S&DS Seminar: Sinho Chewi (MIT)

Towards a theory of complexity of sampling, inspired by optimization

Abstract: 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.

Bio: 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