I am an Assistant Professor and the director of the Personal Autonomous Robotics Lab (PeARL) in the Department of Computer Science at the University of Texas at Austin. I am also a core faculty member in the interdepartmental robotics group at UT.

The goal of my research is to enable personal robots to be deployed in the home and workplace with minimal intervention by robotics experts. In settings such as these, robots do not operate in isolation, but have continual interactions with people and objects in the world. With this in mind, we focus on developing algorithms to solve problems that robot learners encounter in real-world interactive settings. Thus, our work draws roughly equally from both machine learning and robotics, including topics such as imitation learning, reinforcement learning, probabilistic safety, manipulation, and human-robot interaction.

I am a recipient of the of the NSF CAREER Award, the AFOSR Young Investigator Award, and the UT Austin CNS Teaching Excellence Award.

A recent talk on "Scaling Probabilistically Safe Learning to Robotics", given virtually at Carnegie Mellon University on 9/11/20, as part of the Robotics Institute Seminar Series:

Representative Publications

Safe Learning Imitation Learning Reinforcement Learning and Planning Active Learning Multimodal Learning