My research interests lie at the intersection of control theory and machine learning, specifically in robotic and multi-agent systems. Classical control provides stability proofs but struggles with autonomous systems operating in uncertain environments. Pure data-driven methods adapt well to changing dynamics but lack formal performance assurance.
I aim to develop hybrid approaches that combine the best of both worlds, achieving provable performance bounds with real-time adaptation to unknown dynamics.