Scott Niekum

Scott Niekum is an Associate Professor in the Manning College of Information and Computer Sciences (CICS) at the University of Massachusetts Amherst, where he directs the Personal Autonomous Robotics Lab (PeARL). Previously, he held assistant and associate professor positions at the University of Texas at Austin (2015–2022). He earned his PhD in Computer Science from UMass Amherst in 2013 and completed a postdoctoral fellowship at Carnegie Mellon University's Robotics Institute (2013–2015).

His research focuses on enabling robots to learn autonomously through interactions with humans and environments, emphasizing safe and efficient algorithms. Key areas include reinforcement learning, imitation learning, human-robot collaboration, and safety guarantees. His work bridges robotics and machine learning to address challenges like reward design, causal reasoning, and generalization from limited data.

Notable contributions include the EMPATHIC framework for task learning via implicit human feedback, the PeARL lab's advancements in gaze-guided robotics, and scalable methods for offline reinforcement learning. He has been recognized with the NSF CAREER Award (2023), AFOSR Young Investigator Award, and teaching excellence honors.

Dr. Niekum actively serves on conference committees (NeurIPS, ICML, RSS) and journals, promoting rigorous research standards. His lab's innovations aim to deploy autonomous systems in real-world settings like homes and workplaces with minimal expert intervention.