Welcome to the Uncertainty Quantification Group, in the Department of Aeronautics and Astronautics at MIT. We are part of the Aerospace Computational Design Laboratory, as well as the MIT Center for Computational Science and Engineering and the MIT Statistics and Data Science Center, both within the MIT Schwarzman College of Computing.

Research Overview

Our research focuses on advancing foundational computational methodologies for uncertainty quantification, statistical inference, and machine learning in complex physical systems. Our methodological work is motivated by a wide variety of engineering and environmental applications.

We tackle a broad range of projects, but most involve aspects of a few core questions:

  • How to quantify confidence in computational predictions?
  • How to build or refine models of complex physical processes from indirect and limited observations?
  • What information is needed to drive inference, design, and control?

Featured Publications

J. Zech and Y. M. Marzouk, Sparse approximation of triangular transports on bounded domains,Preprint, (2020).
A. Spantini, R. Baptista and Y. M. Marzouk, Coupling techniques for nonlinear ensemble filtering,Preprint, (2019).
M. Parno and Y. M. Marzouk, Transport map accelerated Markov chain Monte Carlo,SIAM/ASA Journal on Uncertainty Quantification, 6 (2018), pp. 645–682.
A. Spantini, D. Bigoni and Y. M. Marzouk, Inference via low-dimensional couplings,The Journal of Machine Learning Research, 19 (2018), pp. 1–71.
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