ASSET Seminar: Building certifiably safe and correct large-scale autonomy (Chuchu Fan, Massachusetts Institute of Technology)

Presentation Abstract: 

The introduction of machine learning (ML) and artificial intelligence (AI) creates unprecedented opportunities for achieving full autonomy. However, learning-based methods in building autonomous systems can be extremely brittle in practice and are not designed to be verifiable. In this talk, I will present several of our recent efforts that combine ML with formal methods and control theory to enable the design of provably dependable and safe autonomous systems. I will introduce our techniques to generate safety certificates and certified control for complex autonomous systems, even when the systems have a large number of agents and follow nonlinear and nonholonomic dynamics.

Speaker Bio:

Chuchu Fan is an Assistant Professor in the Department of Aeronautics and Astronautics (AeroAstro) and Laboratory for Information and Decision Systems (LIDS) at MIT. Before that, she was a postdoc researcher at Caltech and got her Ph.D. from ECE at the University of Illinois at Urbana-Champaign in 2019. She earned her bachelor’s degree from Tsinghua University, Department of Automation. Her research group Realm at MIT works on using rigorous mathematics including formal methods, machine learning, and control theory for the design, analysis, and verification of safe autonomous systems. Chuchu is the winner of the 2020 ACM Dissertation Award.