Aravind Rajeswaran is a PhD student at the University of Washington with Sham Kakade and Emo Todorov. He is interested in the mathematical foundations and applications of deep learning. His current work focuses on leveraging learning and optimization to endow robots with diverse skills. Aravind has contributed towards algorithmic foundations of deep reinforcement learning (computationally efficient natural gradient), meta-learning (online MAML and implicit gradient methods), model-based control (combining MPC with value learning), and simulation to reality transfer (ensemble methods for robust control). Aravind has received a best thesis award for undergraduate research work at IIT Madras in 2015 and a best paper award from SIMPAR 2018.