CS Events

Computer Science Department Colloquium Towards Optimal Sampling Algorithms
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Speaker: Thuy-Duong “June” Vuong

Bio

Thuy-Duong “June” Vuong is a 5th year PhD student at Stanford. Her research is in designing and analyzing algorithms for sampling from complex high-dimensional distributions, with a focus on Markov chains analysis. Her work gives the optimal bounds on the runtime of Markov chains using entropy analysis and the theory of high-dimensional expanders. She received Bachelor of Science degrees in Mathematics and Computer Science at the Massachusetts Institute of Technology. Her research is supported by a Microsoft Research PhD fellowship.

Location : CoRE 301

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Event Type: Computer Science Department Colloquium

Abstract: Sampling is a fundamental task with applications in various areas including physics, statistics, and combinatorics. Many applications involve the challenging task of sampling from complex high-dimensional distributions. Markov chains are a widely adopted approach for tackling these critical problems, but current runtime analyses are suboptimal.In this talk, I will introduce “entropic independence”, a novel and powerful framework for analyzing Markov chains, and use it to obtain the tightest possible runtime bounds. My work gives the first near-linear time sampling algorithms for classical statistical physics models in the tractable regime, resolving a 70-year-old research program. My research results in highly practical algorithms and settles several long-standing open problems in sampling and approximate computing.

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Contact  Assistant Professor Aaron Bernstein

Join Zoom Meeting
https://rutgers.zoom.us/j/2014444359?pwd=WW9ybFNCNVFrUWlycHowSHdNZjhzUT09
Meeting ID: 201 444 4359
Password: 550978