Our research develops mathematical and algorithmic foundations for building autonomous systems that can safely interact with complex, uncertain, and changing environments. Our work lies at the intersection of formal methods, control theory, game theory, and machine learning, with a focus on automatically synthesizing controllers and decision-making strategies that come with rigorous correctness guarantees. More recently, our work explores how these guarantees can be extended beyond idealized “closed-world” assumptions by enabling systems to adapt to new environments, cooperate in distributed settings, learn from data, and interact with humans while remaining reliable and explainable.
Most Recent Projects:
Active Projects:
- Permissive Templates for Strategy Adaptation
- Taming Fairness Assumptions in Two-Player Games
- Context-Triggered Control Design
- Assume/Guarantee Contracts for Distributed Supervisory Control
- Discretization-based Abstraction of Non-linear Control Systems