World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
63
Citations
36367
World Ranking
2673
National Ranking
1326

Ryan P. Adams 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 Ryan P. Adams 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: 144 publications — 24th percentile

24% of scientists in this discipline score the same or lower.

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

Ryan P. Adams 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 Ryan P. Adams 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: 63 D-Index — 81st percentile

81% 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

  • 2015 - Fellow of Alfred P. Sloan Foundation

Overview

Ryan P. Adams is affiliated with Princeton University in the United States. Their research spans multiple disciplines, with a primary focus on Engineering and Computer Science. Within these areas, key subfields of study include Artificial Intelligence, Mechanical Engineering, Materials Chemistry, Biomedical Engineering, and Computational Mechanics.

The scientist's work covers a variety of topics related to machine learning and materials science. These main topics include:

  • Machine Learning in Materials Science
  • Adversarial Robustness in Machine Learning
  • Machine Learning and Algorithms
  • Gaussian Processes and Bayesian Inference
  • Microbial Community Ecology and Physiology
  • Advanced Materials and Mechanics
  • Manufacturing Process and Optimization

Ryan P. Adams has authored publications in several venues. Frequent publication venues include:

  • arXiv (Cornell University)
  • Nature
  • Journal of the American Chemical Society
  • Cell Host & Microbe
  • Nature Materials

Recent papers authored by the scientist demonstrate interdisciplinary collaboration and wide-ranging interests. Selected papers include:

  • Bayesian reaction optimization as a tool for chemical synthesis (2021) published in Nature
  • A Multi-Objective Active Learning Platform and Web App for Reaction Optimization (2022) published in Journal of the American Chemical Society
  • Diverse events have transferred genes for edible seaweed digestion from marine to human gut bacteria (2022) published in Cell Host & Microbe
  • A data-driven computational scheme for the nonlinear mechanical properties of cellular mechanical metamaterials under large deformation (2020) published in Soft Matter
  • Automated discovery of reprogrammable nonlinear dynamic metamaterials (2024) published in Nature Materials

The scientist frequently collaborates with several coauthors, with multiple joint works produced alongside:

  • Deniz Oktay
  • Alex Beatson
  • Eder Medina
  • Diana Cai
  • Mehran Mirramezani

Ryan P. Adams was awarded the status of Fellow of the Alfred P. Sloan Foundation in 2015, recognizing contributions to their fields of study.

Best Publications

  • Practical Bayesian Optimization of Machine Learning Algorithms

    Jasper Snoek;Hugo Larochelle;Ryan P Adams

  • Taking the Human Out of the Loop: A Review of Bayesian Optimization

    Bobak Shahriari;Kevin Swersky;Ziyu Wang;Ryan P. Adams

  • 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

  • Bayesian reaction optimization as a tool for chemical synthesis.

    Benjamin J. Shields;Jason Stevens;Jun Li;Marvin Parasram

  • Mapping Sub-Second Structure in Mouse Behavior.

    Alexander B. Wiltschko;Matthew J. Johnson;Giuliano Iurilli;Ralph E. Peterson

  • Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks

    Jose Miguel Hernandez-Lobato;Ryan Adams

  • Scalable Bayesian Optimization Using Deep Neural Networks

    Jasper Snoek;Oren Rippel;Oren Rippel;Kevin Swersky;Ryan Kiros

  • Bayesian Online Changepoint Detection

    Ryan Prescott Adams;David J. C. MacKay

  • Multi-Task Bayesian Optimization

    Kevin Swersky;Jasper Snoek;Ryan P Adams

  • Gradient-based Hyperparameter Optimization through Reversible Learning

    Dougal Maclaurin;David Duvenaud;Ryan Adams

  • Gaussian Process Kernels for Pattern Discovery and Extrapolation

    Andrew Wilson;Ryan Adams

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

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

  • Bayesian optimization with unknown constraints

    Michael A. Gelbart;Jasper Snoek;Ryan P. Adams

  • Elliptical slice sampling

    Iain Murray;Ryan Prescott Adams;David J.C. MacKay

  • Spectral representations for convolutional neural networks

    Oren Rippel;Jasper Snoek;Ryan P. Adams

  • Discovering Latent Network Structure in Point Process Data

    Scott Linderman;Ryan Prescott Adams

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

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

  • Tractable nonparametric Bayesian inference in Poisson processes with Gaussian process intensities

    Ryan Prescott Adams;Iain Murray;David J. C. MacKay

  • Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence (UAI 2010)

    Ryan Prescott Adams;George E. Dahl;Iain Murray

  • Freeze-Thaw Bayesian Optimization

    Kevin Swersky;Jasper Snoek;Ryan Prescott Adams

Frequent Co-Authors

Jasper Snoek
Jasper Snoek Google (United States)
David Duvenaud
David Duvenaud University of Toronto
Iain Murray
Iain Murray University of Edinburgh
Andrew Miller
Andrew Miller University of Illinois at Urbana-Champaign
Kevin Swersky
Kevin Swersky Google (United States)
Zoubin Ghahramani
Zoubin Ghahramani University of Cambridge
Richard S. Zemel
Richard S. Zemel University of Toronto
José Miguel Hernández-Lobato
José Miguel Hernández-Lobato University of Cambridge
David J. C. MacKay
David J. C. MacKay University of Cambridge
Hugo Larochelle
Hugo Larochelle Google (United States)

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