Ryan Cosner

Ryan's research lies at the intersection of control theory, machine learning, and robotics, with the goal of enabling provably safe and dynamic robot autonomy in uncertain real-world settings. His work bridges theory and practice through the development of risk-aware control frameworks, efficient deployable algorithms, and validation on a wide range of robot platforms. His ultimate goal is to create safety methods that inspire trust and provide us with the confidence needed to deploy high-performance autonomous robots at scale. **Recruiting new PhD students for admission in Fall 2026.**