My research goal is to seek for efficient, effective, and elegant solutions to the motion synthesis problem on high-dimensional robotic systems that have hybrid dynamics from the physical interactions with complex environment. I am always exploring new tools and techniques that potentially can solve or help to solve the problems I am interested in. My research so far has been mainly focused on bipedal robotic locomotion, including dynamic motion planning and feedback control synthesis, using a variety of techniques from dynamics modeling, control theory, numerical optimization, and data-driven approximations. My research interests span from robot design, mechanics, nonlinear dynamics and control, and optimization to machine learning, centering around robotic legged locomotion.

Brief academic trajectory: I obtained my PhD from Caltech in 2021, working with Aaron Ames on nonlinear control and bipedal locomotion. After my PhD, I stayed as a postdoc in the same lab prior to joining UW-Madison. Before coming to Caltech, I worked with Daniel Goldman at Georgia Tech on locomotion on graular terrain. I finished my BS degree in mechanical engineering from Tongji University in 2013 and MS degree from Northwestern University in 2015. During my MS study, I worked with Paul Umbanhowar and Kevin Lynch on robotic manipulation.