Automated Reasoning for Reliable Autonomy
Sicun Gao — University of California, San Diego
Wednesday, March 25, 2020
Abstract: We face grand challenges as computer systems start engaging us physically with high levels of autonomy. Their tight integration of computational and mechanical components generates behaviors that have not been well-studied in computer science or control engineering. The AI components in these systems complicate software execution flows with nonlinear functions, probabilistic reasoning, and error-prone numerical computation. I will describe a framework for automating the design and implementation of reliable autonomous systems, and the need for powerful algorithmic approaches that combine the full power of combinatorial search, numerical optimization, and statistical learning. I will discuss challenges and opportunities in these directions and how they affect the practicality of autonomy.
Biography: Sicun Gao is an Assistant Professor in Computer Science and Engineering at UC San Diego. He works on automated reasoning and design automation for autonomous and cyber-physical systems. He received BS from Peking University, PhD from Carnegie Mellon University, and was a postdoctoral researcher at MIT. His awards include the Air Force Young Investigator Award, Silver Medal for the Kurt Godel Research Fellowship Prize, and Honorable Mention for the CMU School of Computer Science Distinguished Doctoral Dissertation.
Host: Paul Bogdan