Intelligent Learning and Control Lab

Our long-term objective is to advance learning and control methodologies that enable efficient, reliable, and scalable decision-making in multi-agent systems. To that end, we develop the mathematical and algorithmic foundations of data-driven optimal control at the intersection of control theory, learning (reinforcement learning, inverse reinforcement learning, deep neural networks), and game theory, inspired by cognitive science and backed by rigorous provable analysis. We validate this work on real platforms, including robot arms, unmanned vehicles, and sensor networks.

I received my Ph.D. from the University of Texas at Arlington in 2021, advised by Prof. Frank L. Lewis, and then stayed on as a postdoc with Prof. Lewis and Prof. Ali Davoudi until 2023, also teaching control engineering as an adjunct. Since August 2023, I’ve been an assistant professor in Electrical and Computer Engineering (ECE) at Auburn University, Alabama. More in my CV.