World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
46
Citations
7373
World Ranking
6919
National Ranking
3029

Kevin Duh 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 Kevin Duh 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: 208 publications — 49th percentile

49% of scientists in this discipline score the same or lower.

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

Kevin Duh 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 Kevin Duh 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: 46 D-Index — 53rd percentile

53% of scientists in this discipline score the same or lower.

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

Overview

Kevin Duh is affiliated with Johns Hopkins University in the United States. Their primary research domain is Computer Science, with a focus on Artificial Intelligence among other subfields such as Computer Vision and Pattern Recognition, Information Systems, Signal Processing, and Statistical and Nonlinear Physics.

Their work covers several main topics, including:

  • Natural Language Processing Techniques
  • Topic Modeling
  • Speech Recognition and Synthesis
  • Multimodal Machine Learning Applications
  • Text Readability and Simplification
  • Advanced Graph Neural Networks
  • Complex Network Analysis Techniques

Kevin Duh's research contributions are documented across various publication venues, notably:

  • arXiv (Cornell University)
  • Proceedings of the International AAAI Conference on Web and Social Media
  • Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
  • Transactions of the Association for Computational Linguistics
  • Thrombosis Journal

Their recent papers include:

  • "Very Deep Transformers for Neural Machine Translation," 2020, arXiv (Cornell University)
  • "Low-Resource Named Entity Recognition with Cross-Lingual, Character-Level Neural Conditional Random Fields," 2024, arXiv (Cornell University)
  • "Creating Stories: Social Curation of Twitter Messages," 2021, Proceedings of the International AAAI Conference on Web and Social Media
  • "When Does Unsupervised Machine Translation Work?," 2020, arXiv (Cornell University)
  • "Data and Parameter Scaling Laws for Neural Machine Translation," 2021, Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Kevin Duh frequently collaborates with several researchers, including:

  • Shinji Watanabe
  • Matthew Wiesner
  • Sanjeev Khudanpur
  • Kenton Murray
  • Dawn Lawrie

Best Publications

  • DyNet: The Dynamic Neural Network Toolkit

    Graham Neubig;Chris Dyer;Yoav Goldberg;Austin Matthews

  • Representation Learning Using Multi-Task Deep Neural Networks for Semantic Classification and Information Retrieval

    Xiaodong Liu;Jianfeng Gao;Xiaodong He;Li Deng

  • Automatic Evaluation of Translation Quality for Distant Language Pairs

    Hideki Isozaki;Tsutomu Hirao;Kevin Duh;Katsuhito Sudoh

  • ReCoRD: Bridging the Gap between Human and Machine Commonsense Reading Comprehension.

    Sheng Zhang;Xiaodong Liu;Jingjing Liu;Jianfeng Gao

  • Compressing BERT: Studying the Effects of Weight Pruning on Transfer Learning

    Mitchell A. Gordon;Kevin Duh;Nicholas Andrews

  • Stochastic Answer Networks for Machine Reading Comprehension

    Xiaodong Liu;Yelong Shen;Kevin Duh;Jianfeng Gao

  • ESPnet-ST: All-in-One Speech Translation Toolkit

    Hirofumi Inaguma;Shun Kiyono;Kevin Duh;Shigeki Karita

  • Findings of the IWSLT 2022 Evaluation Campaign

    Unknown

  • Morphology-Based Language Modeling for Arabic Speech Recognition

    Dimitra Vergyri;Katrin Kirchhoff;Kevin Duh;Andreas Stolcke

  • Morphology-based language modeling for conversational Arabic speech recognition

    Katrin Kirchhoff;Dimitra Vergyri;Jeff A. Bilmes;Kevin Duh

  • Ordinal Common-sense Inference

    Sheng Zhang;Rachel Rudinger;Kevin Duh;Benjamin Van Durme

  • Overcoming Catastrophic Forgetting During Domain Adaptation of Neural Machine Translation

    Brian Thompson;Jeremy Gwinnup;Huda Khayrallah;Kevin Duh

  • A framework for analyzing semantic change of words across time

    Adam Jatowt;Kevin Duh

  • Learning to rank with partially-labeled data

    Kevin Duh;Katrin Kirchhoff

  • An Empirical Exploration of Curriculum Learning for Neural Machine Translation

    Xuan Zhang;Gaurav Kumar;Huda Khayrallah;Kenton Murray

  • Adaptation Data Selection using Neural Language Models: Experiments in Machine Translation

    Kevin Duh;Graham Neubig;Katsuhito Sudoh;Hajime Tsukada

  • Curriculum Learning for Domain Adaptation in Neural Machine Translation

    Xuan Zhang;Pamela Shapiro;Gaurav Kumar;Paul McNamee

  • Head Finalization: A Simple Reordering Rule for SOV Languages

    Hideki Isozaki;Katsuhito Sudoh;Hajime Tsukada;Kevin Duh

  • Membership Inference Attacks on Sequence-to-Sequence Models: Is My Data In Your Machine Translation System?

    Sorami Hisamoto;Matt Post;Kevin Duh

  • AMR Parsing as Sequence-to-Graph Transduction.

    Sheng Zhang;Xutai Ma;Kevin Duh;Benjamin Van Durme

  • Very Deep Transformers for Neural Machine Translation

    Xiaodong Liu;Kevin Duh;Liyuan Liu;Jianfeng Gao

  • Membership Inference Attacks on Sequence-to-Sequence Models

    Sorami Hisamoto;Matt Post;Kevin Duh

Frequent Co-Authors

Yuji Matsumoto
Yuji Matsumoto Nara Institute of Science and Technology
Benjamin Van Durme
Benjamin Van Durme Johns Hopkins University
Philipp Koehn
Philipp Koehn Johns Hopkins University
Katrin Kirchhoff
Katrin Kirchhoff Amazon (United States)
Shinji Watanabe
Shinji Watanabe Carnegie Mellon University
Graham Neubig
Graham Neubig Carnegie Mellon University
Taku Komura
Taku Komura University of Edinburgh
Marine Carpuat
Marine Carpuat University of Maryland, College Park
Jianfeng Gao
Jianfeng Gao Microsoft (United States)
Tatsuya Kawahara
Tatsuya Kawahara Kyoto University

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