In the future, data and algorithms will play an increasingly important role in solving societal-scale problems, from improving people’s living conditions to modernizing the electric grid. These problems involve complex physical systems that are safety-critical with humans included, where data could be scarce, corrupted, and even adversarial. Consequently, these systems require robustness guarantees against the uncertainty that arises from (1) anomalous inputs or adversarial attacks, and (2) human interventions; moreover, the algorithms need to be scalable and data-efficient. This talk will focus on designing computational methods that meet these requirements.
In the first part, we will introduce an adversarially robust optimization framework for the quadratic sensing problem motivated by the state estimation problem in power system. We will study the worst-case attack and propose a boundary defense mechanism to defend against it. The method is applied to evaluate the vulnerability of the entire U.S. grid. In the second part of the talk, we will develop formal guarantees of robustness for reinforcement learning. Towards the end, we will also discuss our methods for human-centric learning and control to understand and interact with humans in order to enhance system performance.
Ming Jin is a postdoctoral researcher in the Department of Industrial Engineering and Operations Research at University of California, Berkeley. He received his doctoral degree from the Department of Electrical Engineering and Computer Sciences at University of California, Berkeley. His current research interests include optimization, learning and control for safety-critical systems. His work has been also recognized by several awards, including the Siebel scholarship, Best Paper Award at the Building and Environment journal, Best Paper Award at the International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services, Best Paper Award at the International Conference on Mobile Ubiquitous Computing, Systems, Services and Technologies. His work has been featured in multiple media outlets, including IEEE Spectrum, Berkeley Engineer Magazine, and MIT Technology Review.