I joined the Brain team at Google in 2016, working on deep learning, reinforcement learning and robotics. Before that, I was a Member of Technical Staff at the Computational Imaging group at Lytro, working on computer vision, SLAM, light field technology and image processing. I got my PhD of computer science from Georgia Tech in 2015, under the supervision of Greg Turk and Karen Liu.
My research focused on developing computational tools to understand, simulate and control human and animal motions in a complex environment. I developed fast and stable computer programs to simulate complex dynamic systems, such as fluid, soft body and articulated rigid bodies. I applied optimal control and machine learning techniques to enable computers to automatically learn skills inside a complex physical environment.
I am also interested in transferring the control policies that are learned in simulations to real robots. Policies learned in a simulation usually perform poorly on real robots due to the discrepancies between the simulated and the real system. I am developing tools to understand and model such discrepancies. I augmented the physical simulation using real-world data, which not only increases the simulation accuracy, but also improves the real-world performance of the controllers.