World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
69
Citations
35600
World Ranking
1920
National Ranking
974

Yonghui Wu 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 Yonghui Wu 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: 224 publications — 55th percentile

55% of scientists in this discipline score the same or lower.

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

Yonghui Wu 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 Yonghui Wu 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: 69 D-Index — 87th percentile

87% of scientists in this discipline score the same or lower.

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

Overview

Yonghui Wu is affiliated with Google in the United States and has a research focus largely spanning Computer Science and Medicine. Their work emphasizes multiple subfields including Artificial Intelligence, Signal Processing, Molecular Biology, General Health Professions, and Pulmonary and Respiratory Medicine.

The scientist's main research topics include:

  • Topic Modeling
  • Natural Language Processing Techniques
  • Machine Learning in Healthcare
  • Speech Recognition and Synthesis
  • Biomedical Text Mining and Ontologies
  • Music and Audio Processing
  • Speech and Audio Processing

Yonghui Wu's publication record features contributions to numerous academic venues. They have frequently published in:

  • arXiv (Cornell University)
  • bioRxiv (Cold Spring Harbor Laboratory)
  • Journal of the American Medical Informatics Association
  • Alzheimer's & Dementia
  • npj Digital Medicine

Selected recent papers demonstrate a focus on large language models, electronic health records, speech recognition, and natural language processing in healthcare contexts. Notable recent publications include:

  • "A large language model for electronic health records" (2022, npj Digital Medicine)
  • "Conformer: Convolution-augmented Transformer for Speech Recognition" (2020, arXiv)
  • "A study of generative large language model for medical research and healthcare" (2023, npj Digital Medicine)
  • "w2v-BERT: Combining Contrastive Learning and Masked Language Modeling for Self-Supervised Speech Pre-Training" (2021, 2021 IEEE Automatic Speech Recognition and Understanding Workshop)
  • "Extracting social determinants of health from electronic health records using natural language processing: a systematic review" (2021, Journal of the American Medical Informatics Association)

Throughout their career, Yonghui Wu has frequently collaborated with a number of fellow researchers. Regular coauthors include Jiang Bian, Yi Guo, Xi Yang, William R. Hogan, and Ruoming Pang.

Best Publications

  • Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation

    Yonghui Wu;Mike Schuster;Zhifeng Chen;Quoc V. Le

  • Conformer: Convolution-augmented Transformer for Speech Recognition

    Anmol Gulati;James Qin;Chung-Cheng Chiu;Niki Parmar

  • Natural TTS Synthesis by Conditioning Wavenet on MEL Spectrogram Predictions

    Jonathan Shen;Ruoming Pang;Ron J. Weiss;Mike Schuster

  • Google's Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation

    Melvin Johnson;Mike Schuster;Quoc V. Le;Maxim Krikun

  • Tacotron: Towards End-to-End Speech Synthesis

    Yuxuan Wang;R. J. Skerry-Ryan;Daisy Stanton;Yonghui Wu

  • Exploring the limits of language modeling

    Rafal Jozefowicz;Oriol Vinyals;Mike Schuster;Noam Shazeer

  • State-of-the-Art Speech Recognition with Sequence-to-Sequence Models

    Chung-Cheng Chiu;Tara N. Sainath;Yonghui Wu;Rohit Prabhavalkar

  • GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism

    Yanping Huang;Youlong Cheng;Ankur Bapna;Orhan Firat

  • A large language model for electronic health records

    Unknown

  • LibriTTS: A Corpus Derived from LibriSpeech for Text-to-Speech

    Heiga Zen;Viet Dang;Rob Clark;Yu Zhang

  • Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesis

    Ye Jia;Yu Zhang;Ron J. Weiss;Quan Wang

  • Efficient and accurate construction of genetic linkage maps from the minimum spanning tree of a graph.

    Yonghui Wu;Prasanna R. Bhat;Timothy J. Close;Stefano Lonardi

  • Streaming End-to-end Speech Recognition for Mobile Devices

    Yanzhang He;Tara N. Sainath;Rohit Prabhavalkar;Ian McGraw

  • w2v-BERT: Combining Contrastive Learning and Masked Language Modeling for Self-Supervised Speech Pre-Training

    Unknown

  • Conformer: Convolution-augmented Transformer for Speech Recognition

    Anmol Gulati;James Qin;Chung-Cheng Chiu;Niki Parmar

  • The Best of Both Worlds: Combining Recent Advances in Neural Machine Translation

    Mia Xu Chen;Orhan Firat;Ankur Bapna;Melvin Johnson

  • Massively Multilingual Neural Machine Translation in the Wild: Findings and Challenges

    Naveen Arivazhagan;Ankur Bapna;Orhan Firat;Dmitry Lepikhin

  • CLAMP - a toolkit for efficiently building customized clinical natural language processing pipelines.

    Ergin Soysal;Jingqi Wang;Min Jiang;Yonghui Wu

  • Sequence-to-Sequence Models Can Directly Translate Foreign Speech

    Ron J. Weiss;Jan Chorowski;Navdeep Jaitly;Yonghui Wu

  • Large-scale prediction of adverse drug reactions using chemical, biological, and phenotypic properties of drugs

    Mei Liu;Yonghui Wu;Yukun Chen;Jingchun Sun

  • A study of generative large language model for medical research and healthcare

    Unknown

  • Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesis

    Ye Jia;Yu Zhang;Ron J. Weiss;Quan Wang

  • GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism

    Yanping Huang;Youlong Cheng;Ankur Bapna;Orhan Firat

  • ContextNet: Improving Convolutional Neural Networks for Automatic Speech Recognition with Global Context

    Wei Han;Zhengdong Zhang;Yu Zhang;Jiahui Yu

  • Improved Noisy Student Training for Automatic Speech Recognition

    Daniel S. Park;Yu Zhang;Ye Jia;Wei Han

  • Lingvo: a Modular and Scalable Framework for Sequence-to-Sequence Modeling

    Jonathan Shen;Patrick Nguyen;Yonghui Wu;Zhifeng Chen

  • Pushing the Limits of Semi-Supervised Learning for Automatic Speech Recognition

    Yu Zhang;James Qin;Daniel S. Park;Wei Han

  • An Analysis of Incorporating an External Language Model into a Sequence-to-Sequence Model

    Anjuli Kannan;Yonghui Wu;Patrick Nguyen;Tara N. Sainath

Frequent Co-Authors

Zhifeng Chen
Zhifeng Chen Google (United States)
Tara N. Sainath
Tara N. Sainath Google (United States)
Chung-Cheng Chiu
Chung-Cheng Chiu Google (United States)
Ruoming Pang
Ruoming Pang Google (United States)
Patrick Nguyen
Patrick Nguyen Google (United States)
Bo Li
Bo Li University of Illinois at Urbana-Champaign
Rohit Prabhavalkar
Rohit Prabhavalkar Google (United States)
Stefano Lonardi
Stefano Lonardi University of California, Riverside
Navdeep Jaitly
Navdeep Jaitly Google (United States)

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 online degrees and certifications can greatly enhance your career prospects in Computer Science and related fields. Many students are drawn to flexible and cost-effective options, such as the cheapest data science masters in usa. These programs provide high-quality education at a fraction of the cost, making advanced study accessible to a wider audience.

Career outcomes also vary depending on the specialization you pursue. For example, an online electrical engineering career outcomes study highlights opportunities in robotics, power systems, and telecommunications, all of which have strong job prospects in the tech industry.

If you are seeking quick ways to boost your credentials, consider easy certifications to get. These certification programs can be completed in a short time and still lead to rewarding roles with competitive salaries.

For those aiming to earn a master’s degree swiftly, check out some of the shortest masters degree options available. These condensed programs help you enter the workforce sooner while optimizing both time and tuition investment.

Best Scientists Citing Yonghui Wu

Trending Scientists

Recently Published Articles