Emma Brunskill

Emma Brunskill is an assistant professor in the department of computer science. She is affiliated with the Stanford Artificial Intelligence Laboratory and the Stanford Statistical Machine Learning Group. Before coming to Stanford, Brunskill was an assistant professor at Carnegie Mellon University. She holds a BS from the University of Washington, an MS from Oxford University, and a DPhil from MIT.

Brunskill’s research centers on reinforcement learning in high stakes scenarios. One particular focus is on settings in which learning from experience is costly or risky, such as in educational software, healthcare decision making, robotics or people-facing applications. Her work has been honored by early faculty career awards from the National Science Foundation, Office of Naval Research, and Microsoft Research, as well as by several best research paper awards.

Academic History

• 2009 Doctor of Philosophy, Computer Science
  Massachusetts Institute of Technology
• 2002 Master of Science, Neuroscience
  Oxford University
• 2000 Bachelor of Science, Computer Engineering and Physics, Magna Cum Laude, With Honors
  University of Washington

Selected Awards and Honors

• Alumni award, Allen School of Computer Science and Enginnering, University of Washington
• Keynote at International Conference on Automated Planning and Scheduling (2022)
• Best paper award RLDM (2022)
• Best paper at "Bridging the Gap: From Machine Learning Research to Clinical Practice" NeurIPS Workshop (2021)
• Best short paper Intelligent Tutoring Systems (2021)
• Keynote at Machine Learning for Healthcare (2020)
• Keynote at Conference on Learning Theory (2019)
• Keynote at Uncertainty in Artificial Intelligence (2019)
• Google Faculty Research Award (2019)
• Keynote at Human Computation and Crowdsourcing (2017)
• Invited Tutorial at NeurIPS (2017)
• Best paper Uncertainty in Artificial Intelligence (2017)
• Best paper nominee Educational Data Mining (2017)
• Early Career Talk International Joint Conference on AI (2017)
• Best paper award RLDM (2015)
• Office of Naval Research Young Investigator Award (YIP) (2015) (Press release)
• NSF CAREER award (2014)
• Best paper nominee CHI (2014)
• Best paper nominee Educational Data Mining (2013)
• Google Faculty Research Award (2012)
• Invited Tutorial at NeurIPS (joint with Geoff Gordon) (2012)
• Microsoft Research Faculty Fellow (2012) (1 of 7 worldwide)
• Best paper nominee Educational Data Mining (2012)
• Rhodes scholar