Alex Gorodetsky is an Assistant Professor of Aerospace Engineering at the University of Michigan. His research interests include using applied mathematics and computational science to enhance autonomous decision making under uncertainty. He is especially interested in controlling systems, like autonomous aircraft, that must act in complex environments that are often represented by expensive computational simulations. Toward this goal, he pursues research in wide-ranging areas including uncertainty quantification, statistical inference, machine learning, numerical analysis, function approximation, control, and optimization.
Prior to coming to the University of Michigan, Alex was the John von Neumann Postdoctoral Research Fellow at Sandia National Laboratories in Albuquerque, New Mexico. At Sandia, Alex worked in the Optimization and Uncertainty Quantification Group on algorithms for propagating uncertainty through physical systems described with computationally expensive simulations.
Alex completed his Ph.D. (2016) and S.M. (2012) in the Department of Aeronautics and Astronautics at the Massachusetts Institute of Technology, where he worked on algorithms for stochastic optimal control and estimation in dynamical systems. He received his B.S.E (2010) in Aerospace Engineering from the University of Michigan.
View full CV (updated 5/7/2020)
Nick’s research is primarily focused on the areas of system identification and Bayesian inference. He is interested in finding ways to integrate our knowledge of physics into system identification approaches to yield more efficient and reliable results. Additionally, the reach of Bayesian inference is in general limited by system complexity and computational demand, and he is interested in overcoming these limits to extend practical methods of Bayesian inference into increasingly complex problems.
Research interests: Uncertainty quantification, stochastic optimal control, reinforcement learning, automotive systems
Trung Bao Pham
Hadi received his M.S. and Ph.D. in Structural Engineering from Michigan State University. His research interests include data-driven decision making, machine learning, Bayesian inference, uncertainty quantification, and structural/aerospace health monitoring with self-powered sensing. His research is particularly focused on the development of low-rank tensor decompositions algorithms and deep neural networks for autonomous systems and smart infrastructure monitoring.
Saibal De, 2020
Mingfei (Doris) Ye, 2020
Audelia Szulman, 2020
Siddhant Tandon, 2020
Yaser Afshar (Postdoctoral Fellow 2019, now at Minnesota)
Deepika Baskar (Masters, 2018-2019, now at Collins Aerospace)
Koray Kachar (Masters, 2018 — 2020, now at Draper Labs)
Donghak Kim (Masters 2018-2019)
Kaijun He (Undergraduate 2018-2019, now at Stanford)
Jiachen (Lydia) Wang (Masters 2018-2019)
Sunbochen Tang, (Masters 2019-2020)
John Wiegand (Masters, 2020, now at Raytheon)
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