Emma Brunskill
Assistant Professor, CS, Affiliate, Machine Learning
Carnegie Mellon University
ebrunskill at cs dot cmu 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.
In March 2017 I will join the CS department at Stanford. I will be taking new students at Stanford in fall 2017.
Selected Awards
- 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 EDM (2013)
- Microsoft Research Faculty Fellow (2012) (1 of 7 worldwide)
- Best paper nominee EDM (2012)
News
- Excited to be program co-chair for Reinforcement Learning and Decision Making (RLDM) 2017 with Nathaniel Daw
- 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 s 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 working with
- Postdoc
- Ph.D.
- Christoph Dann
- Shayan Doroudi
- Zhaohan Daniel Guo
- Yun-En Liu (joint with Zoran Popovic, University of Washington)
- Travis Mandel (joint with Zoran Popovic, University of Washington)
- Yao Liu
- Masters
- Karan Goel
- Rika Antonova
- Joe Runde
- Li Zhou
Recent teaching: (Fall 2015) Real life Reinforcement Learning.
If you're an undergraduate or graduate at CMU interested in helping us transform and scale personalized learning, or tackling new challenges in sequential decision making under uncertainty, please get in touch!
