I am a practical theoretician, interested in developing theoretical foundations for designing principled algorithms that can efficiently tackle real-world challenges. My research studies learning efficiency, structural properties, and uncertainties in sequential decision making, especially in robotics problems. My recent works focus on reinforcement learning, imitation learning, and lifelong learning. Previously, I worked on online learning, Gaussian processes, and integrated motion planning and control.
I received PhD in Robotics from Georgia Tech, where I was advised by Byron Boots at Institute for Robotics and Intelligent Machines. Before that, I received MS in Mechanical Engineering, and double degrees of BS in Mechanical Engineering and BS in Electrical Engineering from National Taiwan University. See more details on my personal website.