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.
Alex won the Air Force Young Investigator Award in 2018 and the NSF CAREER Award in 2023.
Email Alex: [goroda] at umich dot edu
View Full CV (Updated 7/20/2023)
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.
Email Nick: [ngalioto] at umich dot edu
Doruk received his B.Sc. degree from Bogazici University Department of Mechanical Engineering in Turkey and his MSE degree from University of Michigan Department of Mechanical Engineering. Currently, he is also enrolled in the Ph.D. in Scientific Computing joint degree program. His research interests include tensor decompositions, machine learning, and Bayesian inference. He is currently focused on developing efficient incremental algorithms to compute tensor decompositions.
Email Doruk: [doruk] at umich dot edu
Brian received his B.S. in mathematics from the University of Chicago. His research interests include reinforcement learning and imitation learning, as well as applications of machine learning to quantum computing. Currently, his work is on imitation learning in games, with the goal of extracting interpretable strategies from gameplay. He is co-advised by Dr. Veerapaneni in the Mathematics department.
Email Brian: [chenbri] at umich dot edu
Thomas received his B.S. in Computational Physics and Mathematics at Francis Marion University. His research interests include multi-fidelity estimation and stochastic optimization for trajectory planning and control. Currently, his research is expanding multi-fidelity sampling strategies to utilize multi-output systems and applying these methods in an Entry, Descent, and Landing (EDL) context.
Email Thomas: [tdixono] at umich dot edu
In-space propulsion research efforts are focused on electric propulsion systems, which are essential for low Earth orbit spacecraft and future long-term space exploration missions. Josh’s research interests in this field are specifically on improving computational and predictive plasma dynamics modeling and increasing performance of high-power Hall-effect thrusters. Josh received his B.S. in Mechanical Engineering from Rose-Hulman Institute of Technology.
Email Joshua: [eckelsjd] at umich dot edu
Liliang’s research interests include reinforcement learning, stochastic control and computation compression. Currently, her work is focused on incorporation of reinforcement learning and Bayesian inference for optimal control of systems under uncertainties from different sources, for a good balance of controller performance, robustness, without the need of large datasets.
- Jacob Klinger, 2023
- Jose Eduard Castro Villasana (UDEM), SURE 2023
- Jose Alberto Castro Villasana (UDEM), SURE 2023
- Yaser Afshar, Postdoctoral Fellow 2019
- Deepika Baskar, Masters 2018 - 2019, now at Collins Aerospace
- Koray Kachar, Masters 2018 — 2020
- Tejas Kadambi, Undergraduate, 2022
- Donghak Kim, Masters 2018 - 2019
- Jiwon Kim, Masters 2022 - 2023
- Jacob Klinger, Undergraduate, 2023
- Ian Lasky, Masters 2019
- Hao Liu, Undergraduate, 2021
- Carleen McKenna, Masters, 2019-2023
- Keyshawn Nunely, Undergraduate, 2023
- Kaijun He, Undergraduate 2018 - 2019,
- Trung Pham, Postdoctoral Fellow 2019-2022
- Hadi Salehi, Postdoctoral Fellow, 2020, LinkedIn
- Audelia Szulman, Masters 2020, now at RGBSI LinkedIn
- Siddhant, Tandon, Masters 2020, LinkedIn, website
- Sunbochen Tang, Masters 2019 - 2020, now at MIT
- Jiachen (Lydia) Wang, Masters 2018 - 2019
- John Wiegand, Masters 2020, now at Raytheon
- Alex Xu, Undergraduate 2022-2023,
- Hang Yang, PhD 2022, now at McKinsey LinkedIn
- Yulong (Arthur) Yang, Undergraduate 2020
- Mingfei (Doris) Ye, Masters 2020, LinkedIn
- Xuhao Zhang, Masters 2022
- Jingyi Zhou, Masters, 2023