I am a practical theoretician, interested in developing theoretical foundations for designing principled algorithms that can efficiently tackle real-world challenges. My research is built on machine learning, optimization, and control theories. My current focus concerns learning efficiency, structural properties, and uncertainties in sequential decision making. Specific topics include reinforcement learning, imitation learning, online learning, meta learning, (large-scale) Gaussian processes, and integrated motion planning and control.