Principal Investigator

Youssef Marzouk
Youssef Marzouk

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.

Research Scientists

Jayanth Jagalur
Jayanth Jagalur

Jayanth Jagalur  

My research is broadly in the field of UQ and inverse problems. Other areas that interest me are, multi-scale methods, adaptivity, iterative solvers, and HPC. Prior to joining the group I was at RPI, where I obtained an M.S. in Applied Mathematics and PhD. in Mechanical Engineering. My dissertation research involved developing variational multi-scale methods for deterministic and stochastic wave equations. Outside my work, I usually prefer to spend time in the outdoors, indulge in culinary exploits or continue my quest to learn music.

Postdoctoral Associates

Nisha Chandramoorthy
Nisha Chandramoorthy

Nisha Chandramoorthy  

I am working with Youssef on improving data assimilation (in particular, Bayesian filtering) algorithms in chaotic dynamical systems. I enjoy working at the intersection of dynamical systems theory with computational statistics and with machine learning and optimization, and I am interested in developing scalable algorithms for climate dynamics problems. I am co-advised by Stefanie Jegelka (MIT CSAIL) with whom I am currently working on applying dynamical systems and ergodic theory to analyze machine learning algorithms. Before this, I was a PhD student at MIT advised by Qiqi Wang. I worked with Qiqi on developing an efficient algorithm to differentiate ensemble statistics of chaotic systems with respect to system parameters. I like to run in various places and weathers and to practice music much more frequently than I actually do.
Dallas Foster
Dallas Foster

Dallas Foster  

I am a multidisciplinary applied mathematician with research interest in uncertainty quantification, data assimilation, scientific computing, and machine learning with applications to climate, geophysics and chemistry. Prior to joining the UQ group I received my PhD from Oregon State University where my dissertation centered on data assimilation techniques for stochastic hyperbolic geophysical flows. Beyond work, I usually find myself either enjoying the outdoors, cooking & baking, or traveling.
Matt Li
Matt Li

Matt Li  

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.
Maximilian Ramgraber
Maximilian Ramgraber

Maximilian Ramgraber   www

I am an environmental scientist with a background in hydrogeology and an interest in nonlinear, non-Gaussian data assimilation and sequential parameter inference, jointly hosted with Prof. Dennis McLaughlin from CEE. My current research explores transport maps for nonlinear smoothing. I conducted my PhD research at the University of Neuchâtel and the Swiss Federal Institute of Aquatic Science and Technology (Eawag) in Zürich. Outside of work, I enjoy art, playing the guitar, travelling, and a vague, steadily expanding cloud of digital design interests from 3D modelling in Blender to graphic design to writing interactive web elements.
Paul-Baptiste Rubio
Paul-Baptiste Rubio

Paul-Baptiste Rubio  

My research is broadly in the area of numerical methods for Bayesian inference, uncertainty quantification and prediction of extreme events. Before joining MIT, I grew up in Corsica and did my graduate study at ENS Paris-Saclay in the field of mechanical engineering. I also conducted my PhD there developing numerical methods for fast sequential Bayesian inference using reduced order models. Outside of work I enjoy watching American football and listening to operas.
Sven Wang
Sven Wang

Sven Wang  

I joined the UQ group and IDSS as a postdoctoral researcher in April 2021. My main research interests are twofold. Firstly, I am interested in deriving provable guarantees for commonly used inference procedures (MCMC, transport based methods, convergence rates in the large sample limit) in complex statistical models such as PDE models. Secondly, I am interested to help understand social systems, such as voting procedures, from the perspective of dynamical systems and applied mathematics more broadly.

Graduate Students, PhD

Joanna Zou
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.
Michael Brennan
Michael Brennan

Michael Brennan  

My research is broadly in the area of numerical methods for uncertainty quantification and mathematical modeling. I am currently interested in techniques that exploit multi-scale/multi-feature structure in a system's dynamics. These methods accelerate large scale computations. Before joining MIT, I received an M.S. in Mathematics from Virginia Tech, where I studied nonlinear eigenvalue problems and reduced order modeling. Outside of work, I enjoy cooking, snowboarding, and exploring Boston.
Chi Feng
Chi Feng

Chi Feng  

My research interests include optimal experimental design in the presence of model error and other topics in uncertainty quantification. I received my bachelor's degree in Physics from the California Institute of Technology. Outside of my work, I am interested in classical piano, photography, web design, and the culinary arts.
Dimitris Konomis
Dimitris Konomis

Dimitris Konomis  

I am broadly interested in computational mathematics (applied probability, machine learning, optimization). My current research focuses on efficient rare event simulation and constrained transport map based density estimation. I have worked in randomized numerical linear algebra (CMU), scalable experiment design (Google) and CNN models for disease classification (Petuum). Before joining MIT, I received a bachelors in electrical engineering & computer science from the National Technical University of Athens, as well as a masters in machine learning and a masters in computer science from CMU. When I am not working, I enjoy hiking, photography, soccer and exploring new restaurants.
Kelvin Leung
Kelvin Leung

Kelvin Leung  

My research is broadly focused in uncertainty quantification and Bayesian inference. I’m also interested in machine learning and optimization. I grew up in rural Canada and graduated from the Engineering Science program at the University of Toronto in 2019, majoring in aerospace engineering. Outside of work, I play violin in the MIT Symphony Orchestra and also enjoy travelling and culinary adventures.
Fengyi Li
Fengyi Li

Fengyi Li  

My current research is related to machine learning, inference, and optimal experimental design. In addition, I am interested in Bayesian statistics, optimization and applied probability. I was born and raised in Zhengzhou, China and lived in College Station, Texas for a few years, where I received my Bachelor’s degrees in Mathematics and Mechanical Engineering from Texas A&M University. In my free time, I enjoy playing tennis and listening to music.
Ricardo Miguel Baptista
Ricardo Miguel Baptista

Ricardo Miguel Baptista   www

My current research is on methodology for Bayesian inference and data assimilation in high dimensions. In particular, I focus on inference algorithms based on structured measure transport techniques. Broadly, I am also interested in high-dimensional approximation, and applications in the physical sciences. Before joining MIT, I grew up in Canada and graduated from the Engineering Science program at the University of Toronto. In my free time, I enjoy swimming, traveling, and going on espresso tastings in Boston.
Robert Ren
Robert Ren

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.
Benjamin Zhang
Benjamin Zhang

Benjamin Zhang   www

My research lies at the intersection of computational statistics and computational dynamics. I enjoy studying how these two fields interact and complement each other for predictive modeling and uncertainty quantification. I received my Bachelor's degrees in engineering physics and applied mathematics from UC Berkeley in 2015. Outside of work, I enjoy discussions about politics, history, cartography, intrigue, conspiracy, and gripping denouement.

Graduate Students, SM

Katharine Fisher
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
Julien Luzzatto
Julien Luzzatto

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!
Aimee Maurais
Aimee Maurais

Aimee Maurais  

My current research is focused on multifidelity data assimilation and the associated issue of computing prior-to-posterior transformations from multifidelity ensembles. I am particularly interested in environmental applications and have in the past worked on projects related to atmospheric chemistry and environmental contamination mapping. I graduated from Virginia Tech with a B.S. in Mathematics and a B.S. in Computational Modeling and Data Analytics in 2019, and after that spent 1.5 years on the technical staff at MIT Lincoln Lab before beginning my graduate work. In my free time I enjoy cycling, cooking, hiking, handcrafts, and singing in the MIT Concert Choir.
Daniel Sharp
Daniel Sharp

Daniel Sharp   www

I'm currently researching online parameter estimation using data assimilation in the context of wind fields, with prior experience in high performance computing, model order reduction, and other topics. I'm broadly interested in making simulations both fast and robust using surrogates, code optimizations, software tools, and theory. I grew up near Raleigh, NC, but went to Virginia Tech for a B.S. in Computational Modeling and Data Analytics. For fun, I enjoy perfecting the art of making/eating food, watching films, and meticulously digging in stacks of records.
Joshua White
Joshua White

Joshua White  

My research in this group focuses on tensor methods for high-dimensional partial differential equations with applications in stochastic control. Born and raised in Massachusetts, I graduated with a B.S. in Aerospace Engineering from MIT in 2021 while doing some undergraduate research in rare event simulation with Professor Marzouk. I enjoy running, reading, hiking, a good pair of headphones, and evaluating the Dunkin Donuts locations around Cambridge based solely on how well they can make a frozen coffee.

Alumni

Research Scientists

Postdoctoral Associates

  • Florian Augustin (MathWorks)
  • Ingrid Berkelmans (Australia Future Fund)
  • Tiangang Cui (Senior Lecturer, Monash University)
  • Sonjoy Das (Assistant Professor, University at Buffalo)
  • Michalis Frangos (Schlumberger)
  • Nikhil Galagali (Apple, Inc.)
  • Chen Gu (Assistant Professor, Tsinghua University)
  • Xun Huan (Assistant Professor, University of Michigan)
  • Jinglai Li (Professor, University of Birmingham)
  • Alexandre Marques (MIT)
  • Rebecca Morrison (Assistant Professor, University of Colorado Boulder)
  • Matthew Parno (Dartmouth College and Cold Regions Research and Engineering Laboratory)
  • Antti Solonen (Lappeenranta University of Technology and Eniram)
  • Alessio Spantini (Bridgewater Associates)
  • Ankur Srivastava (Uptake Technologies)
  • Luca Tosatto (Bridgewater Associates)
  • Olivier Zahm (INRIA Grenoble)
  • Jakob Zech (Juniorprofessor, University of Heidelberg)

Long Term Visitors

  • Daniele Bigoni (Technical University of Denmark)
  • Ben Calderhead (Imperial College London)
  • Weiqi Ji (Tsinghua University)
  • Dominic Kohler (Siemens AG)
  • Jinglai Li (Shanghai Jiaotong University)
  • 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)

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
  • Patrick Conrad (graduated April 2014)
    • Thesis: Accelerating Bayesian inference in computationally expensive computer models using local and global approximations 
    • Affiliation: Cherish Health
  • Andrew Davis (graduated May 2018)
    • Thesis: Prediction under uncertainty: from models for marine-terminating glaciers to Bayesian computation (co-advised with Patrick Heimbach)
    • Affiliation: Courant Institute, New York University
  • 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
  • Antoni Musolas (graduated March 2020)
    • Thesis: Covariance estimation on matrix manifolds
    • Affiliation: Farallon Capital Management
  • Matthew Parno (graduated October 2014)
    • Thesis: Transport maps for accelerated Bayesian computation 
    • Affiliation: Cold Regions Research and Engineering Laboratory and Dartmouth College
  • 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: Kaleidoglobe

SM Students

  • Adrianna Boghozian
  • Thomas Coles (MIT)
  • Lucio Di Ciaccio (The Carlyle Group)
  • Naveen Krishnakumar (Grantham Mayo van Otterloo)
  • Subhadeep Mitra (Two Sigma Investments)

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)
  • Ali Saab (American University of Beirut)
  • Yair Shenfeld (Princeton University, ORFE)