Learning algorithms for autonomous systems
I develop learning algorithms that help autonomous systems perceive uncertain environments, learn useful representations and world models, and choose safe, effective actions. My interests include learning-enabled planning and control, adaptation from limited data, decision-making under uncertainty, and reliability under physical constraints.
Incentives, coordination, and task delegation of multiagent autonomous systems
I study how teams of autonomous agents divide work, exchange information, and coordinate decisions when their goals, capabilities, and information differ. This includes incentive design, distributed coordination, dynamic task allocation and delegation, negotiation, and mechanisms that make collective behavior efficient, robust, and aligned with system-level objectives.