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Uncertainty Quantification Group

Massachusetts Institute of Technology

Principal Investigator

Youssef Marzouk

Youssef Marzouk

Youssef Marzouk is the Breene M. Kerr (1951) Professor in Aeronautics and Astronautics at MIT and Associate Dean of the MIT Schwarzman College of Computing. He is also a PI in the MIT Laboratory for Information and Decision Systems (LIDS) and a core member of MIT's Statistics and Data Science Center. His research interests lie at the intersection of computational mathematics, statistical inference, and physical modeling. He develops new methodologies for uncertainty quantification, Bayesian computation, and machine learning, 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 generative 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 a Fellow of SIAM and an Associate Fellow of the AIAA. He is also an avid coffee drinker and an occasional classical pianist.

Postdoctoral Associates

Omar Al-Ghattas

Omar Al-Ghattas WWW CV

I am currently a Postdoctoral Fellow at the Eric and Wendy Schmidt Center at the Broad Institute of MIT and Harvard. My research aims to provide a rigorous statistical analysis of widely used algorithms in the areas of inverse problems, data assimilation, and computational biology. I am particularly interested in identifying the theoretical conditions that guarantee their effectiveness, especially in high-dimensional settings. I recently finished my PhD in Statistics at University of Chicago, working with Daniel Sanz-Alonso. In my free time, I enjoy reading about history, cooking, and exploring new coffee spots.

Ayoub Belhadji

Ayoub Belhadji WWW

I am interested in problems at the intersection of applied mathematics, signal processing, and machine learning. Currently, I research representations of sets and probability measures with applications in optimal experimental design. I completed my Ph.D. in theoretical signal processing and machine learning at Centrale Lille, under Pierre Chainais and Rémi Bardenet. My work includes subset selection, quadrature and interpolation methods using determinantal point processes. Before joining MIT, I collaborated with Rémi Gribonval on compressive learning using sketching methods at ENS de Lyon. In my free time, I enjoy reading, traveling, and playing tennis.

Zongyi Li

Zongyi Li WWW CV

I am a Postdoctoral Associate at MIT, CSAIL. My research interest lies at the intersection of machine learning and scientific computing. I have been working on neural operators for solving partial differential equations, with applications in weather forecast and computational fluid dynamics. I obtained my Ph.D. in computing and mathematical science from Caltech. In my free time, I enjoy hiking, music, cooking, and mushroom hunting.

Aimee Maurais

Aimee Maurais WWW CV

I develop and analyze computational methods for Bayesian inference, uncertainty quantification, and generative modeling. My current work focuses on dynamic measure transport algorithms for sampling in scientific settings like data assimilation. Prior to beginning my graduate work I received bachelor's degrees in Mathematics and Computational Modeling and Data Analytics from Virginia Tech (2019) and spent some time on the technical staff at MIT Lincoln Laboratory developing algorithms and software for anomaly detection. In my free time, I can often be found swimming, cooking, sewing, or reading.

Dan Waxman

Dan Waxman WWW CV

I am a postdoctoral research scientist at Basis, a non-profit research institute, and a Research Affiliate at MIT. My research interests are in Bayesian machine learning and statistics, with a particular focus on sequential learning, Gaussian processes, and dynamical systems. I'm also interested in developing open-source software in these areas. I previously completed my PhD at Stony Brook University in electrical engineering with Petar Djurić. In my free time, I like to read and drink (probably too much) coffee.

Spencer Wyant

Spencer Wyant

My research interests focus on the development of a codebase for atomistic simulations as part of the PSAAP project. This will include developing a toolkit of various active learning schemes to fit machine-learned interatomic potentials, as well as facilitating forward uncertainty propagation across multiscale simulations of complex materials processes. Prior to joining the group, I completed my PhD in materials science at MIT under Asegun Henry; my thesis work focused on improving molecular dynamics simulations of interfacial heat transport. Outside of work, I enjoy the standard affair of watching movies (especially on Criterion), reading, visiting art museums, and eating at restaurants.

Joanna Zou

Joanna Zou

My research interest is in the combination of statistical inference methods and first-principles physical modeling to improve prediction under uncertainty. Currently, I work on applying Bayesian inference techniques to atomistic modeling of materials in the CESMIX project. From my previous positions as a research fellow at TU Delft and MS student at Stanford, I have experience in data assimilation, surrogate modeling, and probabilistic risk analysis. Outside of work, I enjoy rock climbing, hiking, tennis, and live music.

Graduate Students, PhD

Andrey Bryutkin

Andrey Bryutkin

I focus on UQ of dynamical linear systems, parameter reduction, and interpretability. I am interested in applying physical principles, such as equivariance, to inference methods, and exploring UQ for deep learning and sampling. Before joining the MIT Math department, I studied theoretical physics at ETH Zurich, focusing on representation theory in CFT. I completed my master’s at the University of Cambridge (Part III), specializing in probability and statistics and focusing on developing new neural operator architectures. In my free time, I enjoy playing squash and tennis.

Katharine Fisher

Katharine Fisher

My current research is focused on uncertainty quantification for density functional theory. Broadly, my interests include Bayesian inference, multifidelity modeling, and uncertainty propagation over multiple scales. I graduated from the University of Texas at Austin with bachelor’s degrees in computational engineering and mathematics. I completed an undergraduate thesis related to feminist media studies. In my free time, I enjoy fiber arts, reading, and alpine slides.

Daniel Sharp

Daniel Sharp WWW

My current research interests are interacting particle systems, function approximation, and parameteric measure transport. Particularly, I'm interested in discretizing/quantizing measures with exotic or expensive densities (e.g., Bayesian posteriors). I'm a scientific software enthusiast, focusing on developing practical tools for these problems. I grew up near Raleigh, NC, and went to Virginia Tech for a B.S. in Computational Modeling and Data Analytics. For fun, I perfect the art of making/eating food, watching films, and meticulously digging in stacks of records.

Panos Tsimpos

Panos Tsimpos WWW CV

My research lies at the intersection of stochastics, statistics, and optimization. I’m currently focused on the theory of probabilistic generative flows, with particular interest in questions of optimal design and statistical efficiency. I often draw on techniques from stochastic analysis, variational analysis and differential equations, and I’m broadly interested in measure transport and related perspectives. I received my undergraduate degree from Columbia University, where I double majored in Mathematics and Physics. In my free time, I enjoy exercising and music.

Julie Zhu

Julie Zhu WWW

My research interest is three-fold: applied probability, Bayesian inverse problems, and scientific computing. I am excited about everything with a random nature, and developing methods to explore the universal governing rules beneath the chaos. Before joining MIT, I graduated from New York University Shanghai with bachelor’s degrees in Mathematics and Data Science. Besides research, I am a big fan of reading, outdoor activities, classical music, and making food maps around Boston.

Alumni

Research Scientists

  • Daniele Bigoni (CMCC)
  • Tarek El Moselhy (WorldQuant)
  • Jayanth Jagalur (Lawrence Livermore National Laboratory)

Postdoctoral Associates

  • Florian Augustin (MathWorks)
  • Ingrid Berkelmans
  • Michael Brennan (Solea Energy)
  • Nisha Chandramoorthy (Assistant Professor, University of Chicago)
  • Matthew Clarke (Assistant Professor, University of Illinois Urbana-Champaign)
  • Tiangang Cui (Senior Lecturer, University of Sydney)
  • Sonjoy Das (Assistant Professor, University at Buffalo)
  • Matthieu Dolbeault (Centrale Nantes, Department of Mathematics, Laboratoire de Mathématiques Jean Leray)
  • Dallas Foster (Nvidia)
  • Michalis Frangos
  • Nikhil Galagali (Apple, Inc.)
  • Jan Glaubitz (Assistant Professor, Linköping University)
  • Chen Gu (Assistant Professor, Tsinghua University)
  • Xun Huan (Associate Professor, University of Michigan)
  • Matt Levine (Research Scientist, Basis)
  • Matt Li (Assistant Professor, UMass Amherst)
  • Jinglai Li (Professor, University of Birmingham)
  • Hannah Lu (Assistant Professor, University of Texas at Austin)
  • Alexandre Marques (MIT)
  • Rebecca Morrison (Assistant Professor, University of Colorado Boulder)
  • Nicholas Nelsen (Assistant Professor, The University of Texas at Austin)
  • Matthew Parno (Solea Energy)
  • Mirjeta Pasha (Assistant Professor, Virginia Tech)
  • Maximilian Ramgraber (Assistant Professor, TU Delft)
  • Paul-Baptiste Rubio (Naval Group)
  • Timo Schorlepp (New York University)
  • Antti Solonen
  • Alessio Spantini (Bridgewater Associates)
  • Ankur Srivastava (Uptake Technologies)
  • Luca Tosatto
  • Sven Wang (Assistant Professor, EPFL)
  • Olivier Zahm (INRIA Grenoble)
  • Jakob Zech (Professor, University of Heidelberg)

Long-term Visitors

  • Daniele Bigoni (CMCC)
  • Henning Bonart (TU Darmstadt)
  • Ben Calderhead (Imperial College London)
  • Giuseppe Carere (Potsdam)
  • Weiqi Ji (Tsinghua University)
  • Dominic Kohler
  • Jinglai Li
  • Shuigen Liu (National University of Singapore)
  • Lionel Mathelin
  • Friedrich Menhorn (TU Munich)
  • Jakiw Pidstrigach (Potsdam)
  • Sebastian Springer (Lappeenranta University of Technology)
  • Faidra Stavropoulou
  • Jouni Susiluoto (Jet Propulsion Laboratory & Finnish Meteorological Institute)
  • Lara Welder
  • Fabio Zoccolan (EPFL)

Undergraduate Students

  • Erick Fuentes (Fitbit)
  • George Hansel
  • Savithru Jayasinghe
  • Luann Jung
  • Hadi Kasab
  • Tomas Kogan
  • Michael Lieu
  • Kevin Lim (University of Toronto, Department of Economics)
  • Mario Mrowka
  • Ali Saab (American University of Beirut)
  • Yair Shenfeld (Brown University)

PhD Students

  • Raghav Aggarwal (VulcanForms) (graduated January 2018)
    ◦ Thesis: A phase field model for the gallium permeation of aluminum grain boundaries (co-advised with Michael Demkowicz)
    ◦ Affiliation: VulcanForms
  • Michael Brennan (Salvo Energy) (graduated May 2023)
    ◦ Thesis: Gradient-based dimension reduction for Bayesian inverse problems and simulation-based inference
    ◦ Affiliation: Salvo Energy
  • Patrick Conrad (Insitro) (graduated April 2014)
    ◦ Thesis: Accelerating Bayesian inference in computationally expensive computer models using local and global approximations 
    ◦ Affiliation: Insitro
  • Andrew Davis (D. E. Shaw) (graduated May 2018)
    ◦ Thesis: Prediction under uncertainty: from models for marine-terminating glaciers to Bayesian computation (co-advised with P. Heimbach)
    ◦ Affiliation: D. E. Shaw
  • Nikhil Galagali (Apple, Inc.) (graduated December 2015)
    ◦ Thesis: Bayesian inference of chemical reaction networks
    ◦ Affiliation: Apple, Inc.
  • Alex Gorodetsky (Associate Professor, University of Michigan) (graduated September 2016)
    ◦ Thesis: Continuous low-rank tensor decompositions, with applications to stochastic optimal control and data assimilation (co-advised with S. Karaman)
    ◦ Affiliation: Associate Professor, University of Michigan
  • Xun Huan (Associate Professor, University of Michigan) (graduated August 2015)
    ◦ Thesis: Numerical approaches for sequential Bayesian optimal experimental design
    ◦ Affiliation: Associate Professor, University of Michigan
  • Dimitris Konomis (The Voleon Group) (graduated January 2025)
    ◦ Thesis: Max-Stable Processes, Measure Transport, and Conditional Sampling
    ◦ Affiliation: The Voleon Group
  • Kelvin Leung (Machine Learning Scientist, Themis AI) (graduated August 2025)
    ◦ Thesis: Structured Bayesian Inference for Spatio-Temporal Systems with Applications in Remote Sensing
    ◦ Affiliation: Machine Learning Scientist, Themis AI
  • Fengyi Li (LinkedIn) (graduated February 2024)
    ◦ Thesis: New tools for Bayesian optimal experimental design and kernel-based generative modeling
    ◦ Affiliation: LinkedIn
  • Aimee Maurais (graduated 2026)
    ◦ Thesis: Design of Dynamic Measure Transport for Sampling
  • Ricardo Miguel Baptista (Assistant Professor of Statistical Sciences, University of Toronto) (graduated June 2022)
    ◦ Thesis: Probabilistic modeling and Bayesian inference via triangular transport
    ◦ Affiliation: Assistant Professor of Statistical Sciences, University of Toronto
  • Antoni Musolas (Oaktree Capital) (graduated March 2020)
    ◦ Thesis: Covariance estimation on matrix manifolds
    ◦ Affiliation: Oaktree Capital
  • Matthew Parno (Solea Energy) (graduated October 2014)
    ◦ Thesis: Transport maps for accelerated Bayesian computation 
    ◦ Affiliation: Solea Energy
  • Jon Paul (JP) Janet (AstraZeneca) (graduated December 2019)
    ◦ Thesis: Multifidelity methods for design of transition metal complexes (co-advised with H. Kulik)
    ◦ Affiliation: AstraZeneca
  • Robert Ren (Radix Trading) (graduated May 2025)
    ◦ Thesis: Theoretical Foundations of Flow-Based Methods for Sampling and Generative Modeling
    ◦ Affiliation: Radix Trading
  • Andrea Scarinci (Amazon) (graduated September 2021)
    ◦ Thesis: Robust Bayesian Inference via Optimal Transport Misfit Measures: Applications and Algorithms
    ◦ Affiliation: Amazon
  • Alessio Spantini (Bridgewater Associates) (graduated August 2017)
    ◦ Thesis: On the low-dimensional structure of Bayesian inference
    ◦ Affiliation: Bridgewater Associates
  • Zheng Wang (Citadel) (graduated June 2019)
    ◦ Thesis: Optimization-based sampling in function space for Bayesian inverse problems
    ◦ Affiliation: Citadel
  • Benjamin Zhang (Incoming Assistant Professor, Rutgers University) (graduated February 2022)
    ◦ Thesis: Efficient sampling methods of, by, and for stochastic dynamical systems
    ◦ Affiliation: Incoming Assistant Professor, Rutgers University
  • Joanna Zou (graduated 2026)
    ◦ Thesis: Goal-Oriented Learning of Stochastic Dynamical Systems

SM Students

  • Adrianna Boghozian (Bill.com)
  • Thomas Coles (MIT)
  • Lucio Di Ciaccio (The Carlyle Group)
  • Chi Feng (Suno)
  • Naveen Krishnakumar (Grantham Mayo van Otterloo)
  • Subhadeep Mitra (Two Sigma Investments)
  • Oliver Wang
  • Joshua White (Boeing)