World's Best Scientists 2026 revealed!
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Computer Science
UK
2025

D-Index & Metrics

Computer Science

D-Index
81
Citations
53499
World Ranking
988
National Ranking
50

Yee Whye Teh 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 Yee Whye Teh 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: 271 publications — 67th percentile

67% of scientists in this discipline score the same or lower.

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

Yee Whye Teh 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 Yee Whye Teh 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: 81 D-Index — 93rd percentile

93% 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

  • 2025 - Research.com Computer Science in United Kingdom Leader Award
  • 2023 - Research.com Computer Science in United Kingdom Leader Award
  • 2022 - Research.com Computer Science in United Kingdom Leader Award

Overview

Yee Whye Teh is affiliated with the University of Oxford in the United Kingdom and specializes in computer science, with a focus on artificial intelligence and related subfields. Their work spans multiple areas, including deep learning, statistical modeling, and Bayesian inference.

Their research contributions cover a range of topics, including:

  • Gaussian Processes and Bayesian Inference
  • Domain Adaptation and Few-Shot Learning
  • Machine Learning and Data Classification
  • Neural Networks and Applications
  • COVID-19 Epidemiological Studies
  • Generative Adversarial Networks and Image Synthesis
  • Topic Modeling

Yee Whye Teh has published extensively in venues such as:

  • arXiv (Cornell University)
  • Journal of the Royal Statistical Society Series A (Statistics in Society)
  • Science
  • Nature Communications
  • Nature Methods

Some of their recent papers include:

  • "Inferring the effectiveness of government interventions against COVID-19" (2020), published in Science
  • "AI for social good: unlocking the opportunity for positive impact" (2020), published in Nature Communications
  • "Uncertainty Estimation Using a Single Deep Deterministic Neural Network" (2020), published on arXiv (Cornell University)
  • "DeepC: predicting 3D genome folding using megabase-scale transfer learning" (2020), published in Nature Methods
  • "Distral: Robust Multitask Reinforcement Learning" (2025), published in Oxford University Research Archive (ORA) (University of Oxford)

The scientist collaborates frequently with several coauthors, including:

  • Razvan Pascanu
  • Arnaud Doucet
  • Yarin Gal
  • Tim G. J. Rudner
  • Émile Mathieu

Their main contributions lie in advancing the domains of artificial intelligence and machine learning through both theoretical and applied work, frequently intersecting with epidemiological studies during the COVID-19 pandemic. Their publication record reflects a focus on statistical and computational methods for understanding complex systems.

Best Publications

  • A fast learning algorithm for deep belief nets

    Geoffrey E. Hinton;Simon Osindero;Yee-Whye Teh

  • Sharing Clusters among Related Groups: Hierarchical Dirichlet Processes

    Yee W. Teh;Michael I. Jordan;Matthew J. Beal;David M. Blei

  • Bayesian Learning via Stochastic Gradient Langevin Dynamics

    Max Welling;Yee W. Teh

  • The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables

    Chris J. Maddison;Andriy Mnih;Yee Whye Teh

  • A Collapsed Variational Bayesian Inference Algorithm for Latent Dirichlet Allocation

    Yee W. Teh;David Newman;Max Welling

  • On smoothing and inference for topic models

    Arthur Asuncion;Max Welling;Padhraic Smyth;Yee Whye Teh

  • A Hierarchical Bayesian Language Model Based On Pitman-Yor Processes

    Yee Whye Teh

  • Set Transformer: A Framework for Attention-based Permutation-Invariant Neural Networks

    Juho Lee;Yoonho Lee;Jungtaek Kim;Adam R. Kosiorek

  • A fast and simple algorithm for training neural probabilistic language models

    Andriy Mnih;Yee W. Teh

  • Names and faces in the news

    T.L. Berg;A.C. Berg;J. Edwards;M. Maire

  • Bayesian Nonparametrics: Hierarchical Bayesian nonparametric models with applications

    Yee Whye Teh;Michael I. Jordan

  • Do Deep Generative Models Know What They Don't Know?

    Eric T. Nalisnick;Akihiro Matsukawa;Yee Whye Teh;Dilan Görür

  • Distral: robust multitask reinforcement learning

    Yee Whye Teh;Victor Bapst;Wojciech Marian Czarnecki;John Quan

  • Progress & Compress: A scalable framework for continual learning

    Jonathan Schwarz;Jelena Luketina;Wojciech M. Czarnecki;Agnieszka Grabska-Barwinska

  • Progress & Compress: A scalable framework for continual learning

    Jonathan Schwarz;Wojciech Czarnecki;Jelena Luketina;Agnieszka Grabska-Barwinska

  • Beam sampling for the infinite hidden Markov model

    Jurgen Van Gael;Yunus Saatci;Yee Whye Teh;Zoubin Ghahramani

  • Dirichlet Process

    Unknown

  • Stick-breaking Construction for the Indian Buffet Process

    Yee Whye Teh;Dilan Görür;Zoubin Ghahramani

  • AI for social good: unlocking the opportunity for positive impact.

    Nenad Tomašev;Julien Cornebise;Frank Hutter;Frank Hutter;Shakir Mohamed

  • Semiparametric Latent Factor Models

    Yee Whye Teh;Matthias W. Seeger;Michael I. Jordan

  • Augmented Neural ODEs

    Emilien Dupont;Arnaud Doucet;Yee Whye Teh

  • Neural Processes

    Marta Garnelo;Jonathan Schwarz;Dan Rosenbaum;Fabio Viola

Frequent Co-Authors

Balaji Lakshminarayanan
Balaji Lakshminarayanan Google (United States)
Max Welling
Max Welling University of Amsterdam
Nicolas Heess
Nicolas Heess DeepMind (United Kingdom)
Razvan Pascanu
Razvan Pascanu DeepMind (United Kingdom)
Frank Wood
Frank Wood University of British Columbia
Arnaud Doucet
Arnaud Doucet University of Oxford
Geoffrey E. Hinton
Geoffrey E. Hinton University of Toronto
Charles Blundell
Charles Blundell DeepMind (United Kingdom)
Zoubin Ghahramani
Zoubin Ghahramani University of Cambridge

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