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X-WR-CALDESC:Events for College of Engineering - University of Wisconsin-Madison
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DTSTAMP:20260403T194128
CREATED:20260105T133949Z
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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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DTSTART;TZID=America/Chicago:20260115T120000
DTEND;TZID=America/Chicago:20260115T130000
DTSTAMP:20260403T194128
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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DTSTART;TZID=America/Chicago:20260115T170000
DTEND;TZID=America/Chicago:20260115T190000
DTSTAMP:20260403T194128
CREATED:20251230T051841Z
LAST-MODIFIED:20251230T051844Z
UID:10001388-1768496400-1768503600@engineering.wisc.edu
SUMMARY:BME Bay Area Meetup
DESCRIPTION:We’re headed to the California Bay Area to kick off a new year of innovation. You and your guests are invited to join fellow Wisconsin BME alumni and friends for an alumni reception at Steins Beer Garden in Mountain View\, CA. \n\n\n\nRSVP
URL:https://engineering.wisc.edu/event/bme-bay-area-meetup-2/
LOCATION:Steins Beer Garden & Restaurant\, 895 Villa St\, Mountain View\, California\, 94041\, United States
CATEGORIES:Alumni events,Biomedical Engineering,Social Event
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ORGANIZER;CN="Department of Biomedical Engineering":MAILTO:bmehelp@bme.wisc.edu
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