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Brad Knox

Research Associate Professor

Brad Knox is a Research Fellow with a diverse research background encompassing machine learning, human-computer interaction, and computational models of human behavior for cognitive science research. His research primarily focuses on the human side of reinforcement learning, including pioneering work on human-in-the-loop reinforcement learning that earned him the 2012 best dissertation award for the UT Austin Department of Computer Science.

Research

Select Publications

S Amershi, M Cakmak, WB Knox, T Kulesza. 2014. Power to the people: The role of humans in interactive machine learning. AI Magazine Vol. 35.

S Booth, BW Knox, J Shah, S Niekum, P Stone, A Allievi. 2023. The perils of trial-and-error reward design: misdesign through overfitting and invalid task specifications.

Toward Believable Acting for Autonomous Animated Characters. 2022. Toward Believable Acting for Autonomous Animated Characters.

WB Knox, S Hatgis-Kessell, S Booth, S Niekum, P Stone, A Allievi. 2022. Models of human preference for learning reward functions.

WB Knox, A Allievi, H Banzhaf, F Schmitt, P Stone. 2021. Reward (Mis)design for Autonomous Driving.

View complete list

Awards & Honors

  • 2018 - Hasbro Emerging Innovator Award (Finalist; lead technologist on the project for Dash Robotics)
  • 2016 - Awarded NSF Small Business Innovation Research (SBIR) grant as PI
  • 2013 - AI 10 to Watch by IEEE Intelligent Systems
  • 2013 - Bert Kay Dissertation Award (for best dissertation from UT Austin Computer Science)
  • 2013 - IFAAMAS-12 Victor Lesser Distinguished Dissertation Award (Runner-up)
  • 2013 - ICSR Best Paper Award
  • 2012 - Ro-Man CoTeSys Cognitive Robotics Best Paper (Finalist)
  • 2010 - AAMAS Pragnesh Jay Modi Best Student Paper Award
  • 2008 to 2011 - NSF Graduate Research Fellowship

Contact Info

Brad Knox
Research Associate Professor
Homepage
(512)-542-3333
bradknox@cs.utexas.edu
GDC 3.404
CV