Electrical and Computer Engineering Seminar Series

Scalable and Guaranteed Computation: Optimization and Machine Learning for the Future Electric Grid

Speaker

Dr Richard Zhang

Institute

Assistant Professor, Department of Electrical and Computer Engineering , University of Illinois at Urbana-Champaign, USA

Time & Place

Fri, 20 Dec 2019 14:00:00 NZDT in Link 309 Lecture Theatre

Abstract

Computation promises to greatly enhance the electric grid through optimization and machine learning. However, many computational problems remain unsolved at the scale, speed, and quality necessary for the real world, due to issues of complexity and nonconvexity. In the first part of this talk, we solve the optimization problem known as optimal power flow in guaranteed near-linear time and linear memory. Our key insight is use domain-specific techniques to exploit the graph theoretic notion of bounded treewidth. We give case studies on real-world electric grids with tens of thousands of vertices. We also extend our insights to solve other important graph-based optimization problems in transportation systems. In the second part of this talk, we make safety-critical guarantees for the learning problem known as power system state estimation. We draw a connection with the nonconvex low-rank matrix recovery problem in recommendation systems, and prove that 1/2-restricted isometry is necessary and sufficient for guaranteed success in both classes of problems. We discuss implications for future work in the industrial applications of artificial intelligence. 

Biography

Richard Y. Zhang is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Illinois at Urbana-Champaign. He received the B.E. (hons) degree from the University of Canterbury, Christchurch, New Zealand, in 2009, and the S.M. and Ph.D. degrees in Electrical Engineering and Computer Science from MIT in 2012 and 2017. He was a postdoc at UC Berkeley from 2017-2019. His research interests are in optimization and machine learning, and their applications in power and energy systems.