World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
44
Citations
8822
World Ranking
7533
National Ranking
3276

Overview

Emma Brunskill is affiliated with Stanford University in the United States. Their research primarily focuses on the field of Computer Science, with significant contributions across various subfields including Artificial Intelligence, Management Science and Operations Research, Statistics and Probability, Computer Science Applications, and Economics and Econometrics.

Their main research topics include:

  • Advanced Bandit Algorithms Research
  • Reinforcement Learning in Robotics
  • Advanced Causal Inference Techniques
  • Machine Learning and Algorithms
  • Intelligent Tutoring Systems and Adaptive Learning
  • Machine Learning and Data Classification
  • Health Systems, Economic Evaluations, Quality of Life

Emma Brunskill has published extensively, with a considerable number of works appearing in the following venues:

  • arXiv (Cornell University)
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • Journal of the American Statistical Association
  • Proceedings of the National Academy of Sciences
  • Machine Learning

Among notable recent papers are:

  • "On the Opportunities and Risks of Foundation Models," 2021, arXiv (Cornell University)
  • "Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learning," 2020, arXiv (Cornell University)
  • "Scaling up behavioral science interventions in online education," 2020, Proceedings of the National Academy of Sciences
  • "Learning When-to-Treat Policies," 2020, Journal of the American Statistical Association

Their frequent coauthors include Allen Nie, Nigam H. Shah, Stefan Wager, Chris Piech, and Sharad Goel, with collaborative works ranging from 5 to 13 joint publications with these researchers.

Best Publications

  • On the Opportunities and Risks of Foundation Models.

    Rishi Bommasani;Drew A. Hudson;Ehsan Adeli;Russ Altman

  • Building peer-to-peer systems with chord, a distributed lookup service

    F. Dabek;E. Brunskill;M.F. Kaashoek;D. Karger

  • Data-efficient off-policy policy evaluation for reinforcement learning

    Philip S. Thomas;Emma Brunskill

  • Designing mobile interfaces for novice and low-literacy users

    Indrani Medhi;Somani Patnaik;Emma Brunskill;S.N. Nagasena Gautama

  • New Potentials for Data-Driven Intelligent Tutoring System Development and Optimization

    Kenneth R. Koedinger;Emma Brunskill;Ryan Shaun Joazeiro de Baker;Elizabeth A. McLaughlin

  • Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learning

    Peter Henderson;Jieru Hu;Joshua Romoff;Emma Brunskill

  • Offline policy evaluation across representations with applications to educational games

    Travis Mandel;Yun-En Liu;Sergey Levine;Emma Brunskill

  • Sample complexity of episodic fixed-horizon reinforcement learning

    Christoph Dann;Emma Brunskill

  • Unifying PAC and Regret: Uniform PAC Bounds for Episodic Reinforcement Learning

    Christoph Dann;Tor Lattimore;Emma Brunskill

  • QuizBot: A Dialogue-based Adaptive Learning System for Factual Knowledge

    Sherry Ruan;Liwei Jiang;Justin Xu;Bryce Joe-Kun Tham

  • Efficient Exploration Through Bayesian Deep Q-Networks

    Kamyar Azizzadenesheli;Emma Brunskill;Animashree Anandkumar

  • Preventing undesirable behavior of intelligent machines.

    Philip S. Thomas;Bruno Castro da Silva;Andrew G. Barto;Stephen Giguere

  • Tighter Problem-Dependent Regret Bounds in Reinforcement Learning without Domain Knowledge using Value Function Bounds

    Andrea Zanette;Emma Brunskill

  • The Impact on Individualizing Student Models on Necessary Practice Opportunities.

    Jung In Lee;Emma Brunskill

  • Topological mapping using spectral clustering and classification

    E. Brunskill;T. Kollar;N. Roy

  • Evaluating the accuracy of data collection on mobile phones: a study of forms, sms, and voice

    Somani Patnaik;Emma Brunskill;William Thies

  • Faster Teaching via POMDP Planning.

    Anna N. Rafferty;Emma Brunskill;Thomas L. Griffiths;Patrick Shafto

  • Efficient planning under uncertainty with macro-actions

    Ruijie He;Emma Brunskill;Nicholas Roy

  • PAC-inspired Option Discovery in Lifelong Reinforcement Learning

    Emma Brunskill;Lihong Li

  • Faster teaching by POMDP planning

    Anna N. Rafferty;Emma Brunskill;Thomas L. Griffiths;Patrick Shafto

  • Policy Certificates: Towards Accountable Reinforcement Learning

    Christoph Dann;Lihong Li;Wei Wei;Emma Brunskill

  • Learning Near Optimal Policies with Low Inherent Bellman Error

    Andrea Zanette;Alessandro Lazaric;Mykel Kochenderfer;Emma Brunskill

Frequent Co-Authors

Alessandro Lazaric
Alessandro Lazaric Facebook (United States)
Finale Doshi-Velez
Finale Doshi-Velez Harvard University
Lihong Li
Lihong Li Amazon (United States)
Zoran Popović
Zoran Popović University of Washington
Vincent Aleven
Vincent Aleven Carnegie Mellon University
Mykel J. Kochenderfer
Mykel J. Kochenderfer Stanford University
Peter Henderson
Peter Henderson University of Oxford
James A. Landay
James A. Landay Stanford University
Alekh Agarwal
Alekh Agarwal Google (United States)

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