World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
59
Citations
16050
World Ranking
3391
National Ranking
1644

William Yang Wang 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 William Yang Wang 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: 202 publications — 47th percentile

47% of scientists in this discipline score the same or lower.

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

William Yang Wang 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 William Yang Wang 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: 59 D-Index — 77th percentile

77% of scientists in this discipline score the same or lower.

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

Overview

William Yang Wang is affiliated with the University of California, Santa Barbara in the United States. Their research primarily spans the field of Computer Science, with a focus on subfields such as Artificial Intelligence, Computer Vision and Pattern Recognition, Information Systems, Molecular Biology, and Management Science and Operations Research.

Their work covers a variety of topics, including:

  • Topic Modeling
  • Natural Language Processing Techniques
  • Multimodal Machine Learning Applications
  • Text Readability and Simplification
  • Speech and Dialogue Systems
  • Machine Learning in Healthcare
  • Advanced Text Analysis Techniques

William Yang Wang has contributed to several recent papers. Notable publications include:

  • "On the Opportunities and Risks of Foundation Models," 2021, arXiv (Cornell University)
  • "Holistic Evaluation of Language Models," 2022, arXiv (Cornell University)
  • "Associations of semaglutide with first-time diagnosis of Alzheimer's disease in patients with type 2 diabetes: Target trial emulation using nationwide real-world data in the US," 2024, Alzheimer's & Dementia
  • "Generative Adversarial Zero-Shot Relational Learning for Knowledge Graphs," 2020, Proceedings of the AAAI Conference on Artificial Intelligence
  • "Association of semaglutide with reduced incidence and relapse of cannabis use disorder in real-world populations: a retrospective cohort study," 2024, Molecular Psychiatry

The researcher frequently collaborates with colleagues including Wenhu Chen, Wenda Xu, Michael Saxon, Yi-Lin Tuan, and Zhiyu Chen.

William Yang Wang has published extensively in venues such as:

  • arXiv (Cornell University)
  • bioRxiv (Cold Spring Harbor Laboratory)
  • BMJ Global Health
  • Therapeutic Innovation & Regulatory Science
  • Proceedings of the AAAI Conference on Artificial Intelligence

Best Publications

  • On the Opportunities and Risks of Foundation Models.

    Rishi Bommasani;Drew A. Hudson;Ehsan Adeli;Russ Altman

  • "Liar, Liar Pants on Fire": A New Benchmark Dataset for Fake News Detection

    William Yang Wang

  • DeepPath: A Reinforcement Learning Method for Knowledge Graph Reasoning

    Wenhan Xiong;Thien Hoang;William Yang Wang

  • Reinforced Cross-Modal Matching and Self-Supervised Imitation Learning for Vision-Language Navigation

    Xin Wang;Qiuyuan Huang;Asli Celikyilmaz;Jianfeng Gao

  • Mitigating Gender Bias in Natural Language Processing: Literature Review

    Tony Sun;Andrew Gaut;Shirlyn Tang;Yuxin Huang

  • VaTeX: A Large-Scale, High-Quality Multilingual Dataset for Video-and-Language Research

    Xin Wang;Jiawei Wu;Junkun Chen;Lei Li

  • KBGAN: Adversarial Learning for Knowledge Graph Embeddings

    Liwei Cai;William Yang Wang

  • That's So Annoying!!!: A Lexical and Frame-Semantic Embedding Based Data Augmentation Approach to Automatic Categorization of Annoying Behaviors using #petpeeve Tweets

    William Yang Wang;Diyi Yang

  • REVERIE: Remote Embodied Visual Referring Expression in Real Indoor Environments

    Yuankai Qi;Qi Wu;Peter Anderson;Xin Wang

  • Video Captioning via Hierarchical Reinforcement Learning

    Xin Wang;Wenhu Chen;Jiawei Wu;Yuan-Fang Wang

  • Hate Lingo: A Target-Based Linguistic Analysis of Hate Speech in Social Media

    Mai ElSherief;Vivek Kulkarni;Dana Nguyen;William Yang Wang

  • A Survey on Natural Language Processing for Fake News Detection

    Ray Oshikawa;Jing Qian;William Yang Wang

  • One-Shot Relational Learning for Knowledge Graphs

    Wenhan Xiong;Mo Yu;Shiyu Chang;Xiaoxiao Guo

  • TabFact: A Large-scale Dataset for Table-based Fact Verification

    Wenhu Chen;Hongmin Wang;Jianshu Chen;Yunkai Zhang

  • Diagnosing performance changes by comparing request flows

    Raja R. Sambasivan;Alice X. Zheng;Michael De Rosa;Elie Krevat

  • Robust Distant Supervision Relation Extraction via Deep Reinforcement Learning

    Pengda Qin;Weiran Xu;William Yang Wang

  • WikiHow: A Large Scale Text Summarization Dataset

    Mahnaz Koupaee;William Yang Wang

  • Look Before You Leap: Bridging Model-Free and Model-Based Reinforcement Learning for Planned-Ahead Vision-and-Language Navigation

    Xin Wang;Wenhan Xiong;Hongmin Wang;William Yang Wang

  • MojiTalk: Generating Emotional Responses at Scale

    Xianda Zhou;William Yang Wang

  • HybridQA: A Dataset of Multi-Hop Question Answering over Tabular and Textual Data

    Wenhu Chen;Hanwen Zha;Zhiyu Chen;Wenhan Xiong

Frequent Co-Authors

Mo Yu
Mo Yu IBM (United States)
Xiaoxiao Guo
Xiaoxiao Guo The University of Texas at Dallas
Shiyu Chang
Shiyu Chang University of California, Santa Barbara
Elizabeth Belding
Elizabeth Belding University of California, Santa Barbara
William W. Cohen
William W. Cohen Carnegie Mellon University
Yuan-Fang Wang
Yuan-Fang Wang University of California, Santa Barbara
Miguel P. Eckstein
Miguel P. Eckstein University of California, Santa Barbara
Xifeng Yan
Xifeng Yan University of California, Santa Barbara
Yu Su
Yu Su The Ohio State University
Scott T. Grafton
Scott T. Grafton University of California, Santa Barbara

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