MIT Uncertainty Quantification Group

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.

Jan Glaubitz is a Postdoctoral Associate in the Department of Aeronautics and Astronautics at MIT, collaborating with Professor Youssef Marzouk. His research aims to advance the foundational computational methodologies in numerical conservation laws and hierarchical Bayesian learning. Passionate about the interplay between theoretical numerical analysis, method development, and uncertainty quantification, he strives to establish provable approximation, convergence, and stability results while quantifying confidence in computational predictions.

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.

Matthew is an Engineering Excellence Postdoctoral Fellow in the Department of Aeronautics and Astronautics (A.A.) at the Massachusetts Institute of Technology. He is also a member of the V/STOL Aircraft Systems Technical Committee of the American Institute of Aeronautics and Astronautics. He holds a Ph.D. and M.S. in A.A. from Stanford University and a B.S. in Mechanical Engineering from Howard University. His research focuses on aircraft design, aerodynamics, and aeroacoustics, with an emphasis on the analysis and optimization of vehicles for regional and urban air mobility. It encompasses system modeling of novel battery technology for electric propulsion applications and the impact of aircraft noise on transportation network planning. He also explores how noise and battery state of health can guide the development of regional and urban air mobility vehicles for commuter travel around major cities and transit hubs.

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.

My research interests lie in the field of scientific computing, uncertainty quantification and machine learning in applications of environmental fluids. I am working with Youssef and Ruben Juanes (MIT CEE) on leveraging advances in data-driven tools to improve the efficiency of computation, quantify the uncertainty of models, and provide scientific information to inform decision-making on climate change. Before joining MIT, I completed my PhD in energy resources engineering at Stanford, advised by Daniel M. Tartakovsky. Outside of work, I enjoy playing the harp, travelling and exploring good restaurants.

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.

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.

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.

I am broadly interested in applied probability, machine learning, and optimization. My current research focuses on efficient rare event simulation and constrained transport-based density estimation. I have worked in randomized numerical linear algebra (CMU), scalable experiment design (Google), and disease classification (Petuum). Before joining MIT, I received a bachelors in EECS from the National Technical University of Athens, as well as a masters in machine learning and a masters in computer science from CMU. I also enjoy hiking, photography, soccer and exploring new restaurants.

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.

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.

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.

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.

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.

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.

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!

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.