Assistant Professor, Computer Science
ebrun at cs dot stanford dot edu
My goal is to increase human potential through advancing interactive
machine learning. Revolutions in storage and computation have
made it easy to capture and react to sequences of decisions
made and their outcomes. Simultaneously, due to the
rise of chronic health conditions, and
demand for educated workers, there is an urgent
need for more scalable solutions to
assist people to reach their full potential.
Interactive machine learning systems could be a
key part of the solution. To enable this, my
lab's work spans from advancing our theoretical
understanding of reinforcement learning, to
developing new self-optimizing tutoring systems
that we test with learners and in the classroom.
Our applications focus
on education since education can radically transform
the opportunities available to an individual.
Interested students: Unfortunately I am unable to respond to most emails about openings for internships, graduate and postdoctoral positions in my group. Admission decisions are made at the department level so I will not be able to respond about your likelihood of acceptance or possibility of working with me. If you are already enrolled at Stanford or have been admitted, please feel free to reach out if you're interested in discussing research opportunities in my group.
- Best paper Uncertainty in AI (UAI) 2017
- Best paper nominee Educational Data Mining (2017)
- Selected for Early Career Talk, IJCAI (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)
- Microsoft Research Faculty Fellow (2012) (1 of 7 worldwide)
- Best paper nominee Educational Data Mining (2012)
- Upcoming: Delighted to have the opportunity to give a keynote at the Conference on Human Computation and Crowdsourcing (HCOMP) and an invited tutorial at NIPS 2017
- Sep 2017: Congratulations to Karan on being selected as a Siebel scholar!
- Sep 2017: Congratulations to Shayan, Phil, Daniel (and collaborators Alessandro, Matteo, and Tor) on 2 NIPS spotlights and 1 poster!
- Summer 2017: Congratulations to Shayan Doroudi and Phil Thomas for best paper at UAI!
- Summer 2017: Phil Thomas finishes his postdoc and heads to be an assistant professor at UMass Amherst. Congratulations Phil!
- Summer 2017: Travis Mandel defends his thesis and is off to be an assistant professor at the University of Hawaii. Congratulations Travis!
- Spring 2017: Just joined Stanford!
- Excited to be program co-chair for Reinforcement Learning and Decision Making (RLDM) 2017 with
- Dec 2016: Gave invited talks at 3 NIPS workshops (Education, Gaming and Interactive ML)
- Oct 2016: Awesome to help co-organize Rising Stars in EECS: so inspired by the participants!
- August 2016: Congratulations to Joe Runde, Rika Antonova and Qi Guo on finishing their masters!
- Jun 2016: Had a great time giving talks at 3 ICML workshops (ML & Education, Abstraction and RL, and Data Efficient ML)
- Apr 2016: 3 IJCAI and 2 ICML papers accepted. Congratulations Li, Qi, Travis, Yun-En, Phil, and Christoph!
- Mar 2016: My NYT piece on the significance and implications of AlphaGo
- Jan 2016: Invited panelist at the NYU Future of AI Symposium
- Dec 2015: Congratulations to Min Yung Lee on graduating with his masters in maching learning!
- Aug 2015: Delighted to be a co-PI on a NSF BIGDATA award with PI Zoran Popovic and co-PI Min Li on machine learning optimization for education!
- June 2015: Congratulations to Shayan Doroudi for being selected as a PIER fellow!
- May 2015: Congratulations to Yun-En Liu on a successful PhD defense!
- Dec 2014: Great to give 3 invited presentations at NIPS workshops
- Winter 2014: Enjoyed presenting "Learning to Improve Learning" as part of CMU's IdeasLab at the World Economic Forum in Davos
I am fortunate to get to work with a great set of individuals and I am currently advising
- Recent Alumni
- Philip Thomas (postdoc, now asst prof at UMass Amherst)
- Travis Mandel (joint with Zoran Popovic, University of Washington, now asst prof at University of Hawaii)
- Li Zhou (advised masters capstone, now ML researcher at Amazon)
- Rika Antonova (advised robotics masters thesis, now PhD student in Sweden)
- Joe Runde (advised ML masters, now at IBM)
- Dexter Lee (advised ML masters, now at Twitter)
- Yun-En Liu (co-advised PhD thesis w/Zoran Popovic, now at Enlearn)
- Joseph Rollinson (advised Honors senior thesis, now at Duolingo)
Recent teaching: (Fall 2015) Real life Reinforcement Learning.
If you're an undergraduate or graduate already enrolled at Stanford interested in helping us transform and scale personalized learning, or tackling new challenges in sequential decision making under uncertainty, please get in touch!