Research Group

Principal Investigator

alex_abq_2017

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.

Email Alex: [goroda] at umich dot edu

View Full CV (Updated 12/16/2020)

Research Group

PhD Students

Doruk Aksoy

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Doruk recieved 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. His research interests include design and integration of machine learning tools into modeling and control. His current research is focused on creating an inverse design tool for self oscillating gels using deep learning architectures.

Email Doruk: [doruk] at umich dot edu

LinkedIn: https://www.linkedin.com/in/dorukaks

Brian Chen

aksoy

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

Nicholas Galioto

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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

LinkedIn: https://www.linkedin.com/in/ngalioto/

Carleen McKenna

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Carleen McKenna received her B.S. in Mechanical Engineering from North Carolina State University, and is a National Science Foundation Graduate Fellow. Her research interests include optimal control under uncertainty and the integration of iterative learning control with existing stochastic optimal control methodologies. Currently, her research is focused on the application of these topics to dynamic soaring of unmanned aerial vehicles (UAVs).

Email Carleen: [camckenn] at umich dot edu

LinkedIn: https://www.linkedin.com/in/carleen-mckenna-bb82928a/

Liliang Wang

lwang

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.

Hang Yang

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Hang's research aims to develop theoretically sound and computationally efficient uncertainty quantification methods to enable intelligent online decision-making and control for nonlinear stochastic systems. Additionally, Hang's research aims to ensure the capability of these methods to quickly adapt to uncertainties in the environment through statistical inference and learning. Hang received his B.S. from the Penn State and his M.S. from University of Michigan, both in Mechanical Engineering.

Email Hang: [hangyang] at umich dot edu

LinkedIn: https://www.linkedin.com/in/hangyangumich

Postdoctoral Fellows

Trung Bao Pham

trung_pham

Trung finished his Ph.D. in mechanical engineering at Oregon State University. He has always been captivated by mechanics and applied mathematics, including optimization and computational mechanics. His research has focused on topology optimization and recently uncertainty quantification. He is also an open-source advocate who loves minimalism of C, Vim and Arch Linux, while spending his spare time hacking on small projects.

Email Trung: [trungp] at umich dot edu

Independent Research

Saibal De, 2020

Kazuya Tsuji, 2020-2021

Former Members

  • 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
  • Ian Lasky, Masters 2019
  • Kaijun He, Undergraduate 2018-2019, now at Stanford
  • 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
  • Jiachen (Lydia) Wang, Masters 2018-2019
  • John Wiegand, Masters 2020, now at Raytheon
  • Yulong (Arthur) Yang, Undergraduate 2020
  • Mingfei (Doris) Ye, Masters 2020, LinkedIn

Copyright (c) 2020, Alex Gorodetsky, goroda@umich.edu License: CC BY-SA 4.0