Robust learning and control for safety-critical systems
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. Unlike traditional machine learning tasks such as image classification, 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. In this talk I will focus on my recent works in designing computational methods that meet these requirements. The central theme is to develop robust algorithms with formal performance guarantees for complex systems, with emphasis on urban infrastructures that prioritize human values.
Postdoctoral Researcher, UC Berkeley
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 EECS department at University of California, Berkeley in 2017. His current research interests include optimization, learning and control for safety-critical systems. He was the recipient of the Siebel scholarship, Best Paper Award at the Building and Environment journal, 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, Electronic and Computer Engineering Department Scholarship, School of Engineering Scholarship, and University Scholarship at the Hong Kong University of Science and Technology.