World's Best Scientists 2026 revealed!
David Duvenaud

David Duvenaud

D-Index & Metrics

Computer Science

D-Index
52
Citations
21164
World Ranking
4952
National Ranking
194

David Duvenaud 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 David Duvenaud 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: 94 publications — 7th percentile

7% of scientists in this discipline score the same or lower.

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

David Duvenaud 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 David Duvenaud 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: 52 D-Index — 65th percentile

65% of scientists in this discipline score the same or lower.

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

Overview

David Duvenaud is affiliated with the University of Toronto in Canada and has a research focus primarily in the field of Computer Science, with numerous contributions to subfields such as Artificial Intelligence, Computer Vision and Pattern Recognition, Statistical and Nonlinear Physics, Hardware and Architecture, and Management Science and Operations Research.

Their research spans several main topics including Adversarial Robustness in Machine Learning, Gaussian Processes and Bayesian Inference, Generative Adversarial Networks and Image Synthesis, Model Reduction and Neural Networks, Stochastic Gradient Optimization Techniques, Topic Modeling, and Explainable Artificial Intelligence (XAI).

David Duvenaud has published extensively in various venues, with the majority of their work appearing in arXiv (Cornell University). Other publication venues include the Proceedings of the ACM on Programming Languages.

Recent papers authored or co-authored by David Duvenaud include:

  • "Scalable Gradients for Stochastic Differential Equations" (2020, arXiv (Cornell University))
  • "Towards Understanding Sycophancy in Language Models" (2023, arXiv (Cornell University))
  • "Learning Differential Equations that are Easy to Solve" (2020, arXiv (Cornell University))
  • "Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Training" (2024, arXiv (Cornell University))
  • "Learning the Stein Discrepancy for Training and Evaluating Energy-Based Models without Sampling" (2020, arXiv (Cornell University))

Frequent co-authors in David Duvenaud's collaborations include:

  • Ricky T. Q. Chen
  • Samuel R. Bowman
  • Ethan Perez
  • Dami Choi
  • Shauna Kravec

Best Publications

  • Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules

    Rafael Gómez-Bombarelli;Jennifer Nansean Wei;David Duvenaud;José Miguel Hernández-Lobato

  • Convolutional networks on graphs for learning molecular fingerprints

    David Duvenaud;Dougal Maclaurin;Jorge Aguilera-Iparraguirre;Rafael Gómez-Bombarelli

  • Neural ordinary differential equations

    Ricky T. Q. Chen;Yulia Rubanova;Jesse Bettencourt;David Duvenaud

  • Isolating Sources of Disentanglement in Variational Autoencoders.

    Tian Qi Chen;Xuechen Li;Roger B. Grosse;David Duvenaud

  • Automatic model construction with Gaussian processes

    David Duvenaud

  • Gradient-based Hyperparameter Optimization through Reversible Learning

    Dougal Maclaurin;David Duvenaud;Ryan Adams

  • Structure Discovery in Nonparametric Regression through Compositional Kernel Search

    David Duvenaud;James Lloyd;Roger Grosse;Joshua Tenenbaum

  • FFJORD: Free-form Continuous Dynamics for Scalable Reversible Generative Models

    Will Grathwohl;Ricky T. Q. Chen;Jesse Bettencourt;Ilya Sutskever

  • Isolating Sources of Disentanglement in Variational Autoencoders

    Ricky T. Q. Chen;Xuechen Li;Roger Grosse;David Duvenaud

  • Neural networks for the prediction organic chemistry reactions

    Jennifer N. Wei;David Duvenaud;Alán Aspuru-Guzik

  • Neural Networks for the Prediction of Organic Chemistry Reactions

    Jennifer N. Wei;David Duvenaud;Alán Aspuru-Guzik

  • Composing graphical models with neural networks for structured representations and fast inference

    Matthew J. Johnson;David Duvenaud;Alexander B. Wiltschko;Ryan P. Adams

  • Structure Discovery in Nonparametric Regression through Compositional Kernel Search

    David Duvenaud;James Robert Lloyd;Roger Grosse;Joshua B. Tenenbaum

  • Invertible Residual Networks

    Jens Behrmann;Will Grathwohl;Ricky T. Q. Chen;David Duvenaud

  • Additive Gaussian Processes

    David K Duvenaud;Hannes Nickisch;Carl E. Rasmussen

  • Composing graphical models with neural networks for structured representations and fast inference

    Matthew J. Johnson;David Duvenaud;Alexander B. Wiltschko;Sandeep R. Datta

  • Latent Ordinary Differential Equations for Irregularly-Sampled Time Series

    Yulia Rubanova;Ricky T. Q. Chen;David K. Duvenaud

  • Automatic construction and natural-language description of nonparametric regression models

    James Robert Lloyd;David Duvenaud;Roger Grosse;Joshua B. Tenenbaum

  • Latent ODEs for Irregularly-Sampled Time Series

    Yulia Rubanova;Ricky T. Q. Chen;David Duvenaud

  • Your classifier is secretly an energy based model and you should treat it like one

    Will Grathwohl;Kuan-Chieh Wang;Joern-Henrik Jacobsen;David Duvenaud

  • Backpropagation through the Void: Optimizing control variates for black-box gradient estimation

    Will Grathwohl;Dami Choi;Yuhuai Wu;Geoffrey Roeder

  • Scalable Gradients for Stochastic Differential Equations

    Xuechen Li;Ting-Kam Leonard Wong;Ricky T. Q. Chen;David Duvenaud

  • Efficient Graph Generation with Graph Recurrent Attention Networks

    Renjie Liao;Yujia Li;Yang Song;Shenlong Wang

Frequent Co-Authors

Ryan P. Adams
Ryan P. Adams Princeton University
Roger Grosse
Roger Grosse University of Toronto
Alán Aspuru-Guzik
Alán Aspuru-Guzik University of Toronto
Zoubin Ghahramani
Zoubin Ghahramani University of Cambridge
Richard S. Zemel
Richard S. Zemel University of Toronto
Mohammad Norouzi
Mohammad Norouzi Google (United States)
Kevin Swersky
Kevin Swersky Google (United States)
Graeme Hirst
Graeme Hirst University of Toronto
Samuel J. Gershman
Samuel J. Gershman Harvard University

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

Exploring Computer Science in the USA opens many online education options, allowing students flexibility and access to diverse fields. For those just starting their academic journey, an associate’s degree can be a quick entry point into technology careers. If you’re wondering what is the easiest associate's degree to get, online programs offer accessible and supportive formats to help you begin.

Those aiming for leadership or academic roles may consider an advanced degree such as an ed d in education. This qualification can open doors to teaching, research, or administrative positions in tech education.

Choosing the right institution is crucial. Many students prefer famous online colleges to ensure quality instruction, robust support services, and accreditation that employers value.

For those interested in creative technology areas, a video game design degree can lead to exciting roles in the gaming industry, blending computer science with art and storytelling.

Whether you’re beginning as an associate student, pursuing advanced education, or exploring creative pathways, accredited online programs in the US offer flexible, reputable routes to fulfilling tech careers.

Best Scientists Citing David Duvenaud

Trending Scientists