Safety Critical

Safety-critical systems research focuses on designing and verifying systems where failures can have catastrophic consequences, with a current emphasis on robotics, autonomous vehicles, and AI. Key research areas involve developing robust algorithms for real-time safety monitoring and recovery (e.g., using Q-networks and control barrier functions), diagnosing and mitigating distribution shifts in machine learning models (e.g., via martingales), and ensuring fairness and efficiency in multi-agent systems. This work is crucial for deploying reliable and trustworthy autonomous systems across various domains, improving safety and enabling wider adoption of advanced technologies.