CSCE Colloquium

Friday, September 4, 11:00 a.m.
Innovation Center, Room 2277
550 Assembly St

Abstract
A particularly challenging problem in AI safety is providing guarantees on the behavior of high-dimensional autonomous systems. Verification approaches centered around reachability analysis fail to scale, and purely statistical approaches are constrained by the distributional assumptions about the sampling process. Instead, we pose a distributionally robust version of the statistical verification problem for black-box systems, where our performance guarantees hold over a large family of distributions. This talk describes an approach based on uncertainty quantification using concepts from imprecise probabilities. A central piece of our approach is an ensemble technique called Imprecise Neural Networks, which provides the uncertainty quantification. Additionally, we solve the allied problem of exploring the input set using active learning. The active learning uses an exhaustive neural-network verification tool Sherlock to collect samples. An evaluation on multiple physical simulators in the openAI gym Mujoco environments with reinforcement-learned controllers demonstrates that our approach can provide useful and scalable guarantees for high-dimensional systems.
 

Bio:

Dr. Ivan Ruchkin is a Malachowsky Family Endowed Rising Star Assistant Professor in the Department of Electrical & Computer Engineering at the University of Florida.  His research improves the safety, reliability, interpretability, and trustworthiness of autonomous systems by advancing their modeling, analysis, verification, monitoring, and prediction.

Previously, Ivan was a postdoctoral researcher at the PRECISE Center at the University of Pennsylvania. He received his PhD in Software Engineering from Carnegie Mellon University (CMU) and a Specialist Degree in Applied Mathematics and Computer Science (with honors) from the Lomonosov Moscow State University. Ivan also held several positions in the industry and government, including at the Air Force Research Lab (AFRL) and NASA Jet Propulsion Lab (JPL).

Ivan’s research on assuring cyber-physical systems has been widely recognized. He was fortunate to receive the NSF CAREER Award, the TCCPS Early Career Award for rigorous assurance of learning-enabled cyber-physical systems, and the Frank Anger Memorial Award for the crossover of ideas between the SIGSOFT and SIGBED communities. Ivan's work was recognized with multiple Best Paper/Poster/Demo Awards as well as a Gold Medal in the ACM SRC student competition at the MODELS conference.