I am a practical theoretician, interested in developing theoretical foundations for designing principled algorithms that can efficiently tackle real-world challenges. Joining learning, optimization, and control theories, my research concerns learning efficiency, structural properties, and uncertainties in sequential decision making. Specific topics include reinforcement learning, imitation learning, online learning, meta learning, 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.