Glen Chou
Glen Chou is an assistant professor at Georgia Tech in the College of Computing, within the School of Cybersecurity & Privacy (SCP), and in the College of Engineering, within the School of Aerospace Engineering (AE). He joined Georgia Tech in November 2024. Glen directs the Trustworthy Robotics Lab, which focuses on the design of algorithms that can enable general-purpose robots and autonomous systems to operate capably, safely, and securely, while remaining resilient to real-world failures and uncertainty. To achieve this, the lab leverages control theory and machine learning, while connecting to optimization, perception, formal methods, motion planning, human-robot interaction, and statistics. Glen received dual B.S. degrees in EECS and ME from UC Berkeley in 2017 and M.S. and Ph.D. degrees in ECE from the University of Michigan in 2019 and 2022, respectively. Prior to joining Georgia Tech in 2024, Glen spent two years as a postdoc at MIT CSAIL.
My teaching interests span robotics and robot learning, trustworthy autonomy, control theory, and computer security. In control, I am interested in teaching about feedback systems at both the undergraduate and graduate levels, covering linear, nonlinear, and optimal control with an emphasis on rigorous analysis and real-world implementation. I aim to connect state-space methods and computational tools to modern applications in robotics. I am also interested in teaching about scalable methods for formal verification with applications in robotics and safe machine learning for autonomy, equipping students to design provably reliable cyber-physical systems. I also teach computer security with at the undergraduate level.
I direct the Trustworthy Robotics Lab, where we design algorithms that can enable general-purpose robots and autonomous systems to operate capably, safely, and securely with humans, while remaining resilient to real-world failures and uncertainty. To achieve this, we leverage control and machine learning, while connecting to optimization, perception, formal methods, planning, human-robot interaction, and statistics. I believe strongly in validating that the theoretical guarantees of my algorithms translate to the real world when deployed on hardware. I'm interested in a broad range of applications, including robotic manipulation, vision-based navigation, aerospace autonomy, and the control of large-scale cyber-physical systems.
Disciplines:
- Trustworthy Robotics Laboratory
- Flight Mechanics and Controls
AE Multidisciplinary Research Areas::
- Cyberphysical Systems, Safety, and Reliability
- Robotics, Autonomy, and Human Interactions
- B.S., Electrical Engineering and Computer Science and Mechanical Engineering, University of California, Berkeley, 2017
- M.S., Electrical and Computer Engineering, University of Michigan, 2019
- Ph.D., Electrical and Computer Engineering, University of Michigan, 2022
- Robotics: Science and Systems Pioneer, 2022
- National Defense Science and Engineering Graduate (NDSEG) Fellowship, 2019
- National Science Foundation (NSF) Graduate Fellowship, 2019
- '- Nath*, Yin*, and Chou. Scalable Data-Driven Reachability Analysis and Control via Koopman Operators with Conformal Coverage Guarantees. 8th Annual Learning for Dynamics & Control Conference (L4DC), oral presentation, June 2026.
- Zhan, Chiu, Leeman, and Chou. Robustly Constrained Dynamic Games for Uncertain Nonlinear Dynamics. IEEE International Conference on Robotics and Automation (ICRA), June 2026.
- Li and Chou. A Convex Formulation of Compliant Contact between Filaments and Rigid Bodies. IEEE International Conference on Robotics and Automation (ICRA), June 2026.
- Chiu, Zhang, and Chou. Learning Constraints from Stochastic Partially-Observed Closed-Loop Demonstrations. IEEE Control Systems Letters, Jan 2026.
- Suh, Chou, Dai, Yang, Gupta, and Tedrake. Fighting Uncertainty with Gradients: Offline Reinforcement Learning via Diffusion Score Matching. 7th Conference on Robot Learning (CoRL), Nov 2023.