Yuanyuan Shi

Yuanyuan Shi is an Assistant Professor in the Electrical and Computer Engineering Department at the University of California, San Diego. Her research interests include energy systems, cyber-physical systems, and machine learning for energy management. Shi's work on input convex neural networks for building energy management has been applied in production, enhancing existing data-driven and linear control methods. She has also contributed to the integration of energy storage, which has been commercialized for datacenter battery control and grid frequency regulation. She joined UC San Diego in July 2021, previously serving as a postdoctoral fellow at Caltech and holding a Ph.D. from the University of Washington.

Yuanyuan Shi is an Assistant Professor at UCSD, specializing in Power systems, Control, and Machine learning. Her research focuses on integrating advanced machine learning techniques with control theory to address challenges in power systems and energy management. She has published extensively on topics such as optimal battery control, frequency regulation, and data-driven voltage regulation. Her work emphasizes the development of robust and efficient algorithms for real-time control and optimization in complex systems. Shi's research also explores the application of reinforcement learning and neural networks in energy systems, aiming to improve sustainability and operational efficiency.