My research interests lie in the theoretical and algorithmic foundations of data science, machine learning, signal processing and inverse problems, with applications in sensing, imaging, decision making, and societal systems, broadly defined. The problems my group studies are often interdisciplinary in nature, lying at the intersection of statistics, learning, optimization, and sensing. My current focus is on reinforcement learning, nonconvex statistical estimation, distributed and federated learning, and generative AI.