Energy systems, cyber-physical systems, machine learning for energy management
Shi's research interests are in the area of energy systems and cyberphysical systems, spanning from machine learning, statistics to optimization and control. Her work on input convex neural networks for building energy management has been used in production, and significantly improves existing purely data-driven and linear control methods. Also, she has worked on the integration of energy storage, which has been commercialized for datacenter battery control and grid frequency regulation. She's currently working on data-driven control for energy systems and energy market design under multi-agent learning dynamics.
Yuanyuan Shi joined UC San Diego as an assistant professor in the Electrical and Computer Engineering Department in July 2021. Previously, she was a postdoctoral fellow in the Department of Computing and Mathematical Sciences at Caltech, jointly hosted by Adam Wierman and Anima Anandkumar. Before that, she received her Ph.D. from the Department of Electrical and Computer Engineering at the University of Washington in 2020, advised by Baosen Zhang. She holds a Bachelor of Engineering from Nanjing University, China, and Masters of Electrical Engineering and Statistics, both from the University of Washington. She was named a Rising Star in EECS by MIT in 2018 and awarded the inaugural 2020 Scientific Achievement Award by the Clean Energy Institute at University of Washington.