Principal Investigator
Youssef Marzouk
Youssef Marzouk is a Professor in the Department of Aeronautics and Astronautics at MIT, and co-director of the MIT Center for Computational Science & Engineering. He is also a core member of MIT's Statistics and Data Science Center and director of the MIT Aerospace Computational Design Laboratory.
His research interests lie at the intersection of computation and statistical inference with physical modeling. He develops new methodologies for uncertainty quantification, Bayesian modeling and computation, data assimilation, experimental design, and machine learning in complex physical systems. His methodological work is motivated by a wide variety of engineering, environmental, and geophysics applications.
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 recipient of the Hertz Foundation Doctoral Thesis Prize, the Sandia Laboratories Truman Fellowship, the US Department of Energy Early Career Research Award, and the Junior Bose Award for Teaching Excellence from the MIT School of Engineering. He is an Associate Fellow of the AIAA and currently serves on the editorial boards of the SIAM Journal on Scientific Computing, the SIAM/ASA Journal on Uncertainty Quantification, and several other journals. He is also an avid coffee drinker and occasional classical pianist.
Postdoctoral Associates
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.
Matt Levine www
I am a Postdoctoral Fellow at the Broad Institute of MIT/Harvard in the Eric and Wendy Schmidt Center. I am broadly interested in intersections of machine learning, dynamical systems, and biomedical sciences. My work focuses on improving the prediction and inference of biological and physical systems by blending machine learning, mechanistic modeling, and data assimilation techniques. I studied biophysics as an undergraduate at Columbia University, and did a PhD in Computing and Mathematical Sciences at Caltech under the supervsion of Andrew Stuart. Outside of work, I enjoy playing music, going to concerts, playing tennis, skiing, and camping.
Matt Li www
I am a computational scientist/applied mathematician with interests in scientific inverse problems. Before working with Youssef I completed a PhD in computational science and engineering at MIT, advised by Laurent Demanet. Prior to MIT, I attended the University of Toronto and graduated with a bachelors and a masters degree in aerospace engineering. I enjoy watching and playing hockey, and it has been a painful experience being a fan of the Toronto Maple Leafs while living in Boston all these years.
I am an NSF Mathematical Sciences Postdoctoral Research Fellow at MIT. My research interests lie at the intersection of computational mathematics and statistics. I develop novel machine learning methods for high- and infinite-dimensional problems, establish theoretical guarantees on the reliability and trustworthiness of these methods, and apply them in the physical and data sciences. Recently, I have been working on blending operator learning with ideas from inverse problems, generative modeling, and uncertainty quantification. I received my Ph.D. from Caltech in 2024, advised by Andrew Stuart.
Spencer Wyant
My research interests focus on the development of a codebase for atomistic simulations as part of the CESMIX 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.
Current Visitors
Omar Al-Ghattas www, cv University of Chicago
My research rigorously analyzes widely used algorithms for inverse problems and data assimilation. Particularly, I investigate conditions under which these algorithms are effective, especially in possibly infinite-dimensional settings where the sample size is much smaller than the dimensionality of the state. I am finishing the final year of 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.
Fabio Zoccolan www EPFL
My research focuses on reduced order methods for stochastic differential equations. Particularly, I am studying the Dynamical Low-Rank Approximation for SDEs, working primarily on well-posedness and numerical discretizations via the Dynamically Orthogonal formalism. I joined the group as a visiting PhD to apply these methods to data assimilation and inverse problems. I am currently pursuing my doctorate in math at EPFL under the supervision of Prof. F. Nobile and Dr. Y. Kazashi. Originally from northeastern Italy, I enjoy watching films, traveling, eating and I am also a huge soccer and basketball fan!
Graduate Students, PhD
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
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.
Kelvin Leung
My research is focused on Bayesian inference and uncertainty quantification for high-dimensional problems. I’m currently working on developing algorithms in Earth remote sensing for climate science. I grew up in Canada and graduated from the Engineering Science program at the University of Toronto in 2019, majoring in aerospace engineering. Outside of work, I enjoy travel and culinary adventures.
Julien Luzzatto
My research interests are in mathematical and physical modeling, computational techniques and applied probability. My current research focuses on the long-time simulation of molecular systems for the CESMIX project, and involves stochastic modeling, high-dimensional approximation and numerical integration. Before joining MIT, I grew up in Italy and France, and graduated in Mathematics and Physics, at Ecole Polytechnique, Paris. In my free time, I enjoy playing soccer, tasting French wine and cooking Italian food!
My current research is focused on developing iterative methods for Bayesian inference using ideas from geometry and dynamics. I additionally have interests in data assimilation, covariance estimation, and application of Bayesian inference to physical systems. Prior to joining the UQ Group I spent 1.5 years on the technical staff at MIT Lincoln Laboratory, and I have a B.S. in Mathematics and a B.S. in Computational Modeling and Data Analytics from Virginia Tech (2019). In my free time I can often be found swimming, cooking, cycling, reading, or crafting.
Robert Ren
My research interest broadly lies in mathematical problems arising from data science. In particular, I am interested in using tools from variational analysis, empirical process theory, and PDEs to develop the mathematical theory of learning and explore its implications for possible advancement in numerical algorithms. My current research projects involve quantifying approximation rates for ODE-parametrized transport maps and establishing sample complexity bounds for sampling using NeuralODEs.
Daniel Sharp www
My current research interests are multi-level inference, function approximation, and parameteric measure transport. Particularly, I'm interested in how to approximate integrals over exotic or expensive weights (e.g., Bayesian posteriors). I tend toward being 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
I am interested in problems that lie in the intersection of stochastic analysis, differential geometry and inference with applications to uncertainty quantification and physics. I am currently researching optimal transport via the lens of information geometry. Before joining the UQ group I was an undergraduate student in Columbia University where I double majored in Mathematics and Physics. In my free time I enjoy exercising, being outdoors and visiting art exhibits.
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, SM
Oliver Wang
My current research revolves around developing algorithms that enable accurate, efficient, and systematic sampling from conditional distributions arising from complex dynamical systems with physical constraints. Beyond developing such algorithms, I am researching conditional robustness for inference and sampling methods through establishing guarantees under modeling uncertainty. Prior to joining MIT, I pursued my undergraduate studies at Emory University, majoring in applied mathematics and statistics (AMS). In my free time, I enjoy alpine skiing, golf, and food explorations.
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 (EnergyWay)
- Tarek El Moselhy (WorldQuant)
- Jayanth Jagalur (Lawrence Livermore National Laboratory)
Postdoctoral Associates
- Florian Augustin (MathWorks)
- Ingrid Berkelmans (Australia Future Fund)
- 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)
- Dallas Foster (Nvidia)
- Michalis Frangos (Schlumberger)
- Nikhil Galagali (Apple, Inc.)
- Jan Glaubitz (Assistant Professor, Linköping University)
- Chen Gu (Assistant Professor, Tsinghua University)
- Xun Huan (Assistant Professor, University of Michigan)
- 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)
- Matthew Parno (Solea Energy)
- Mirjeta Pasha (Professor, Virginia Tech)
- Maximilian Ramgraber (Assistant Professor, TU Delft)
- Paul-Baptiste Rubio (Naval Group)
- Timo Schorlepp (New York University)
- Antti Solonen (Lappeenranta University of Technology and Eniram)
- Alessio Spantini (Bridgewater Associates)
- Ankur Srivastava (Uptake Technologies)
- Luca Tosatto (Bridgewater Associates)
- Sven Wang (Assistant Professor, Humboldt University)
- Olivier Zahm (INRIA Grenoble)
- Jakob Zech (Juniorprofessor, University of Heidelberg)
Long Term Visitors
- Daniele Bigoni (Technical University of Denmark)
- Henning Bonart (TU Darmstadt)
- Ben Calderhead (Imperial College London)
- Weiqi Ji (Tsinghua University)
- Dominic Kohler (Siemens AG)
- Jinglai Li (Shanghai Jiaotong University)
- Shuigen Liu (National University of Singapore)
- Lionel Mathelin (LIMSI/CNRS France)
- Friedrich Menhorn (TU Munich)
- Sebastian Springer (Lappeenranta University of Technology)
- Faidra Stavropoulou (TU Munich)
- Jouni Susiluoto (Jet Propulsion Laboratory & Finnish Meteorological Institute)
- Lara Welder (RWTH Aachen)
Undergraduate Students
- Erick Fuentes (Fitbit)
- George Hansel (Google)
- Savithru Jayasinghe (Cambridge University)
- Luann Jung
- Hadi Kasab (American University of Beirut)
- Tomas Kogan (Cambridge University)
- Michael Lieu (Aurora Flight Sciences)
- Kevin Lim (University of Toronto, Department of Economics)
- Mario Mrowka (MIT)
- Ali Saab (American University of Beirut)
- Yair Shenfeld (MIT Department of Mathematics)
PhD Students
- Raghav Aggarwal (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 (graduated May 2023)
- Thesis: Gradient-based dimension reduction for Bayesian inverse problems and simulation-based inference
- Affiliation: Solea Energy
- Patrick Conrad (graduated April 2014)
- Thesis: Accelerating Bayesian inference in computationally expensive computer models using local and global approximations
- Affiliation: Insitro
- Andrew Davis (graduated May 2018)
- Thesis: Prediction under uncertainty: from models for marine-terminating glaciers to Bayesian computation (co-advised with Patrick Heimbach)
- Affiliation: D. E. Shaw
- Nikhil Galagali (graduated December 2015)
- Thesis: Bayesian inference of chemical reaction networks
- Affiliation: Apple, Inc.
- Alex Gorodetsky (graduated September 2016)
- Thesis: Continuous low-rank tensor decompositions, with applications to stochastic optimal control and data assimilation (co-advised with S. Karaman)
- Affiliation: Assistant Professor, University of Michigan
- Xun Huan (graduated August 2015)
- Thesis: Numerical approaches for sequential Bayesian optimal experimental design
- Affiliation: Assistant Professor, University of Michigan
- Dimitris Konomis (graduated January 2025)
- Thesis: Max-Stable Processes, Measure Transport, and Conditional Sampling
- Affiliation: The Voleon Group
- Fengyi Li (graduated February 2024)
- Thesis: New tools for Bayesian optimal experimental design and kernel-based generative modeling
- Affiliation: LinkedIn
- Ricardo Miguel Baptista (graduated June 2022)
- Thesis: Probabilistic modeling and Bayesian inference via triangular transport
- Affiliation: von Karman Instructor, Caltech
- Antoni Musolas (graduated March 2020)
- Thesis: Covariance estimation on matrix manifolds
- Affiliation: Oaktree Capital
- Matthew Parno (graduated October 2014)
- Thesis: Transport maps for accelerated Bayesian computation
- Affiliation: Solea Energy
- Jon Paul (JP) Janet (graduated December 2019)
- Thesis: Multifidelity methods for design of transition metal complexes (co-advised with Heather Kulik)
- Affiliation: AstraZeneca
- Andrea Scarinci (graduated September 2021)
- Thesis: Robust Bayesian Inference via Optimal Transport Misfit Measures: Applications and Algorithms
- Affiliation: Amazon
- Alessio Spantini (graduated August 2017)
- Thesis: On the low-dimensional structure of Bayesian inference
- Affiliation: Bridgewater Associates
- Zheng Wang (graduated June 2019)
- Thesis: Optimization-based sampling in function space for Bayesian inverse problems
- Affiliation: Citadel
- Benjamin Zhang (graduated February 2022)
- Thesis: Efficient sampling methods of, by, and for stochastic dynamical systems
- Affiliation: Brown University
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)
- Joshua White (Boeing)