World Statistics Day Seminar

Speaker:Youssef Marzouk, Professor in the Department of Aeronautics and Astronautics at MIT and co-director of the MIT Center for Computational Science and Engineering

Talk Title: “Scaling up the black box: simulation-based inference, transport, and dimension reduction”

Abstract: Many practical Bayesian inference problems fall into the simulation-based or “likelihood-free” setting, where evaluations of the likelihood function or prior density are intractable; instead one can only draw samples from the joint parameter-data prior. Conditional sampling becomes the key computational challenge in this setting. Transportation of measure offers a unifying framework for tackling this challenge, encompassing an enormous variety of contemporary algorithms. Scaling these algorithms to high dimensional parameters and data, however, requires exploiting the prospect of low-dimensional structure. Our recent work has shown how to identify maximally informative (and informed) low-dimensional projections of the data (and parameters), and obtain error bounds on the resulting posterior approximations, via gradient-based dimension reduction. We then propose a framework, derived from score-matching, to extend these dimension reduction methods to the simulation-based setting where gradients are unavailable. I will showcase examples of this approach in inverse problems, data assimilation, and energy market modeling.

Bio: Youssef Marzouk is a professor in the Department of Aeronautics and Astronautics at MIT, and Co-director of the MIT Center for Computational Science & Engineering. His research interests lie at the intersection of computation and statistical inference with physical modeling. He develops new methodologies for uncertainty quantification, Bayesian modeling and computation, data assimilation, experimental design, and machine learning in complex physical systems.

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