World's Best Scientists 2026 revealed!
Thomas G. Dietterich

Thomas G. Dietterich

D-Index & Metrics

Computer Science

D-Index
86
Citations
58680
World Ranking
746
National Ranking
394

Thomas G. Dietterich 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 Thomas G. Dietterich 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: 315 publications — 76th percentile

76% of scientists in this discipline score the same or lower.

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

Thomas G. Dietterich 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 Thomas G. Dietterich 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: 86 D-Index — 95th percentile

95% of scientists in this discipline score the same or lower.

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

Research.com Recognitions

  • 2007 - Fellow of the American Association for the Advancement of Science (AAAS)
  • 2002 - ACM Fellow For contributions to machine learning.
  • 1994 - Fellow of the Association for the Advancement of Artificial Intelligence (AAAI) For contributions to the science and practice of machine learning, methodology of machine learning research, and for service to the AI community.

Overview

Thomas G. Dietterich is affiliated with Oregon State University in the United States and has contributed extensively to the field of computer science, particularly in artificial intelligence. Their work spans various subfields including anomaly detection techniques, Bayesian modeling, causal inference, domain adaptation, and imbalanced data classification. Their research interests also extend into cancer research, general health professions, global and planetary change, and computer networks and communications.

The scientist's recent notable papers include:

  • Confidence Calibration for Domain Generalization under Covariate Shift, 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
  • Discovering Anomalies by Incorporating Feedback from an Expert, 2020, ACM Transactions on Knowledge Discovery from Data
  • International AI Safety Report, 2025, arXiv (Cornell University)
  • International Scientific Report on the Safety of Advanced AI (Interim Report), 2024, arXiv (Cornell University)
  • K-N-MOMDPs: Towards Interpretable Solutions for Adaptive Management, 2021, Proceedings of the AAAI Conference on Artificial Intelligence

Frequent co-authors collaborating with Thomas G. Dietterich include:

  • Daniel Privitera
  • Nicholas R. Jennings
  • Vidushi Marda
  • Helen Margetts
  • Arvind Narayanan

The scientist has published most frequently in venues such as arXiv (Cornell University), SuperIntelligence - Robotics - Safety & Alignment, the 2021 IEEE/CVF International Conference on Computer Vision (ICCV), ACM Transactions on Knowledge Discovery from Data, and the Proceedings of the AAAI Conference on Artificial Intelligence.

Thomas G. Dietterich's book publication includes Learning and Reasoning, released in 2025 by Springer Science+Business Media.

Areas of study covered by their body of work can be grouped as follows:

  • Artificial Intelligence
  • Cancer Research
  • General Health Professions
  • Global and Planetary Change
  • Computer Networks and Communications

Research topics prominently addressed include:

  • Anomaly Detection Techniques and Applications
  • Bayesian Modeling and Causal Inference
  • Domain Adaptation and Few-Shot Learning
  • Cancer-related molecular mechanisms research
  • Imbalanced Data Classification Techniques
  • Machine Learning and Data Classification
  • Machine Learning and ELM

Thomas G. Dietterich's professional recognitions include:

  • Fellow of the American Association for the Advancement of Science (AAAS), 2007
  • ACM Fellow, 2002, for contributions to machine learning
  • Fellow of the Association for the Advancement of Artificial Intelligence (AAAI), 1994, for contributions to machine learning science, methodology, and community service

Best Publications

  • Ensemble Methods in Machine Learning

    Thomas G. Dietterich

  • Approximate statistical tests for comparing supervised classification learning algorithms

    Thomas G. Dietterich

  • Solving multiclass learning problems via error-correcting output codes

    Thomas G. Dietterich;Ghulum Bakiri

  • An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, Boosting, and Randomization

    Thomas G. Dietterich

  • Solving the multiple instance problem with axis-parallel rectangles

    Thomas G. Dietterich;Richard H. Lathrop;Tomás Lozano-Pérez

  • Introduction to Semi-Supervised Learning

    Xiaojin Zhu;Andrew B. Goldberg;Ronald Brachman;Thomas Dietterich

  • Machine-Learning Research

    Thomas G. Dietterich

  • Hierarchical reinforcement learning with the MAXQ value function decomposition

    Thomas G. Dietterich

  • A Unifying Review of Deep and Shallow Anomaly Detection

    Lukas Ruff;Jacob R. Kauffmann;Robert A. Vandermeulen;Gregoire Montavon

  • Adaptive computation and machine learning

    Thomas Glen Dietterich

  • The eBird enterprise: An integrated approach to development and application of citizen science

    Brian L. Sullivan;Jocelyn L. Aycrigg;Jessie H. Barry;Rick E. Bonney

  • Benchmarking Neural Network Robustness to Common Corruptions and Perturbations

    Dan Hendrycks;Thomas G. Dietterich

  • Learning with many irrelevant features

    Hussein Almuallim;Thomas G. Dietterich

  • Overfitting and undercomputing in machine learning

    Tom Dietterich

  • Machine Learning for Sequential Data: A Review

    Thomas G. Dietterich

  • Deep Anomaly Detection with Outlier Exposure

    Dan Hendrycks;Mantas Mazeika;Thomas G. Dietterich

  • Pruning Adaptive Boosting

    Dragos D. Margineantu;Thomas G. Dietterich

  • Learning Boolean concepts in the presence of many irrelevant features

    Hussein Almuallim;Thomas G. Dietterich

  • A reinforcement learning approach to job-shop scheduling

    Wei Zhang;Thomas G. Dietterich

  • Error-correcting output coding corrects bias and variance

    Eun Bae Kong;Thomas G. Dietterich

  • Multiple Classifier Systems

    Gerhard Goos;Juris Hartmanis;Jan van Leeuwen

Frequent Co-Authors

Alan Fern
Alan Fern Oregon State University
Prasad Tadepalli
Prasad Tadepalli Oregon State University
Weng-Keen Wong
Weng-Keen Wong Oregon State University
Xiaoli Z. Fern
Xiaoli Z. Fern Oregon State University
Linda G. Shapiro
Linda G. Shapiro University of Washington
Lise Getoor
Lise Getoor University of California, Santa Cruz
Stephen Muggleton
Stephen Muggleton Imperial College London
David A. Lytle
David A. Lytle Oregon State University
Sinisa Todorovic
Sinisa Todorovic Oregon State University
Luc De Raedt
Luc De Raedt KU Leuven

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