Events Calendar

Computational Science Distinguished Seminar Series

Speaker: Youssef Marzouk, MIT

Talk Title: Transport methods for Bayesian inference and optimal experimental design

Abstract: Measure transport has emerged as a versatile tool in probabilistic modeling and inference, offering a unifying perspective on various computational challenges. This talk explores the core principles of transport maps and their ability to induce couplings between probability measures, facilitating efficient simulation and analysis. We will survey the diverse landscape of transport representations, from polynomials and invertible neural networks to ODE flow maps, highlighting how these constructions capture different notions of low-dimensional structure in probabilistic models.
The presentation will then focus on recent advancements in two key areas:
1. Nonlinear ensemble filtering: This talk explores novel transport-based algorithms that generalize the ensemble Kalman filter to nonlinear settings, offering improved performance in challenging filtering problems.
2. Simulation-based inference: We will investigate how transport maps can be leveraged to enhance the efficiency and accuracy of inference in scenarios where only forward simulations are available.
Additionally, this talk explores the application of transport-based density estimates in bounding information-theoretic objectives for optimal experimental design, demonstrating the broad utility of this framework in decision-making under uncertainty.

Biography: Youssef Marzouk is the Breene M. Kerr (1951) Professor of Aeronautics and Astronautics at the Massachusetts Institute of Technology (MIT), and co-director of the Center for Computational Science and Engineering within the MIT Schwarzman College of Computing. He is also a core member of MIT's Statistics and Data Science Center and a PI in the MIT Laboratory for Information and Decision Systems (LIDS).
His research interests lie at the intersection of statistical inference, computational mathematics, and physical modeling. He develops new methodologies for uncertainty quantification, Bayesian computation, and machine learning in complex physical systems, motivated by a broad range of engineering and science applications. His recent work has centered on algorithms for inference, with applications to data assimilation and inverse problems; dimension reduction methodologies for high-dimensional learning and surrogate modeling; optimal experimental design; and transportation of measure as a tool for inference and stochastic modeling.
He received his SB, SM, and PhD degrees from MIT and spent four years at Sandia National Laboratories before joining the MIT faculty in 2009. He is also an avid coffee drinker and an occasional classical pianist.

Host: The School of Advanced Computing

More Info: https://sac.usc.edu/events/

Location: Ronald Tutor Hall of Engineering (RTH) - 526

Audiences: Everyone Is Invited

Contact: Tessa Yao

Event Link: https://sac.usc.edu/events/