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SUMMARY:S&DS Seminar: Sinho Chewi (MIT)
DTSTART:20230215T210000Z
DTEND:20230215T220000Z
DTSTAMP:20230202T192033Z
LAST-MODIFIED:20230206T192719Z
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LOCATION:Mason Lab 211 with remote access option, 9 Hillhouse Avenue, New Haven, CT 06520
DESCRIPTION:\nSpeaker: Sinho Chewi\, Mathematics\, Probability & Statistics\, MIT\n\n\n\nTowards a theory of complexity of sampling\, inspired by optimization\n\n\n\nAbstract: Sampling is a fundamental and widespread algorithmic primitive that lies at the heart of Bayesian inference and scientific computing\, among other disciplines. Recent years have seen a flood of works aimed at laying down the theoretical underpinnings of sampling\, in analogy to the fruitful and widely used theory of convex optimization. In this talk\, I will discuss some of my work in this area\, focusing on new convergence guarantees obtained via a proximal algorithm for sampling\, as well as a new framework for studying the complexity of non-log-concave sampling.\n\n\n\nBio: I am an Applied Mathematics PhD candidate at the Massachusetts Institute of Technology (MIT)\, advised by Philippe Rigollet. I received my B.S. in Engineering Mathematics and Statistics from University of California\, Berkeley in 2018. In Fall 2021\, I participated in the Simons Institute program on Geometric Methods in Optimization and Sampling and co-organized (with Kevin Tian) a working group on the complexity of sampling. In Spring 2022\, I visited Jonathan Niles-Weed at New York University (NYU). In Summer 2022\, I was a research intern at Microsoft Research\, supervised by Sébastien Bubeck and Adil Salim\n\n\n\n\n\nWednesday\, February 15\, 2023\n\n\n\n3:30pm - Pre-talk meet and greet teatime - Dana House\, 24 Hillhouse Avenue\n\n\n\n4:00pm - 5:00 pm - Talk - Mason Lab 211\, 9 Hillhouse Avenue\n\n\n\n\nWatch\n\n\n\n
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