Pratik is interested in using reinforcement learning to control agile and dynamic robots in the real world, as well as improving sample efficiency in sim2real transfer. His previous work involved using machine learning for state estimation in human-augmentation robotics as well as online adaptation of machine learning-based control. He holds a B.S. and M.S. in Computer Engineering from Georgia Institute of Technology.