Applied Mathematics Colloquium with Youssef Marzouk, MIT

Monday, December 5, 2022
11:30 AM - 12:30 PM
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At this time, only Columbia University ID-holders and approved guests are invited to in-person talks. All others are invited to attend remotely.  Please contact [email protected] for the Zoom link.


Speaker: Youssef Marzouk, Massachusetts Institute of Technology

 

Title: "Transport methods for simulation-based Bayesian inference and data assimilation"

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 unavailable or intractable; instead one can only draw samples from the joint parameter-data prior. Learning conditional distributions is essential to the solution of these problems. To this end, I will discuss a powerful class of methods for conditional density estimation and conditional simulation based on transportation of measure. An important application for these methods lies in data assimilation for dynamical systems, where transport enables new approaches to nonlinear filtering and smoothing. I will also present related methods for joint dimension reduction of data and parameters in data assimilation and other non-Gaussian inference problems. Time permitting, I will also discuss some new results on the statistical convergence of transport-based density estimators.

This is joint work with Ricardo Baptista, Max Ramgraber, Alessio Spantini, Sven Wang, and Olivier Zahm.

Biography: Youssef Marzouk is a Professor in the Department of Aeronautics and Astronautics at the Massachusetts Institute of Technology (MIT), and co-director of the MIT Center for Computational Science and Engineering, within the Schwarzman College of Computing. He is also a core member of MIT's Statistics and Data Science Center. His research interests lie at the intersection of statistical inference, computational mathematics, and physical modeling.  His recent research efforts have 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, stochastic modeling, and machine learning. 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 occasional classical pianist.

Event Contact Information:
APAM Department
[email protected]
LOCATION:
  • Morningside
TYPE:
  • Lecture
CATEGORY:
  • Engineering
EVENTS OPEN TO:
  • Alumni
  • Faculty
  • Graduate Students
  • Postdocs
  • Staff
  • Students
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