Dr. Zhang joined the University of Maryland, College Park as a tenure-track Assistant Professor in Oct. 2022. During the deferral time before joining, he was a postdoctoral scholar affiliated with LIDS and CSAIL at the Massachusetts Institute of Technology, and a Research Fellow at Simons Institute for the Theory of Computing at Berkeley.
Dr. Zhang received his Ph.D. in 2021 from the Department of Electrical and Computer Engineering (ECE) at the University of Illinois at Urbana-Champaign, affiliated with the Coordinated Science Laboratory (CSL). Prior to his Ph.D., Dr. Zhang received two M.S. degrees in 2017, one in ECE and the other in Applied Math from the University of Illinois at Urbana-Champaign. Dr. Zhang holds a B.E. with a second degree in Economics from Tsinghua University in China.
His research interests lie broadly in control theory, game theory, reinforcement learning, robotics, and their intersections. He is the recipient of several awards and fellowships, including Hong, McCully, and Allen Fellowship, Kuck Computational Science & Engineering Scholarship, YEE Fellowship, Mavis Future Faculty Fellowship at UIUC, Simons-Berkeley Research Fellowship, CSL PhD Thesis Award, and ICML Outstanding Paper.
My research interests lie in the intersection of control theory, game theory, and machine/reinforcement learning, especially in multi-agent and safety-critical systems; with applications in intelligent and distributed cyber-physical systems, e.g., robotics, smart grid, and transportation systems. I resort to mathematical tools from the areas of Control Theory, Game Theory, Operations Research, and Probability Theory to develop provably convergent and efficient algorithms. Broadly speaking, the primary goal of my research is to lay theoretical foundations for the learning algorithms and systems that address (data-driven) sequential-decision-making problems in game theory and control theory, particularly in the presence of multiple decision-makers, towards large-scale and reliable autonomy.
- Control Theory
- Game Theory
- Reinforcement Learning
- Economics and Decision Theory
- Machine Learning and Autonomy
ENEE769L: Decision Making Under Uncertainty -- Reinforcement Learning, Control, and Games, Spring 2023