World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
50
Citations
7091
World Ranking
5718
National Ranking
343

Richard E. Turner 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 Richard E. Turner 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: 167 publications — 33rd percentile

33% of scientists in this discipline score the same or lower.

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

Richard E. Turner 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 Richard E. Turner 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: 50 D-Index — 62nd percentile

62% of scientists in this discipline score the same or lower.

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

Overview

Richard E. Turner is affiliated with the University of Cambridge in the United Kingdom. Their primary field of study is Computer Science, with a notable focus on Artificial Intelligence. Other subfields include Atmospheric Science, Computer Vision and Pattern Recognition, Global and Planetary Change, and Statistical and Nonlinear Physics.

Their research covers multiple topics, prominently featuring Gaussian Processes and Bayesian Inference, Domain Adaptation and Few-Shot Learning, Machine Learning and Data Classification, Meteorological Phenomena and Simulations, Climate Variability and Models, Model Reduction and Neural Networks, and Adversarial Robustness in Machine Learning.

Richard E. Turner's publication record includes recent papers such as:

  • The Panoramic ECAP Method: Estimating Patient-Specific Patterns of Current Spread and Neural Health in Cochlear Implant Users (2021, Journal of the Association for Research in Otolaryngology)
  • Fast and Flexible Multi-Task Classification Using Conditional Neural Adaptive Processes (2020, Apollo (University of Cambridge))
  • Convolutional conditional neural processes for local climate downscaling (2022, Geoscientific Model Development)
  • A foundation model for the Earth system (2025, Nature)
  • Continual Deep Learning by Functional Regularisation of Memorable Past (2020, arXiv (Cornell University))

Frequent co-authors in their work include:

  • James Requeima
  • Wessel P. Bruinsma
  • J. Scott Hosking
  • Stratis Markou
  • Anna Vaughan

Richard E. Turner has published extensively in several venues, with arXiv (Cornell University) being the most frequent. Other prominent venues include Apollo (University of Cambridge), Nature, Environmental Data Science, and Hydrology and Earth System Sciences.

Best Publications

  • Variational continual learning

    Cuong V. Nguyen;Yingzhen Li;Thang D. Bui;Richard E. Turner

  • The processing and perception of size information in speech sounds

    David R. R. Smith;Roy D. Patterson;Richard Turner;Hideki Kawahara

  • Gaussian Process Behaviour in Wide Deep Neural Networks.

    Alexander G. de G. Matthews;Mark Rowland;Jiri Hron;Richard E. Turner

  • Two problems with variational expectation maximisation for time-series models

    Richard Eric Turner;Maneesh Sahani

  • Deep Gaussian processes for regression using approximate expectation propagation

    Thang D. Bui;José Miguel Hernández-Lobato;Daniel Hernández-Lobato;Yingzhen Li

  • Rényi divergence variational inference

    Yingzhen Li;Richard E. Turner

  • Invariant models for causal transfer learning

    Mateo Rojas-Carulla;Bernhard Schölkopf;Richard Turner;Jonas Peters

  • Q-PrOP: Sample-efficient policy gradient with an off-policy critic

    Shixiang Gu;Timothy Lillicrap;Zoubin Ghahramani;Richard Eric Turner

  • Black-Box Alpha Divergence Minimization

    José Miguel Hernández-Lobato;Yingzhen Li;Mark Rowland;Thang D. Bui

  • Black-box α-divergence minimization

    José Miguel Hernández-Lobato;Yingzhen Li;Mark Rowland;Daniel Hernández-Lobato

  • Deep Gaussian Processes for Regression using Approximate Expectation Propagation

    Thang D. Bui;Daniel Hernández-Lobato;Yingzhen Li;José Miguel Hernández-Lobato

  • Meta-Learning Probabilistic Inference for Prediction

    Jonathan Gordon;John Bronskill;Matthias Bauer;Sebastian Nowozin

  • Practical Deep Learning with Bayesian Principles

    Kazuki Osawa;Siddharth Swaroop;Mohammad Emtiyaz E. Khan;Anirudh Jain

  • R'enyi Divergence Variational Inference

    Yingzhen Li;Richard E. Turner

  • Interpolated Policy Gradient: Merging On-Policy and Off-Policy Gradient Estimation for Deep Reinforcement Learning

    Shixiang Gu;Timothy P. Lillicrap;Zoubin Ghahramani;Richard E. Turner

  • A Maximum-Likelihood Interpretation for Slow Feature Analysis

    Richard Turner;Maneesh Sahani

  • Sequence tutor: conservative fine-tuning of sequence generation models with KL-control

    Natasha Jaques;Shixiang Gu;Dzmitry Bahdanau;José Miguel Hernández-Lobato

  • A Unifying Framework for Gaussian Process Pseudo-Point Approximations using Power Expectation Propagation

    Thang D. Bui;Josiah Yan;Richard E. Turner

  • Deterministic Variational Inference for Robust Bayesian Neural Networks

    Anqi Wu;Sebastian Nowozin;Edward Meeds;Richard E. Turner

  • On sparse variational methods and the Kullback-Leibler divergence between stochastic processes

    Alexander G. de G. Matthews;James Hensman;Richard E. Turner;Zoubin Ghahramani

  • Neural adaptive sequential Monte Carlo

    Shixiang Gu;Zoubin Ghahramani;Richard E. Turner

  • Structured Evolution with Compact Architectures for Scalable Policy Optimization

    Krzysztof Choromanski;Mark Rowland;Vikas Sindhwani;Richard E. Turner

  • The Mirage of Action-Dependent Baselines in Reinforcement Learning.

    George Tucker;Surya Bhupatiraju;Shixiang Gu;Richard E. Turner

  • Nonlinear ICA Using Auxiliary Variables and Generalized Contrastive Learning

    Aapo Hyvärinen;Hiroaki Sasaki;Richard E. Turner

Frequent Co-Authors

José Miguel Hernández-Lobato
José Miguel Hernández-Lobato University of Cambridge
Sebastian Nowozin
Sebastian Nowozin Microsoft (United States)
Maneesh Sahani
Maneesh Sahani University College London
Zoubin Ghahramani
Zoubin Ghahramani University of Cambridge
Shixiang Gu
Shixiang Gu Google (United States)
Brian C. J. Moore
Brian C. J. Moore University of Cambridge
Sergey Levine
Sergey Levine University of California, Berkeley
Bernhard Schölkopf
Bernhard Schölkopf Max Planck Institute for Intelligent Systems
Roy D. Patterson
Roy D. Patterson University of Cambridge
Adrian Weller
Adrian Weller University of Cambridge

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