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DTSTART;TZID=America/Chicago:20260121T120000
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DTSTAMP:20260428T152902
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:20260115T120000
DTEND;TZID=America/Chicago:20260115T130000
DTSTAMP:20260428T152902
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
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20260112T120000
DTEND;TZID=America/Chicago:20260112T130000
DTSTAMP:20260428T152902
CREATED:20260105T133949Z
LAST-MODIFIED:20260105T140449Z
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
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20251121T120000
DTEND;TZID=America/Chicago:20251121T130000
DTSTAMP:20260428T152902
CREATED:20251105T181612Z
LAST-MODIFIED:20251105T181614Z
UID:10001366-1763726400-1763730000@engineering.wisc.edu
SUMMARY:Closing the Loop on Machine Learning: A Perturbation Analysis Approach to Decision-Dependent Distribution Shift
DESCRIPTION:UW-ISyE looks forward to welcoming Roy Dong Assistant Professor in the Industrial & Enterprise Systems Engineering department at the University of Illinois at Urbana-Champaign. \n\n\n\nRoy Dong – research assistant professor\, electrical and computer engineering\n\n\n\nAs data-driven methods are deployed in real-world settings\, the processes that generate the observed data will often react to the decisions of the learner. For example\, a data source may have some incentive for the algorithm to provide a particular label (e.g. approve a bank loan)\, and manipulate their features accordingly. In this talk\, I will present our recent work on analyzing this decision-dependent distribution shift through the lens of perturbation analysis in control theory. This framework allows us to consider settings with multiple equilibria and characterize the regions of attraction for each equilibrium\, which is observed in practice: learning algorithms can settle into echo chambers\, and characterizes the set of initial conditions which leads to each ultimate outcome. Additionally\, I will discuss how these methods can be computationally calculated using integral quadratic constraints\, how they can be made distributionally robust\, and how it can be used for trajectory predictions for robotic crowd navigation. \n\n\n\n\n\nBio: Roy Dong is an Assistant Professor in the Industrial & Enterprise Systems Engineering department at the University of Illinois at Urbana-Champaign. He received a BS Honors in Computer Engineering and a BS Honors in Economics from Michigan State University in 2010. He received a PhD in Electrical Engineering and Computer Sciences at the University of California\, Berkeley in 2017\, where he was funded in part by the NSF Graduate Research Fellowship. Prior to his current position\, he was a postdoctoral researcher in the Berkeley Energy & Climate Institute\, a visiting lecturer in the Industrial Engineering and Operations Research department at UC Berkeley\, and a Research Assistant Professor in the Electrical and Computer Engineering department at the University of Illinois at Urbana-Champaign. His research uses tools from control theory\, economics\, statistics\, and optimization to understand the closed-loop effects of machine learning\, with applications in cyber-physical systems such as the smart grid\, modern transportation networks\, and autonomous vehicles.
URL:https://engineering.wisc.edu/event/closing-the-loop-on-machine-learning-a-perturbation-analysis-approach-to-decision-dependent-distribution-shift/
LOCATION:1163 Mechanical Engineering\, 1513 Engineering Dr.\, Madison\, WI\, 53706\, United States
CATEGORIES:Colloquium,Industrial & Systems Engineering
ATTACH;FMTTYPE=image/jpeg:https://engineering.wisc.edu/wp-content/uploads/2025/11/donggraphic.avif
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BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20251010T120000
DTEND;TZID=America/Chicago:20251010T130000
DTSTAMP:20260428T152902
CREATED:20250929T154550Z
LAST-MODIFIED:20250929T154957Z
UID:10001337-1760097600-1760101200@engineering.wisc.edu
SUMMARY:Understanding Generalization of Diffusion Models: Structured Data and Memorization
DESCRIPTION:UW-ISyE looks forward to welcoming Minshuo Chen\, assistant professor with the Department of Industrial Engineering & Management Sciences at Northwestern University \n\n\n\n\n\n\n\nDiffusion models achieve state-of-the-art performance in various high-dimensional data modeling tasks. These empirical successes challenge conventional wisdom while raising critical concerns. On the one hand\, in high-dimensional applications\, diffusion models’ strong performance appears to circumvent the curse of dimensionality. On the other hand\, memorization emerges as an unwanted byproduct\, limiting creativity and raising safety and privacy issues. In this talk\, we theoretically decipher these observations. The first part develops statistical learning guarantees of diffusion models for low-dimensional manifold data—an assumption aligns well with many practical datasets. We prove that diffusion models can learn data distributions at rates governed by the intrinsic dimension and curvature of the data. The second part establishes separation in memorization and generalization through the statistical learning and network approximation lens. Building on these insights\, we propose a pruning-based method that reduces memorization while maintaining generation quality. \n\n\n\n\n\nBio: Minshuo Chen is an assistant professor with the Department of Industrial Engineering & Management Sciences at Northwestern University. He was an associate research scholar with the Department of Electrical and Computer Engineering at Princeton University from 2022 to 2024. He completed his Ph.D. from the School of Industrial and Systems Engineering at Georgia Tech\, majoring in Machine Learning. His research focuses on developing principled methodologies and theoretical foundations of deep learning\, with a particular interest in 1) generative models including diffusion models\, 2) foundations of machine learning\, such as optimization and sample efficiency\, and 3) reinforcement learning.
URL:https://engineering.wisc.edu/event/understanding-generalization-of-diffusion-models-structured-data-and-memorization/
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/2025/09/chengraphic.avif
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20251003T120000
DTEND;TZID=America/Chicago:20251003T130000
DTSTAMP:20260428T152902
CREATED:20250918T131424Z
LAST-MODIFIED:20250918T193205Z
UID:10001332-1759492800-1759496400@engineering.wisc.edu
SUMMARY:Strong duals for mixed integer programs.
DESCRIPTION:UW-ISyE looks forward to welcoming Dr. Santanu Dey\, Professor at  H. Milton Stewart School of Industrial and Systems Engineering at Georgia Institute of Technology.  \n\n\n\n\n\n\n\nWe develop two general-purpose strong dual formulations for binary MINLPs\, motivated by sensitivity analysis and distributed computation. For mixed binary quadratic programs (MBQPs)\, we show that the copositive dual of Burer’s completely positive reformulation has no duality gap when the feasible region is bounded or the objective is convex. Since the right-hand side of the original MBQP appears only in the dual’s objective\, this formulation enables sensitivity analysis. For nearly decomposable nonlinear mixed binary programs\, we propose a hierarchy of relaxations that preserve decomposability. The first level coincides with the classical Lagrangian relaxation\, while higher levels yield progressively tighter bounds\, culminating in a strong dual. We analyze the quality of these bounds for various types of MILPs. This is joint work with Diego Cifuentes and Jingye Xu. \n\n\n\n\n\nBio: Santanu S. Dey is an Anderson-Interface professor and director of doctorial recruiting and admissions in the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Institute of Technology. Dr. Dey’s research interests are in the area of non-convex optimization\, and in particular mixed integer linear and nonlinear programming. His research is partly motivated by applications of non-convex optimization problems arising in areas such as electrical power engineering\, process engineering\, civil engineering\, logistics\, and statistics. He currently serves on the editorial board of Mathematical Programming A\, Mathematics of Operations Research and SIAM Journal on Optimization. He has previously served as an area editor for Mathematical Programming C and associate editor of INFORMS Journal on Computing. He has won the INFORMS Nicholson student paper competition\, IBM Faculty Award\, the Class of 1969 Teaching Fellow at Georgia Tech\, the NSF CAREER award\, the INFORMS Energy Natural Resources and Environment best paper award\, and the INFORMS optimization society Balas Prize.
URL:https://engineering.wisc.edu/event/strong-duals-for-mixed-integer-programs/
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/2025/09/deygraphic.avif
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20250926T120000
DTEND;TZID=America/Chicago:20250926T130000
DTSTAMP:20260428T152902
CREATED:20250903T155005Z
LAST-MODIFIED:20250918T163656Z
UID:10001309-1758888000-1758891600@engineering.wisc.edu
SUMMARY:Optimization\, ML and AI in Operations Management 
DESCRIPTION:UW-ISyE looks forward to welcoming Dr. Georgia Perakis\, Professor at the MIT Sloan School of Management. \n\n\n\n\n\n\n\nData-driven decision-making has garnered a growing interest due to the increase in data availability in recent years. With that growth many opportunities as well as challenges arise. Optimization\, Machine Learning (ML) and more generally\, AI play and can play even more an important role to address important challenges in a variety of Operations Management applications. In this talk\, we will discuss some of these applications and will highlight the importance and challenges of integrating optimization with ML in data-driven decision-making. We will also discuss some concrete examples of these synergies in pricing and healthcare. As a result\, we will also discuss how we can optimize over already trained objective functions that arise from neural network models in order to recommend better decisions. \n\n\n\n\n\nBio: Georgia Perakis is the William F. Pounds Professor and a Professor of Operations Management\, Operations Research & Statistics at the MIT Sloan School of Management. She is also serving as co-director of the Operations Research Center. On July 1 she started her sabbatical at Harvard Business School where she is spending the year as a Visiting Scholar. For the past year and a half\, she served as the John C Head III Dean (Interim) at MIT Sloan and before that\, she served as an Associate Dean for Social and Ethical Responsibility in Computing (SERC) in the Schwarzman College of Computing and MIT Sloan. Her research has received many awards and focuses on analytics/AI\, in particular\, in the intersection of optimization and machine learning with applications in pricing\, revenue management\, supply chain\, sustainability and healthcare among others. She received the PECASE Award from the Office of the President on Science and Technology. In 2016\, she was elected as an INFORMS Fellow\, and in 2021 as Distinguished MSOM Fellow.  \n\n\n\nPerakis has passion for supervising PhD\, masters\, and undergraduate students\, having graduated 34 PhD and 63 master’s students. She has received numerous awards for teaching including the Graduate Student Council Teaching Award (2002)\, the Samuel M. Seegal Award (2012)\, the Jamieson Prize for excellence in Teaching (2014)\, the Teacher of the Year Award (2017) and the Outstanding Teaching Award (2024) at MIT Sloan. Perakis is currently the Editor in Chief of the M&SOM journal and has served on the editorial board at a number of other journals. She holds a BS in mathematics from the University of Athens as well as an MS in applied mathematics and a PhD in applied mathematics from Brown University.
URL:https://engineering.wisc.edu/event/optimization-ml-and-ai-in-operations-management/
LOCATION:1163 Mechanical Engineering\, 1513 Engineering Dr.\, Madison\, WI\, 53706\, United States
CATEGORIES:Colloquium,Industrial & Systems Engineering
ATTACH;FMTTYPE=image/jpeg:https://engineering.wisc.edu/wp-content/uploads/2025/09/perakisgraphic.avif
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