Distinguished Seminar in Optimization and Data: Philippe Rigollet (MIT)

Title: Statistical Applications of Wasserstein Gradient Flows
Speaker: Philippe Rigollet (MIT)
Date: Monday, May 22, 2023
Abstract: Otto calculus is a fundamental toolbox in mathematical optimal transport, imparting the Wasserstein space of probability measures with a Riemmanian structure. In particular, one can compute the Riemannian gradient of a functional over this space and, in turn, optimize it using Wasserstein gradient flows. The necessary background to define and compute Wasserstein gradient flows will be presented in the first part of the talk before moving to statistical applications such as variational inference and maximum likelihood estimation in Gaussian mixture models.
Bio: Philippe Rigollet is a Professor of Mathematics at MIT. He received his Ph.D. in mathematics from the University of Paris VI. in 2006. His work is at the intersection of statistics, machine learning, and optimization, focusing primarily on the design and analysis of statistical methods for high-dimensional problems. Rigollet's recent research focuses on statistical optimal transport and its applications to geometric data analysis and sampling. Please advertise it through your department's mailing list, and send it to anyone who may be interested.
This video is closed captioned.

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Wow, he lost so much weight!