are tight over the entire problem class. Yet algorithms routinely outperform these predictions in practice, prompting the question of which other structural properties may determine algorithm efficiency. In this talk, I will present a line of research showing that local structural properties of optimization problems can reveal tractability hidden by worst-case analysis. I will discuss two interconnected threads. The first develops a new complexity framework based on local subgradient variation, which captures when optimization is substantially easier than global bounds suggest. A striking consequence is that parallelization can provably accelerate convex optimization for broad problem classes---including those underlying classical lower bounds---overturning long-standing conventional wisdom. The second thread applies a similar "closer look" philosophy to robust learning: I will discuss how local geometric structure enables polynomial-time algorithms with best-possible error guarantees for learning generalized linear and single-index models under adversarial noise and distributional shifts. I will close with future directions toward a broader theory of optimization that explains efficiency beyond the worst case.
]]>Optimization Meets Participation: Iterative School Zone Generation with LLMs
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.
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
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)
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.
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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)
]]>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.
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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.
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.
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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.
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.
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.
]]>XR for the Sky: Enhancing UAV and UAM Operations
Georgia Tech, College of Engineering : DR-CSE-AE, Labs: ASDL and CCG (Aerospace Systems Design Laboratory and Contextual Computing Group)
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.
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
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.
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).
]]>We strongly encourage you to attend to seek full-time employment, internships, and projects (rather than waiting until the end of the semester).
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!
Visit https://www.scl.gatech.edu/outreach/supplychainday.
]]>Motion, Heart Rate, and Haptics: Using XR devices for Human Monitoring and Regulation
School of Interactive Computing (Georgia Tech College of Computing), Georgia Tech Sonification Lab
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.
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.
]]>Simulating the Future: Experience Prototyping for Emerging Technologies via Multimodal XR
School of Psychology (Georgia Tech College of Sciences), Georgia Tech Sonification Lab
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.
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.
]]>Human-in-the-Loop and XR for Context-Rich Industrial Decision-Making
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.
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.
Solving the Mysteries of Place Cells and Grid Cells by Representation Learning
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.
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.
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.
Making AI Impactful in Healthcare
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.
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].
]]>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.
]]>Thursday, March 5, 2026 | 12-1pm ET
Electric vehicles are reshaping freight networks, charging demand is growing faster than infrastructure can keep up, and companies are trying to understand how electrification will change the cost and design of their supply chains. Georgia Tech Assistant Professor, Dr. Constance Crozier’s research tackles these questions with models that capture grid constraints, charging behavior, transportation patterns, and the economic tradeoffs behind electrified logistics. In this session, she will walk through the forces that will define the future of freight electrification.
Thursday, April 2, 2026 | 12-1pm ET
AI initiatives often struggle not because of model sophistication, but because the underlying data supply chain has not been mapped, synchronized, or aligned with operational decision-making. Just as a physical supply chain cannot function without visibility, coordination, and flow from source to destination, AI systems cannot create value when data sources are siloed, inconsistently governed, or disconnected from the “last mile” of decision execution. Even well designed models stall when the upstream data inputs are unreliable or the downstream decision processes are unclear.
This talk reframes AI implementation through the lens of the hidden data supply chain that propels it - from data sourcing and acquisition, to transformation, integration, governance, and delivery into decision environments. We will explore the continuous loop between data engineering and AI development, showing how model requirements should shape data architecture from the outset, and why data pipelines must be engineered as critical infrastructure rather than reactive fixes. Participants will leave with a practical framework for mapping their data supply chain, identifying bottlenecks and failure points in the data-to-decision flow, and building resilient data architectures that support reliable, explainable, and production ready AI systems.
Featuring Rosemarie Santa González, Ph.D., Research Scientist in the Institute for Robotics and Intelligent Machines (IRIM) and the NSF AI‑CARING Institute at Georgia Tech, and an instructor in the SCL professional education program.
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.
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