Robust machine learning

This program aims to establish principled foundations for robust machine learning in complex and safety-critical environments. Its research pillars include robustness to distribution shifts and adversarial perturbations, formal verification and quantification of reliability, uncertainty estimation, trustworthy deployment in real-world systems, and applications in healthh, environmental sciences, autonomous systems, and industrial control.