World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
38
Citations
46543
World Ranking
9918
National Ranking
4165

George E. Dahl 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 George E. Dahl 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: 55 publications — 1st percentile

1% of scientists in this discipline score the same or lower.

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

George E. Dahl 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 George E. Dahl 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: 38 D-Index — 30th percentile

30% of scientists in this discipline score the same or lower.

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

Overview

George E. Dahl is affiliated with Google in the United States, where their research primarily spans the field of Computer Science with a focus on Artificial Intelligence. Their work involves various subfields including Computer Vision and Pattern Recognition, Molecular Biology, Statistical and Nonlinear Physics, and Computational Theory and Mathematics.

The scientist's main research topics include:

  • Machine Learning and Data Classification
  • Advanced Neural Network Applications
  • Machine Learning and Algorithms
  • Stochastic Gradient Optimization Techniques
  • Advanced Graph Neural Networks
  • Artificial Intelligence in Healthcare and Education
  • Gaussian Processes and Bayesian Inference

George E. Dahl has contributed significantly to several publication venues, with the majority of their work appearing in:

  • arXiv (Cornell University)
  • Nature Communications
  • Communications Medicine

Some of their recent papers include:

  • Understanding the Impact of Value Selection Heuristics in Scheduling Problems, 2025, arXiv (Cornell University)
  • Machine learning guided aptamer refinement and discovery, 2021, Nature Communications
  • A mobile-optimized artificial intelligence system for gestational age and fetal malpresentation assessment, 2022, Communications Medicine
  • Pre-trained Gaussian Processes for Bayesian Optimization, 2021, arXiv (Cornell University)
  • A Large Batch Optimizer Reality Check: Traditional, Generic Optimizers Suffice Across Batch Sizes, 2021, arXiv (Cornell University)

Collaborations form a notable part of their research, with frequent coauthors including:

  • Justin Gilmer
  • Zachary Nado
  • Sourabh Medapati
  • Jasper Snoek
  • Rohan Anil

Best Publications

  • Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups

    G. Hinton;Li Deng;Dong Yu;G. E. Dahl

  • On the importance of initialization and momentum in deep learning

    Ilya Sutskever;James Martens;George Dahl;Geoffrey Hinton

  • Neural Message Passing for Quantum Chemistry

    Justin Gilmer;Samuel S. Schoenholz;Patrick F. Riley;Oriol Vinyals

  • Context-Dependent Pre-Trained Deep Neural Networks for Large-Vocabulary Speech Recognition

    G. E. Dahl;Dong Yu;Li Deng;A. Acero

  • Deep Neural Networks for Acoustic Modeling in Speech Recognition

    Geoffrey Hinton;Li Deng;Dong Yu;George Dahl

  • Relational inductive biases, deep learning, and graph networks

    Peter W. Battaglia;Jessica B. Hamrick;Victor Bapst;Alvaro Sanchez-Gonzalez

  • Acoustic Modeling Using Deep Belief Networks

    A. Mohamed;G. E. Dahl;G. Hinton

  • Improving deep neural networks for LVCSR using rectified linear units and dropout

    George E. Dahl;Tara N. Sainath;Geoffrey E. Hinton

  • Deep Convolutional Neural Networks for Large-scale Speech Tasks

    Tara N. Sainath;Brian Kingsbury;George Saon;Hagen Soltau

  • Deep neural nets as a method for quantitative structure-activity relationships.

    Junshui Ma;Robert P. Sheridan;Andy Liaw;George E. Dahl

  • Prediction Errors of Molecular Machine Learning Models Lower than Hybrid DFT Error

    Felix A. Faber;Luke Hutchison;Bing Huang;Justin Gilmer

  • Large-scale malware classification using random projections and neural networks

    George E. Dahl;Jack W. Stokes;Li Deng;Dong Yu

  • Detecting Cancer Metastases on Gigapixel Pathology Images

    Yun Liu;Krishna Kumar Gadepalli;Mohammad Norouzi;George Dahl

  • Phone Recognition with the Mean-Covariance Restricted Boltzmann Machine

    George Dahl;Marc'aurelio Ranzato;Abdel-rahman Mohamed;Geoffrey E. Hinton

  • Deep Belief Networks using discriminative features for phone recognition

    Abdel-rahman Mohamed;Tara N. Sainath;George Dahl;Bhuvana Ramabhadran

  • Artificial Intelligence-Based Breast Cancer Nodal Metastasis Detection: Insights Into the Black Box for Pathologists.

    Yun Liu;Timo Kohlberger;Mohammad Norouzi;George E. Dahl

  • Large scale distributed neural network training through online distillation

    Rohan Anil;Gabriel Pereyra;Alexandre Tachard Passos;Robert Ormandi

  • Improvements to Deep Convolutional Neural Networks for LVCSR

    Tara N. Sainath;Brian Kingsbury;Abdel-rahman Mohamed;George E. Dahl

  • Multi-task Neural Networks for QSAR Predictions

    George E. Dahl;Navdeep Jaitly;Ruslan Salakhutdinov

  • Measuring the Effects of Data Parallelism on Neural Network Training

    Christopher J. Shallue;Jaehoon Lee;Joseph M. Antognini;Jascha Sohl-Dickstein

  • Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence (UAI 2010)

    Ryan Prescott Adams;George E. Dahl;Iain Murray

  • The shared views of four research groups )

    Geoffrey Hinton;Li Deng;Dong Yu;George E. Dahl

Frequent Co-Authors

Geoffrey E. Hinton
Geoffrey E. Hinton University of Toronto
Abdel-rahman Mohamed
Abdel-rahman Mohamed Facebook (United States)
Li Deng
Li Deng Citadel
Dong Yu
Dong Yu Tencent (China)
Tara N. Sainath
Tara N. Sainath Google (United States)
Ryan P. Adams
Ryan P. Adams Princeton University
Mohammad Norouzi
Mohammad Norouzi Google (United States)
Brian Kingsbury
Brian Kingsbury IBM (United States)
Oriol Vinyals
Oriol Vinyals DeepMind (United Kingdom)

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