Time: 3-4 pm Eastern Time, Sep 12 (Friday), 2025

Deep reinforcement learning (RL) is a promising tool for real-time control of complex dynamical systems such as power and energy systems, yet its deployment is often hindered by the lack of explicit stability guarantees. In this talk, I will present a stability-constrained RL framework for power grid control, where we show that monotonicity in control policies implies Lyapunov stability. By parameterizing policies with a novel monotone neural network design, we ensure stability by design, achieving better control performance and rigorous guarantee compared to standard RL methods. In the second part, I will introduce a generative AI approach for analytical Lyapunov function discovery. Using transformer-based models trained with RL and verification feedback, our framework can generate interpretable symbolic Lyapunov functions for nonlinear systems, including high-dimensional and non-polynomial cases. Together, these efforts highlight new pathways toward principled, stability-guaranteed AI for real-world control. 

Yuanyuan Shi is an Assistant Professor of Electrical and Computer Engineering at the University of California San Diego. She received her Ph.D. in Electrical and Computer Engineering (ECE), masters in ECE and Statistics, all from the University of Washington, in 2020. From 2020 to 2021, she was a Postdoctoral Scholar at Caltech. Her research focuses on machine learning, dynamical systems and control, with applications to sustainable energy systems. She received the NSF CAREER Award in 2025, Schmidt Sciences AI2050 Early Career Fellowship in 2024, and best paper finalists in L4DC 2025 and ACM e-Energy 2022.