Assistant Professor, The George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology.
Adjunct Assistant Professor, The School of Electrical and Computer Engineering, Georgia Institute of Technology.
Director, Laboratory for Intelligent Decision and Autonomous Robots (LIDAR)
Before joining Georgia Tech, I was a Postdoctoral Fellow at the Agile Robotics Laboratory, Computer Science, SEAS, Harvard University, and worked with Scott Kuindersma (now at Boston Dynamics). I received my Ph.D. from the Human Centered Robotics Laboratory with Luis Sentis, Mechanical Engineering, The University of Texas at Austin in 2016. I obtained the Master degree from HCR Lab in 2013 and Robotics Portfolio Program Degree in 2016, at UT Austin. I received my Bachelor degree in Automation, Department of Control Science and Engineering, Harbin Institute of Technology in 2011. During 2009-2011, I worked as an undergraduate researcher with Huijun Gao and Lixian Zhang at the Institute of Intelligent Control and Systems.
The LIDAR group at Georgia Tech is looking for highly motivated graduate students, postdocs, and visiting scholars. Candidates with backgrounds in robotics, optimization, machine learning, and control are preferred. If you are interested in our research, please send an email to email@example.com. We will get back to you soon if your background could be a good match.
Our research interests lie broadly in planning, control, optimization, and machine learning algorithms of highly dynamic, under-actuated, autonomous, and human-centered robots. We are particularly interested in research directions: (i) robust trajectory optimization of contact-rich locomotion and manipulation; (ii) distributed optimization algorithms for robots with highly complex dynamics and soft contact models; (iii) task and motion planning for robot navigation in complex environments; (iv) optimal motion planning and control of legged locomotion over rough terrain; (v) safe and verifiable reinforcement learning for dynamic legged locomotion; (vi) lower-limb exoskeleton control with robustness and safety guarantees.
We are especially interested in computationally efficient optimization algorithms for challenging robotics problems, for which robust, autonomous, agile, and real-time performance are formally guaranteed. Our long-term goal is to devise and generalize algorithmic approaches for compliant and collaborative humanoid and mobile robots, and human assistive devices, operating in cluttered environments and working alongside humans.