Daniel S. Brown

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Personal Information

I am a second-year computer science PhD Student at UT Austin. I work in the Personal Autonomous Robotics Lab (PeARL), with Scott Niekum. I am interested in safe learning from demonstration. In particular I am developing methods that allow a robot to reason about the performance a policy learned from a limited number of demonstrations. My recent work shows how a learning agent can use Bayesian Inverse Reinforcement Learning to calculate accurate probabilistic performance bounds, without knowing the demonstrator's reward function.

Prior to coming to UT I worked as a research scientist at the Air Force Research Lab's Information Directorate in Rome, New York. I earned my master's degree in Computer Science from Brigham Young University under the advisement of Mike Goodrich. I also obtained my bachelor's degree in Mathematics at Brigham Young University and completed an honors thesis under the advisement of Sean Warnick.

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