Scott Niekum

Scott Niekum's 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, his research focuses on developing algorithms to solve problems that robot learners encounter in real-world interactive settings. Thus, this work draws roughly equally from both machine learning and robotics, including topics such as imitation learning, reinforcement learning, safety, manipulation, and human-robot interaction. Specifically, he is interested in addressing the following questions: How can human demonstrations and interactions be used to bootstrap the learning process? How can robots autonomously improve their understanding of the world through embodied interaction? And how can robots learn from heterogenous, noisy interactions and still provide strong probabilistic guarantees of correctness and safety? 

Niekum is an associate professor in the Manning College of Information and Computer Sciences (CICS) at UMass Amherst, where he directs the Personal Autonomous Robotics Lab (PeARL). Previously, he was an assistant (2015-2021) and associate (2021-2022) professor at The University of Texas at Austin in the Department of Computer Science. Niekum received his PhD in 2013 from UMass Amherst (supervised by Andrew Barto) and was a postdoctoral research fellow at the Carnegie Mellon University Robotics Institute from 2013 to 2015.

Niekum is a recipient of the National Science Foundation CAREER Award and the AFOSR Young Investigator Award. While in his position as an assistant professor at The University of Texas at Austin, he also received the College of Natural Sciences Teaching Excellence Award. Niekum regularly serves as an area chair, associate editor, or reviewer for machine learning and robotics conferences and journals, including NeurIPS, ICML, ICLR, RSS, CoRL, ICRA, IROS, HRI, AAAI, IJCAI, JMLR, IJRR, and TRO.