Alex Gorodetsky is an Assistant Professor of Aerospace Engineering at the University of Michigan. 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.
Dr. Gorodetsky's 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.
A.Gorodetsky and J. D. Jakeman. Gradient-based optimization for regression in the functional tensor-train format. ArXiv e-print 1801.00885, 2018.
Alex Gorodetsky, Sertac Karaman, and Youssef Marzouk. “High-dimensional stochastic optimal control using continuous tensor decompositions”. The International Journal of Robotics Research 37:2-3(2018). Pg. 340-377.
Boris Kramer, Alex A. Gorodetsky. “System Identification via CUR-Factored Hankel Approximation”. SIAM Journal on Scientific Computing, 40:2(2018). Pg. A848-A866.
Alex A. Gorodetsky, Gianluca Geraci, Michael S. Eldred and John D. Jakeman. “LATENT VARIABLE NETWORKS FOR MULTIFIDELITY UNCERTAINTY QUANTIFICATION AND DATA FUSION”. 6th European Conference on Computational Mechanics (2018).
Michael S. Eldred, Gianluca Geraci, Alex Gorodetsky, John Jakeman. “Multilevel-Multifidelity Approaches for Forward UQ in the DARPA SEQUOIA project”. AIAA SciTech Forum (January 2018).