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DTSTAMP:20260428T181850
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UID:10001389-1768219200-1768222800@engineering.wisc.edu
SUMMARY:ISyE - Choice-based Operations At Scale: Complementarity and Dynamic Decisions
DESCRIPTION:Assortment and inventory decisions lie at the core of supply chain and retail operations. In practice\, these decisions face two fundamental challenges arising from complex customer choice behavior. First\, customers often purchase complementary products across categories\, which makes category-level decisions interdependent. Second\, inventory is limited and customers arrive over time\, so product availability changes dynamically as items stock out. Most work in choice-based operations has focused on single-category and static settings\, while research addressing these two challenges remains relatively limited. Existing approaches often either oversimplify customer preferences in choice modeling or rely on algorithms that are not tractable in large-scale settings. In this talk\, I will present two projects that address these challenges. The first proposes a Markovian framework to model cross-category complementarity\, supporting scalable estimation and joint assortment optimization. The second introduces a unified algorithmic framework for dynamic assortment and inventory optimization under MNL choice\, with provable guarantees in both personalized and non-personalized settings. Together\, these works offer scalable tools for decision-making in complex\, data-driven supply chain environments. \n\n\n\n\n\nBio: Shuo Sun is a PhD candidate in Industrial Engineering and Operations Research at the University of California\, Berkeley. Her research focuses on modeling and algorithm design for supply chain and revenue management using optimization and machine learning. Her work has received several recognitions\, including the INFORMS Daniel H. Wagner Prize and a finalist distinction in the INFORMS RMP Jeff McGill Best Student Paper Award. She has publications in leading conferences and papers published or under revision at leading operations and analytics journals\, as well as industry experience at Amazon and JD.com on retail operations problems.
URL:https://engineering.wisc.edu/event/isye-colloquium-shuo-sun/
LOCATION:1163 Mechanical Engineering\, 1513 Engineering Dr.\, Madison\, WI\, 53706\, United States
CATEGORIES:Colloquium,Industrial & Systems Engineering
ATTACH;FMTTYPE=image/png:https://engineering.wisc.edu/wp-content/uploads/2026/01/sungraphic.avif
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BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20260115T120000
DTEND;TZID=America/Chicago:20260115T130000
DTSTAMP:20260428T181850
CREATED:20260106T182252Z
LAST-MODIFIED:20260108T134323Z
UID:10001390-1768478400-1768482000@engineering.wisc.edu
SUMMARY:ISyE - Auto-Conditioned First-Order and Stochastic Optimization Methods
DESCRIPTION:First-order methods are widely used to tackle data science and machine learning problems with complex structures\, such as nonconvexity\, nonsmoothness\, and stochasticity. However\, in many real-world scenarios\, the problem structure and parameters can be unknown or ambiguous\, creating significant challenges for algorithm design and stepsize selection. \n\n\n\nIn this talk\, I will present a novel class of first-order methods\, termed auto-conditioned methods\, that are universal for solving various classes of optimization problems without requiring prior knowledge of problem parameters or resorting to any line search or backtracking procedures. In the first part of the talk\, we focus on convex optimization and propose a uniformly optimal method for smooth\, weakly smooth\, and nonsmooth problems. In the second part of the talk\, we consider smooth but possibly nonconvex optimization\, and propose a novel parameter-free projected gradient method with the best-known unified complexity for convex and nonconvex problems. We then generalize the method to the stochastic setting\, achieving new universal complexity bounds that are nearly optimal for both convex and nonconvex problems. The advantages of the proposed methods are demonstrated by encouraging numerical results. \n\n\n\n\n\nBio: Tianjiao Li is a postdoctoral associate at the MIT Sloan School of Management and Operations Research Center. He received his Ph.D. in Operations Research from the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Tech\, where he was advised by Prof. George Lan and Prof. Ashwin Pananjady. His research interests lie in the theory and methodology of nonlinear optimization\, stochastic optimization\, and reinforcement learning\, with a central focus on bridging rigorous theoretical development with practical relevance\, especially in data science and artificial intelligence. His work has been recognized as an honorable mention in the INFORMS George Nicholson Student Paper Competition and as second place in the INFORMS Optimization Society Student Paper Award.
URL:https://engineering.wisc.edu/event/isye-auto-conditioned-first-order-and-stochastic-optimization-methods/
LOCATION:1163 Mechanical Engineering\, 1513 Engineering Dr.\, Madison\, WI\, 53706\, United States
CATEGORIES:Colloquium,Industrial & Systems Engineering
ATTACH;FMTTYPE=image/png:https://engineering.wisc.edu/wp-content/uploads/2026/01/LIgraphic.avif
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BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20260121T120000
DTEND;TZID=America/Chicago:20260121T130000
DTSTAMP:20260428T181850
CREATED:20260113T150154Z
LAST-MODIFIED:20260113T150157Z
UID:10001398-1768996800-1769000400@engineering.wisc.edu
SUMMARY:ISyE - GPU-Accelerated Linear Programming and Beyond
DESCRIPTION: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. \n\n\n\n\n\nBio: 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.
URL:https://engineering.wisc.edu/event/isye-gpu-accelerated-linear-programming-and-beyond/
LOCATION:1163 Mechanical Engineering\, 1513 Engineering Dr.\, Madison\, WI\, 53706\, United States
CATEGORIES:Colloquium,Industrial & Systems Engineering
ATTACH;FMTTYPE=image/png:https://engineering.wisc.edu/wp-content/uploads/2026/01/yanggraphic.avif
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20260126T120000
DTEND;TZID=America/Chicago:20260126T130000
DTSTAMP:20260428T181850
CREATED:20260114T182823Z
LAST-MODIFIED:20260114T182825Z
UID:10001399-1769428800-1769432400@engineering.wisc.edu
SUMMARY:ISyE - From Dyads to Teams: Modeling Human Trust Dynamics and Behaviors in Human-Autonomy Interactions
DESCRIPTION:Technology is evolving rapidly\, and human interaction with autonomous technologies is no longer confined to one-to-one decision-support settings. Intelligent agents are increasingly working alongside groups of people in diverse contexts such as defense\, transportation\, and manufacturing. Consequently\, there is a growing need to design trust- and behavior-aware adaptive agents that allow humans and autonomous systems to leverage their complementary strengths\, while promoting values unique to human society. To this end\, our research focuses on modeling human trust and behavioral dynamics over time across various teaming scenarios. In this seminar\, I will first introduce our work on classifying and predicting trust dynamics profiles using individuals’ personal characteristics\, uncovering who exhibits which trust trajectory and why. I will then present a study examining individual variability in trust bias (contrast versus assimilation) and its effects on coordination and decision-making when humans collaborate with multiple autonomous agents simultaneously. This work further extends to understanding distinct perceptions and teamwork strategies in hierarchical mixed human-agent teams. Time permitting\, I will also discuss our research on the design of AI-supported emergency navigation systems that promote human altruism and trust.   \n\n\n\n\n\nBio: Hyesun Chung is a final-year Ph.D. candidate in Industrial and Operations Engineering at the University of Michigan. She was recently named a Barbour Fellow and received the Student Member with Honors Award from the Human Factors and Ergonomics Society (HFES). Prior to joining the University of Michigan as a doctoral student\, she earned three bachelor’s degrees in Industrial Engineering\, Business\, and Industrial Design\, as well as an M.S. in Industrial Engineering\, all from Seoul National University in South Korea. Building on her interdisciplinary background\, she is a human factors engineer and human-computer interaction researcher who integrates computational and statistical methods with psychological theory to better understand and design human-AI interaction and teaming.
URL:https://engineering.wisc.edu/event/isye-from-dyads-to-teams-modeling-human-trust-dynamics-and-behaviors-in-human-autonomy-interactions/
LOCATION:1163 Mechanical Engineering\, 1513 Engineering Dr.\, Madison\, WI\, 53706\, United States
CATEGORIES:Colloquium,Industrial & Systems Engineering
ATTACH;FMTTYPE=image/png:https://engineering.wisc.edu/wp-content/uploads/2026/01/chunggraphic.avif
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BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20260129T120000
DTEND;TZID=America/Chicago:20260129T130000
DTSTAMP:20260428T181850
CREATED:20260121T184709Z
LAST-MODIFIED:20260121T184711Z
UID:10001441-1769688000-1769691600@engineering.wisc.edu
SUMMARY:ISyE - Finding the needle in the haystack: How gradient descent converges to low-dimensional solutions in over-parameterized models.
DESCRIPTION:In contemporary machine learning\, realistic models exhibit increasing nonconvexity and overwhelming overparameterization. This nonconvex nature often leads to numerous undesirable or “spurious” local solutions\, while overparameterization exacerbates the risk of overfitting. Yet\, simple “short-sighted” algorithms\, such as gradient descent (GD) or its variants\, often find the needle in the haystack: they converge to the correct\, low-dimensional solutions even when such structures are neither explicitly encoded in the model nor required by the algorithm. This talk delves into explaining this desirable performance of GD-based algorithms by studying their fine-grained trajectory on over-parameterized models\, spanning from low-rank models to deep neural networks.   \n\n\n\n\n\nBio: Salar Fattahi is an Assistant Professor of Industrial and Operations Engineering at the University of Michigan. He received his Ph.D. from the University of California\, Berkeley in 2020. He is the recipient of a National Science Foundation CAREER Award and the Deans’ MLK Spirit Award. His research focuses on optimization and machine learning and has been recognized with multiple nominations and awards\, including the INFORMS Junior Faculty Interest Group Best Paper Award\, the INFORMS Data Mining Best Paper Award\, and the INFORMS Computing Society Best Student Paper Award. He currently serves as Vice Chair for Machine Learning in the INFORMS Optimization Society\, as an Associate Editor for the INFORMS Journal on Data Science\, and as an Area Chair for several premier conferences\, including NeurIPS\, ICML\, and ICLR.
URL:https://engineering.wisc.edu/event/isye-finding-the-needle-in-the-haystack-how-gradient-descent-converges-to-low-dimensional-solutions-in-over-parameterized-models/
LOCATION:2188 Mechanical Engineering Building\, 1513 University Avenue\, Madison\, WI\, 53706\, United States
CATEGORIES:Colloquium,Industrial & Systems Engineering
ATTACH;FMTTYPE=image/png:https://engineering.wisc.edu/wp-content/uploads/2026/01/fattahigraphic.avif
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20260130T120000
DTEND;TZID=America/Chicago:20260130T130000
DTSTAMP:20260428T181850
CREATED:20260122T132927Z
LAST-MODIFIED:20260122T144835Z
UID:10001442-1769774400-1769778000@engineering.wisc.edu
SUMMARY:ISyE - Advancement of Large-Scale 3D Printing
DESCRIPTION:Most advances for the integration of 3D printing (3DP) into production settings have focused on small-scale 3DP with manufacturing of components such as aerospace fuel nozzles\, aircraft’s hydraulic components\, and military hardware to name a few. But when it comes to large-scale applications 3DP research has been minimally explored. Defining large-scale 3DP as additive manufacturing technologies aimed at applications such as remanufacturing of large components in aerospace\, automotive\, and agricultural industries\, to construction and infrastructure\, research in large-scale 3DP is nascent and needed. Alongside cost\, establishment of 3DP technologies for large-scale applications has been hindered by the interdisciplinarity required to identify technological design needs\, manufacturing\, validation\, and implementation of these technologies while securing safety standards. Therefore\, this presentation will discuss outcomes of studies revolving around industrial and manufacturing engineering practices that can contribute to advancing 3DP technologies for large-scale 3DP applications. Concluding remarks will suggest to opportunities that can lead to identification of interdisciplinary research collaborations to further advance the field. \n\n\n\n\n\nBio: Dr. Iris V. Rivero is the Paul and Heidi Brown Preeminent Chair and Department Chair of Industrial and Systems Engineering at the University of Florida (UF). She received her B.S.\, M.S.\, and Ph.D. in Industrial & Manufacturing Engineering from Penn State University. Her research group\, iMED (Interdisciplinary Manufacturing Engineering and Design) laboratory\, specializes in the design and validation of additive and hybrid manufacturing techniques for the processing of a wide array of material systems ranging from biopolymers\, metal alloys\, to concrete. She was a faculty fellow at NASAs Marshall Space Flight Center\, and her research has been funded by the National Science Foundation\, Department of Energy\, and NASA\, to name a few. She has over 90 peer-reviewed publications and over 100 invited talks and peer-reviewed presentations. She is a fellow in the Institute of Industrial and Systems Engineers (IISE) and in SME (formerly the Society of Manufacturing Engineers).
URL:https://engineering.wisc.edu/event/isye-finding-the-needle-in-the-haystack-how-gradient-descent-converges-to-low-dimensional-solutions-in-over-parameterized-models-2/
LOCATION:1163 Mechanical Engineering\, 1513 Engineering Dr.\, Madison\, WI\, 53706\, United States
CATEGORIES:Colloquium,Industrial & Systems Engineering
ATTACH;FMTTYPE=image/png:https://engineering.wisc.edu/wp-content/uploads/2026/01/riverographic.avif
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