Youssef Marzouk is a professor in the Department of Aeronautics and Astronautics at MIT and co-director of the MIT Center for Computational Science and Engineering. He is also a core member of MIT's Statistics and Data Science Center and director of MIT’s 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 and environmental applications. He is an avid coffee drinker and occasional classical pianist.
Youssef M. Marzouk
MIT Department of Aeronautics and Astronautics
77 Massachusetts Avenue, Building 37-451
Cambridge, MA 02139
S.B., 1997, Massachusetts Institute of Technology S.M., 1999, Massachusetts Institute of Technology Ph.D., 2004, Massachusetts Institute of Technology
Associate Fellow of the AIAA, 2018; MIT Class of 1942 Career Development Chair, 2012–2015; MIT Junior Bose Award for Excellence in Teaching, 2012; AIAA Undergraduate Teaching Award, 2011; DOE Early Career Research Award, 2010-2015; Truman Fellow, Sandia National Laboratories, 2004–2007; Hertz Foundation Doctoral Thesis Prize, 2004; Joseph H. Keenan Prize, MIT Department of Mechanical Engineering, 2004; Hertz Fellow, 1997–2002
American Institute of Aeronautics and Astronautics, American Statistical Association, Society for Industrial and Applied Mathematics, American Physical Society, International Society for Bayesian Analysis
Assistant Professor, 2009-2012; Associate Professor, 2012-2020; Professor, 2020-present
Sandia National Laboratories, 2004–2008
Computational science and engineering, computational statistics. Uncertainty quantification, inverse problems, data assimilation. Energy and geophysics applications.
Computational mathematics, probability and statistics, fluid dynamics, aerodynamics, uncertainty quantification, stochastic modeling.