Seminar - Reasoning Numerically
Venue: Room T1, Building E, Viale Regina Elena 295
Speaker: Prof. Sicun Gao - University of California, San Diego. https://scungao.github.io/
Title: Reasoning Numerically
Abstract: Highly-nonlinear continuous functions have become a pervasive model of computation. Despite newsworthy progress, the practical success of “intelligent” computing is still restricted by our ability to answer questions regarding their reliability and quality: How do we rigorously know that a system will do exactly what we want it to do and nothing else? For traditional software and hardware systems that primarily use rule-based designs, automated reasoning has provided the fundamental principles and widely-used tools for ensuring their quality. However, the rigid symbolic formulations of typical automated reasoning methods often make them unsuitable for dealing with computation units that are driven by numerical and data-driven approaches. I will overview some of our attempts in bridging this gap. I will highlight how the core challenge of NP-hardness is shared across discrete and continuous domains, and how it motivates us to seek the unification of symbolic, numerical, and statistical methods towards better understanding and handling of the curse of dimensionality.
Bio: Sicun Gao is an Associate Professor in Computer Science and Engineering at the University of California, San Diego. He works on search and optimization algorithms for improving the quality of automation and autonomous systems. He is a recipient of the Air Force Young Investigator Award, NSF Career Award, Amazon Research Award, and Silver Medal for the Kurt Godel Research Prize. He received his PhD from Carnegie Mellon University and was a postdoctoral researcher at CMU and MIT.