World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
39
Citations
29043
World Ranking
9456
National Ranking
4000

Aaron van den Oord 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 Aaron van den Oord 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: 63 publications — 1st percentile

1% of scientists in this discipline score the same or lower.

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

Aaron van den Oord 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 Aaron van den Oord 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: 39 D-Index — 33rd percentile

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

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

Overview

Aaron van den Oord is affiliated with Google in the United States. Their research primarily spans the fields of computer science, with a particular focus on artificial intelligence, signal processing, and computer vision and pattern recognition. The scientist's work includes publications in various subfields related to audio and speech processing and machine learning applications.

The main topics addressed in their research include:

  • Music and Audio Processing
  • Speech Recognition and Synthesis
  • Domain Adaptation and Few-Shot Learning
  • Speech and Audio Processing
  • Multimodal Machine Learning Applications
  • AI in cancer detection
  • Advanced Neural Network Applications

Notable recent papers authored or co-authored by Aaron van den Oord include:

  • "Are we done with ImageNet?", 2020, published in arXiv (Cornell University)
  • "Divide and Contrast: Self-supervised Learning from Uncurated Data", 2021, presented at the 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
  • "Towards Learning Universal Audio Representations", 2022, at ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
  • "Efficient Visual Pretraining with Contrastive Detection", 2021, presented at the 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
  • "Multi-Format Contrastive Learning of Audio Representations", 2021, published in arXiv (Cornell University)

Frequent co-authors in their body of work include:

  • Luyu Wang
  • Jean-Baptiste Alayrac
  • Olivier J. Hénaff
  • Pauline Luc
  • Adrià Recasens

Aaron van den Oord's publications frequently appear in venues such as:

  • arXiv (Cornell University)
  • 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
  • ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Best Publications

  • Representation Learning with Contrastive Predictive Coding

    Aaron van den Oord;Yazhe Li;Oriol Vinyals

  • WaveNet: A Generative Model for Raw Audio

    Aäron van den Oord;Sander Dieleman;Heiga Zen;Karen Simonyan

  • Neural Discrete Representation Learning

    Aaron van den Oord;Oriol Vinyals;koray kavukcuoglu

  • Conditional image generation with PixelCNN decoders

    Aäron van den Oord;Nal Kalchbrenner;Oriol Vinyals;Lasse Espeholt

  • Pixel recurrent neural networks

    Aäron Van Den Oord;Nal Kalchbrenner;Koray Kavukcuoglu

  • Deep content-based music recommendation

    Aaron van den Oord;Sander Dieleman;Benjamin Schrauwen

  • Data-Efficient Image Recognition with Contrastive Predictive Coding

    Olivier J. Hénaff;Aravind Srinivas;Jeffrey De Fauw;Ali Razavi

  • A note on the evaluation of generative models

    Lucas Theis;Aäron van den Oord;Matthias Bethge

  • Generating Diverse High-Fidelity Images with VQ-VAE-2

    Ali Razavi;Aaron van den Oord;Oriol Vinyals

  • Parallel WaveNet: Fast High-Fidelity Speech Synthesis

    Aäron van den Oord;Yazhe Li;Igor Babuschkin;Karen Simonyan

  • Neural Machine Translation in Linear Time

    Nal Kalchbrenner;Lasse Espeholt;Karen Simonyan;Aäron van den Oord

  • Efficient Neural Audio Synthesis

    Nal Kalchbrenner;Erich Elsen;Karen Simonyan;Seb Noury

  • Count-based exploration with neural density models

    Georg Ostrovski;Marc G. Bellemare;Aäron van den Oord;Rémi Munos

  • Adversarial Risk and the Dangers of Evaluating Against Weak Attacks.

    Jonathan Uesato;Brendan O'Donoghue;Aaron van den Oord;Pushmeet Kohli

  • On Variational Bounds of Mutual Information

    Ben Poole;Sherjil Ozair;Aaron van den Oord;Alexander A. Alemi

  • Unsupervised Speech Representation Learning Using WaveNet Autoencoders

    Jan Chorowski;Ron J. Weiss;Samy Bengio;Aaron van den Oord

  • Video Pixel Networks

    Nal Kalchbrenner;Aäron van den Oord;Karen Simonyan;Ivo Danihelka

  • Beyond Temporal Pooling: Recurrence and Temporal Convolutions for Gesture Recognition in Video

    Lionel Pigou;Aäron van den Oord;Sander Dieleman;Mieke Van Herreweghe

  • Parallel Multiscale Autoregressive Density Estimation.

    Scott E. Reed;Aäron van den Oord;Nal Kalchbrenner;Sergio Gomez Colmenarejo

  • Are we done with ImageNet

    Lucas Beyer;Olivier J. Hénaff;Alexander Kolesnikov;Xiaohua Zhai

  • Parallel Multiscale Autoregressive Density Estimation

    Scott Reed;Aäron van den Oord;Nal Kalchbrenner;Sergio Gómez Colmenarejo

Frequent Co-Authors

Oriol Vinyals
Oriol Vinyals DeepMind (United Kingdom)
Nal Kalchbrenner
Nal Kalchbrenner Google (United States)
Karen Simonyan
Karen Simonyan DeepMind (United Kingdom)
Koray Kavukcuoglu
Koray Kavukcuoglu DeepMind (United Kingdom)
Alex Graves
Alex Graves Google (United States)
Nando de Freitas
Nando de Freitas DeepMind (United Kingdom)
Benjamin Schrauwen
Benjamin Schrauwen Ghent University
Heiga Zen
Heiga Zen Google (United States)
Danilo Jimenez Rezende
Danilo Jimenez Rezende DeepMind (United Kingdom)
Ben Poole
Ben Poole Google (United States)

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