World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
38
Citations
5218
World Ranking
10352
National Ranking
413

Frank Wood 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 Frank Wood 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: 140 publications — 23rd percentile

23% of scientists in this discipline score the same or lower.

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

Frank Wood 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 Frank Wood 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

Frank Wood is affiliated with the University of British Columbia in Canada. Their research spans a broad array of topics within computer science, with particular emphasis on artificial intelligence.

Wood's research contributions cover the following main fields of study:

  • Computer Science

Their subfields of study include:

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Management Science and Operations Research
  • Modeling and Simulation
  • Statistical and Nonlinear Physics

Key topics within their work are:

  • Gaussian Processes and Bayesian Inference
  • Bayesian Modeling and Causal Inference
  • Machine Learning and Algorithms
  • Generative Adversarial Networks and Image Synthesis
  • Machine Learning and Data Classification
  • COVID-19 epidemiological studies
  • Advanced Bandit Algorithms Research

Recent publications by Frank Wood include the following:

  • "Some Formal Structures in Probability (Invited Talk)," 2021, arXiv (Cornell University)
  • "Enhancing Few-Shot Image Classification with Unlabelled Examples," 2022, 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)
  • "On the Challenges and Opportunities in Generative AI," 2024, arXiv (Cornell University)
  • "Differentiable Particle Filtering without Modifying the Forward Pass," 2021, arXiv (Cornell University)
  • "Planning as Inference in Epidemiological Models," 2020, arXiv (Cornell University)

Frequent co-authors collaborating with Wood include:

  • Vaden Masrani
  • Adam Ścibior
  • Saeid Naderiparizi
  • Berend Zwartsenberg
  • Andrew Warrington

Wood's work is primarily disseminated through publication venues such as:

  • arXiv (Cornell University)
  • 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)
  • Frontiers in Artificial Intelligence
  • Proceedings of the International Symposium on Combinatorial Search
  • 2022 International Joint Conference on Neural Networks (IJCNN)

Best Publications

  • A New Approach to Probabilistic Programming Inference

    Frank D. Wood;Jan-Willem van de Meent;Vikash Mansinghka

  • Learning Disentangled Representations with Semi-Supervised Deep Generative Models

    N. Siddharth;Brooks Paige;Brooks Paige;Jan-Willem van de Meent;Alban Desmaison

  • Diagnosis code assignment: models and evaluation metrics

    Adler J. Perotte;Rimma Pivovarov;Karthik Natarajan;Karthik Natarajan;Nicole Gray Weiskopf

  • On the variability of manual spike sorting

    F. Wood;M.J. Black;C. Vargas-Irwin;M. Fellows

  • Improved Few-Shot Visual Classification

    Peyman Bateni;Raghav Goyal;Vaden Masrani;Frank Wood

  • Deep Variational Reinforcement Learning for POMDPs

    Maximilian Igl;Luisa M. Zintgraf;Tuan Anh Le;Frank Wood

  • A nonparametric Bayesian alternative to spike sorting.

    Frank Wood;Michael J. Black

  • Hierarchically Supervised Latent Dirichlet Allocation

    Adler J. Perotte;Frank Wood;Noemie Elhadad;Nicholas Bartlett

  • Semantics for probabilistic programming: higher-order functions, continuous distributions, and soft constraints

    Sam Staton;Frank Wood;Hongseok Yang;Chris Heunen

  • An Introduction to Probabilistic Programming

    Jan-Willem van de Meent;Brooks Paige;Hongseok Yang;Frank Wood

  • Tighter Variational Bounds are Not Necessarily Better

    Tom Rainforth;Adam R. Kosiorek;Tuan Anh Le;Chris J. Maddison

  • A stochastic memoizer for sequence data

    Frank Wood;Cédric Archambeau;Jan Gasthaus;Lancelot James

  • Auto-Encoding Sequential Monte Carlo

    Tuan Anh Le;Maximilian Igl;Tom Rainforth;Tom Jin

  • Online Learning Rate Adaptation with Hypergradient Descent

    Atilim Gunes Baydin;Robert Cornish;David Martinez Rubio;Mark Schmidt

  • Using synthetic data to train neural networks is model-based reasoning

    Tuan Anh Le;Atilim Giines Baydin;Robert Zinkov;Frank Wood

  • A non-parametric Bayesian method for inferring hidden causes

    Frank Wood;Thomas L. Griffiths;Zoubin Ghahramani

  • Inference Compilation and Universal Probabilistic Programming

    Tuan Anh Le;Atilim Gunes Baydin;Frank D. Wood

  • Design and Implementation of Probabilistic Programming Language Anglican

    David Tolpin;Jan-Willem van de Meent;Hongseok Yang;Frank Wood

  • Inference networks for sequential Monte Carlo in graphical models

    Brooks Paige;Frank Wood

  • Semantics for probabilistic programming: higher-order functions, continuous distributions, and soft constraints

    Sam Staton;Hongseok Yang;Chris Heunen;Ohad Kammar

  • The sequence memoizer

    Frank Wood;Jan Gasthaus;Cédric Archambeau;Lancelot James

  • On Nesting Monte Carlo Estimators

    Tom Rainforth;Robert Cornish;Hongseok Yang;Andrew Warrington

Frequent Co-Authors

Yee Whye Teh
Yee Whye Teh University of Oxford
Hongseok Yang
Hongseok Yang Korea Advanced Institute of Science and Technology
Philip H. S. Torr
Philip H. S. Torr University of Oxford
Michael J. Black
Michael J. Black Max Planck Institute for Intelligent Systems
Aram Galstyan
Aram Galstyan University of Southern California
Arnaud Doucet
Arnaud Doucet University of Oxford
Pushmeet Kohli
Pushmeet Kohli DeepMind (United Kingdom)
Noah D. Goodman
Noah D. Goodman Stanford University
Mark Schmidt
Mark Schmidt University of British Columbia
John P. Donoghue
John P. Donoghue Brown University

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