World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
45
Citations
9334
World Ranking
7143
National Ranking
3132

Douglas Eck 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 Douglas Eck 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: 112 publications — 12th percentile

12% of scientists in this discipline score the same or lower.

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

Douglas Eck 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 Douglas Eck 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: 45 D-Index — 51st percentile

51% of scientists in this discipline score the same or lower.

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

Overview

Douglas Eck is affiliated with Google in the United States and has an extensive research portfolio primarily focused on computer science. Their work covers a range of subfields including artificial intelligence, computer vision and pattern recognition, management science and operations research, information systems, and signal processing.

The scientist's research includes significant contributions to topics such as topic modeling, natural language processing techniques, multimodal machine learning applications, reinforcement learning in robotics, data quality and management, web data mining and analysis, and advanced malware detection techniques.

Douglas Eck's recent published papers include:

  • PaLM: Scaling Language Modeling with Pathways, 2022, arXiv (Cornell University)
  • Deduplicating Training Data Makes Language Models Better, 2022, Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
  • Gemma: Open Models Based on Gemini Research and Technology, 2024, arXiv (Cornell University)
  • A Real-World WebAgent with Planning, Long Context Understanding, and Program Synthesis, 2023, arXiv (Cornell University)
  • Emergent Social Learning via Multi-agent Reinforcement Learning, 2020, arXiv (Cornell University)

Frequent coauthors collaborating with Douglas Eck include Daphne Ippolito, Katherine Lee, Chris Callison-Burch, Chiyuan Zhang, and Nicholas Carlini. The partnership patterns suggest active involvement in collaborative research efforts.

The scientist's works have appeared predominantly in the following publication venues:

  • arXiv (Cornell University)
  • Zenodo (CERN European Organization for Nuclear Research)
  • Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Best Publications

  • A Neural Representation of Sketch Drawings

    David Ha;Douglas Eck

  • Neural audio synthesis of musical notes with WaveNet autoencoders

    Jesse Engel;Cinjon Resnick;Adam Roberts;Sander Dieleman

  • Aggregate features and ADABOOST for music classification

    James Bergstra;Norman Casagrande;Dumitru Erhan;Douglas Eck

  • LEARNING FEATURES FROM MUSIC AUDIO WITH DEEP BELIEF NETWORKS

    Philippe Hamel;Douglas Eck

  • A Hierarchical Latent Vector Model for Learning Long-Term Structure in Music

    Adam Roberts;Jesse H. Engel;Colin Raffel;Curtis Hawthorne

  • Music Transformer: Generating Music with Long-Term Structure

    Cheng-Zhi Anna Huang;Ashish Vaswani;Jakob Uszkoreit;Noam Shazeer

  • A First Look at Music Composition using LSTM Recurrent Neural Networks

    Douglas Eck;Juergen Schmidhuber

  • Finding temporal structure in music: blues improvisation with LSTM recurrent networks

    D. Eck;J. Schmidhuber

  • Applying LSTM to Time Series Predictable through Time-Window Approaches

    Felix Gers;Douglas Eck;Jürgen Schmidhuber

  • Enabling Factorized Piano Music Modeling and Generation with the MAESTRO Dataset.

    Curtis Hawthorne;Andriy Stasyuk;Adam Roberts;Ian Simon

  • Automatic Generation of Social Tags for Music Recommendation

    Douglas Eck;Paul Lamere;Thierry Bertin-mahieux;Stephen Green

  • Neural Audio Synthesis of Musical Notes with WaveNet Autoencoders

    Jesse Engel;Cinjon Resnick;Adam Roberts;Sander Dieleman

  • Deduplicating Training Data Makes Language Models Better

    Katherine Lee;Daphne Ippolito;Andrew Nystrom;Chiyuan Zhang

  • Online and linear-time attention by enforcing monotonic alignments

    Colin Raffel;Minh-Thang Luong;Peter J. Liu;Ron J. Weiss

  • Automatic Detection of Generated Text is Easiest when Humans are Fooled

    Daphne Ippolito;Daniel Duckworth;Chris Callison-Burch;Douglas Eck

  • Onsets and Frames: Dual-Objective Piano Transcription

    Curtis Hawthorne;Erich Elsen;Jialin Song;Adam Roberts

  • This time with feeling: learning expressive musical performance

    Sageev Oore;Ian Simon;Sander Dieleman;Douglas Eck

  • Autotagger: A Model for Predicting Social Tags from Acoustic Features on Large Music Databases

    Thierry Bertin-Mahieux;Douglas Eck;François Maillet;Paul Lamere

  • Kalman filters improve LSTM network performance in problems unsolvable by traditional recurrent nets

    Juan Antonio Pérez-Ortiz;Felix A. Gers;Douglas Eck;Jürgen Schmidhuber

  • Temporal pooling and multiscale learning for automatic annotation and ranking of music audio

    Philippe Hamel;Simon Lemieux;Yoshua Bengio;Douglas Eck

  • Music Transformer

    Cheng-Zhi Anna Huang;Ashish Vaswani;Jakob Uszkoreit;Noam Shazeer

Frequent Co-Authors

Jürgen Schmidhuber
Jürgen Schmidhuber King Abdullah University of Science and Technology
Colin Raffel
Colin Raffel University of Toronto
Samy Bengio
Samy Bengio Apple (United States)
Chris Callison-Burch
Chris Callison-Burch University of Pennsylvania
Yoshua Bengio
Yoshua Bengio University of Montreal
Karen Simonyan
Karen Simonyan DeepMind (United Kingdom)
Richard E. Turner
Richard E. Turner University of Cambridge
Shixiang Gu
Shixiang Gu Google (United States)

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