Computer Science Department
Carnegie Mellon University
ebrunskill at cs dot cmu dot edu
My research interests include machine learning,
sequential decision making under uncertainty, and artificial intelligence.
I am particularly interested in using these approaches to
transform the quality of education.
I am also interested in using information and
communication technologies for international development and health applications.
Please see a fairly recent cv for more information on
me and my work. I did my PhD at MIT and my postdoc at the University of California, Berkeley. At CMU I am also affiliated with the Machine Learning Department.
- Summer 2013: Yun-en Liu's EDM paper is nominated for best paper. Congratulations Yun-en!
- Summer 2013: Paper with Mohammad Azer and Alessandro Lazaric on ("Regret Bounds for Reinforcement Learning with Policy Advice") accepted to ECML
- Spring 2013: Paper with Lihong Li on multi-task reinforcement learning accepted to UAI
- Spring 2013: Two papers accepted to Education Data Mining. Congratulations to Yun-en Liu and Anna Rafferty!
- Dec 2012: I gave a joint invited tutorial with Geoff Gordon on "Machine Learning for Student Learning" at NIPS.
- Fall 2012: I had a great time presenting my group's work as part of
CMU's IdeasLab at the World Economic Forum in Tianjin, China.
- Summer 2012: I received a Google Research Award. Thanks Google!
- Summer 2012: Paper on Incentive Decision Processes (with Sashank Reddi) accepted to UAI.
- Summer 2012: Jung In Lee and I had a paper nominated for best paper at the International Conference on Educational Data Mining. Congratulations to Jung In!
- Spring 2012: I'm delighted to have been selected as a
Microsoft Research Faculty Fellow.
- Spring 2012: I co-taught (with Professor Manuela Veloso) a new class on Artificial Intelligence for Health & Sustainability.
- Summer 2011: I co-organized a IJCAI workshop on Decision Making in Partially Observable, Uncertain Worlds
Selected Publications (Full list available here)
- RAPID: A reachable anytime planner for imprecisely-sensed domains
E.Brunskill and S.Russell
Uncertainty in Artificial Intelligence (UAI) 2010
- Provably efficient learning with typed parametric models
E.Brunskill, B. Leffler, L. Li, M. Littman, and N. Roy.
Journal of Machine Learning Research, 2009
- Evaluating an adaptive multi-user educational tool for low-resource regions
E.Brunskill, S.Garg, C.Tseng, J.Pal and L.Findlater
International Conference on Information and Communication Technologies and Development (ICTD) 2010