World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
44
Citations
8822
World Ranking
7532
National Ranking
3276

Emma Brunskill publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Emma Brunskill sits on this spectrum.

32–41 publications: 7 scientists 42–51 publications: 22 scientists 52–61 publications: 82 scientists 62–71 publications: 134 scientists 72–81 publications: 249 scientists 82–91 publications: 324 scientists 92–101 publications: 421 scientists 102–111 publications: 420 scientists 112–121 publications: 497 scientists 122–131 publications: 544 scientists 132–141 publications: 555 scientists 142–151 publications: 609 scientists 152–161 publications: 559 scientists 162–171 publications: 534 scientists 172–181 publications: 556 scientists 182–191 publications: 583 scientists 192–201 publications: 519 scientists 202–211 publications: 508 scientists 212–221 publications: 490 scientists 222–231 publications: 437 scientists 232–241 publications: 423 scientists 242–251 publications: 408 scientists 252–261 publications: 377 scientists 262–271 publications: 301 scientists 272–281 publications: 335 scientists 282–291 publications: 320 scientists 292–301 publications: 293 scientists 302–311 publications: 250 scientists 312–321 publications: 238 scientists 322–331 publications: 206 scientists 332–341 publications: 209 scientists 342–351 publications: 208 scientists 352–361 publications: 162 scientists 362–371 publications: 176 scientists 372–381 publications: 127 scientists 382–391 publications: 158 scientists 392–401 publications: 128 scientists 402–411 publications: 104 scientists 412–421 publications: 94 scientists 422–431 publications: 99 scientists 432–441 publications: 83 scientists 442–451 publications: 108 scientists 452–461 publications: 73 scientists 462–471 publications: 77 scientists 472–481 publications: 69 scientists 482–491 publications: 84 scientists 492–501 publications: 62 scientists 502–511 publications: 54 scientists 512–521 publications: 57 scientists 522–531 publications: 51 scientists 532–541 publications: 51 scientists 542–551 publications: 32 scientists 552–561 publications: 38 scientists 562–571 publications: 28 scientists 572–581 publications: 43 scientists 582–591 publications: 33 scientists 592–601 publications: 41 scientists 602–611 publications: 32 scientists 612–621 publications: 28 scientists 622–631 publications: 25 scientists 632–641 publications: 27 scientists 642–651 publications: 17 scientists 652–661 publications: 20 scientists 662–671 publications: 17 scientists 672–681 publications: 15 scientists 682–691 publications: 14 scientists 692–701 publications: 21 scientists 702–711 publications: 13 scientists 712–721 publications: 12 scientists 722–731 publications: 19 scientists 732–741 publications: 14 scientists 742–751 publications: 12 scientists 752–761 publications: 10 scientists 762–771 publications: 10 scientists 772–781 publications: 11 scientists 782–791 publications: 10 scientists 792–801 publications: 11 scientists 802–811 publications: 8 scientists 812–821 publications: 8 scientists 822–831 publications: 7 scientists 832–841 publications: 11 scientists 842–851 publications: 10 scientists 852–861 publications: 5 scientists 862–871 publications: 9 scientists 872–881 publications: 4 scientists 882–891 publications: 6 scientists 892–901 publications: 3 scientists 902–911 publications: 6 scientists 912–921 publications: 3 scientists 922–931 publications: 2 scientists 932–941 publications: 2 scientists 942–951 publications: 2 scientists 952–961 publications: 3 scientists 962–971 publications: 3 scientists 972–981 publications: 3 scientists 982–990 publications: 5 scientists 991+ publications: 100 scientists
32 publications 991+

This scientist: 186 publications — 41st percentile

41% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 991 publications or more.

Emma Brunskill D-index placement in Computer Science in 2026

The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Emma Brunskill sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 983 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 968 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 763 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 518 scientists 54–55 D-Index: 500 scientists 56–57 D-Index: 458 scientists 58–59 D-Index: 400 scientists 60–61 D-Index: 337 scientists 62–63 D-Index: 308 scientists 64–65 D-Index: 292 scientists 66–67 D-Index: 249 scientists 68–69 D-Index: 213 scientists 70–71 D-Index: 192 scientists 72–73 D-Index: 189 scientists 74–75 D-Index: 165 scientists 76–77 D-Index: 139 scientists 78–79 D-Index: 119 scientists 80–81 D-Index: 121 scientists 82–83 D-Index: 113 scientists 84–85 D-Index: 88 scientists 86–87 D-Index: 87 scientists 88–89 D-Index: 75 scientists 90–91 D-Index: 69 scientists 92–93 D-Index: 57 scientists 94–95 D-Index: 46 scientists 96–97 D-Index: 38 scientists 98–99 D-Index: 34 scientists 100–101 D-Index: 36 scientists 102–103 D-Index: 27 scientists 104–105 D-Index: 37 scientists 106–107 D-Index: 18 scientists 108–109 D-Index: 31 scientists 110–111 D-Index: 19 scientists 112–113 D-Index: 16 scientists 114–115 D-Index: 12 scientists 116–117 D-Index: 20 scientists 118–119 D-Index: 15 scientists 120–121 D-Index: 5 scientists 122–123 D-Index: 20 scientists 124–125 D-Index: 8 scientists 126–127 D-Index: 5 scientists 128–129 D-Index: 7 scientists 130 D-Index: 3 scientists 131+ D-Index: 98 scientists
30 D-Index 131+

This scientist: 44 D-Index — 48th percentile

48% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

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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