## Introduction

Welcome to the Uncertainty Quantification Group, in the Department of Aeronautics and Astronautics at MIT. We are part of the Aerospace Computational Design Laboratory and affiliated with the Center for Computational Engineering.

#### Research Overview

Our research focuses on advancing fundamental computational methodology for uncertainty quantification and statistical inference in complex physical systems, and using these tools to address challenges in modeling energy conversion 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

*Coupling techniques for nonlinear ensemble filtering,*Preprint, (2019).

*Certified dimension reduction in nonlinear Bayesian inverse problems,*Preprint, (2018).

*Transport map accelerated Markov chain Monte Carlo,*SIAM/ASA Journal on Uncertainty Quantification, 6 (2018), pp. 645–682.

*Inference via low-dimensional couplings,*The Journal of Machine Learning Research, 19 (2018), pp. 1–71.

##### Announcements

**April 2020**

Congratulations to Robert Ren for winning an NSF Graduate Research Fellowship!

**November 2019**

Congratulations to Jakob Zech for accepting a faculty position in the Institute of Applied Mathematics at the University of Heidelberg!

**September 2019**

Congratulations to Ben Zhang for winning the MathWorks Fellowship from the MIT School of Engineering.

**May 2019**

Congratulations to Zheng Wang for successfully defending his PhD thesis!

**February 2019**

Congratulations to Fengyi Li for passing her doctoral qualifying exams.

**May 2018**

Congratulations to Andrew Davis for successfully defending his PhD thesis!