Research

My research aims to understand the fundamental value of information and structure in sequential decision-making, and to develop reinforcement learning algorithms that efficiently exploit them. Examples of such information and structure include imperfect predictions, decomposable dynamics, Bellman convexity, Lyapunov stability, and chance constraints.

My applied research focuses on emerging challenges in power and energy systems, particularly reliable grid operation and the integration of AI data centers into power grids.