Brad Knox

Brad Knox is a Research Associate Professor in the Department of Computer Science at the University of Texas at Austin. His work bridges machine learning, human-computer interaction, and computational cognitive science, with a focus on developing systems that learn from human feedback.

• Key research areas: Reinforcement Learning, Human-AI Interaction, Reward Design, Autonomous Systems
• Notable contributions: TAMER framework for human-guided learning, empirical studies on reward misdesign, and human preference modeling for autonomous agents

Research Trends: His recent work (2023-2025) emphasizes reward alignment, safety in autonomous systems, and preference-based learning frameworks. Earlier studies (2012-2020) established foundational methods for integrating human feedback into reinforcement learning architectures and exploring behavioral signatures in decision-making.

Scientific Honors:

• Bert Kay Dissertation Award (2013)
• Victor Lesser Distinguished Dissertation Award (IFAAMAS, Runner-up, 2013)
• NSF SBIR Grant (PI, 2016)
• NSF Graduate Research Fellowship (2008-2011)
• IEEE Intelligent Systems AI 10 to Watch (2013)

Teaching & Leadership: Knox served as Principal Lecturer for MIT's Interactive Machine Learning course (2013) and held organizational roles at major conferences including Reinforcement Learning Conference (Scheduling Chair, 2025) and RLDM workshop (Co-chair, 2022).