World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
39
Citations
11758
World Ranking
9519
National Ranking
4033

Hoifung Poon 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 Hoifung Poon 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: 126 publications — 17th percentile

17% of scientists in this discipline score the same or lower.

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

Hoifung Poon 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 Hoifung Poon 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

Hoifung Poon is affiliated with Microsoft in the United States. Their main field of study is computer science, with a focus on artificial intelligence and related subfields. The scientist's work extensively covers topics in artificial intelligence, molecular biology, and computer vision and pattern recognition, alongside several areas within health informatics and medical imaging.

The primary research topics explored by Hoifung Poon include:

  • Topic Modeling
  • Natural Language Processing Techniques
  • Biomedical Text Mining and Ontologies
  • Artificial Intelligence in Healthcare and Education
  • Multimodal Machine Learning Applications
  • Machine Learning in Healthcare
  • Radiomics and Machine Learning in Medical Imaging

The scientist has contributed significantly to several recent publications in notable venues. Selected papers include:

  • "Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing," 2021, ACM Transactions on Computing for Healthcare
  • "BioGPT: generative pre-trained transformer for biomedical text generation and mining," 2022, Briefings in Bioinformatics
  • "A whole-slide foundation model for digital pathology from real-world data," 2024, Nature
  • "LLaVA-Med: Training a Large Language-and-Vision Assistant for Biomedicine in One Day," 2023, arXiv (Cornell University)
  • "Can Generalist Foundation Models Outcompete Special-Purpose Tuning? Case Study in Medicine," 2023, arXiv (Cornell University)

Frequent co-authors collaborating with Hoifung Poon include:

  • Tristan Naumann
  • Naoto Usuyama
  • Sheng Zhang
  • Cliff Wong
  • Brian Piening

The scientist regularly publishes in venues related to computer science and biomedical machine learning. Prominent publication venues are:

  • arXiv (Cornell University)
  • bioRxiv (Cold Spring Harbor Laboratory)
  • Patterns
  • NEJM AI
  • Nature Communications

Overall, Hoifung Poon's research spans a variety of subfields including artificial intelligence, molecular biology, and medical imaging. Their work contributes broadly to methods and applications intersecting machine learning and healthcare, providing insights across natural language processing, biomedical information extraction, and multimodal data analysis.

Best Publications

  • Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing

    Yu Gu;Robert Tinn;Hao Cheng;Michael Lucas

  • Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing

    Yu Gu;Robert Tinn;Hao Cheng;Michael Lucas

  • BioGPT: Generative Pre-trained Transformer for Biomedical Text Generation and Mining

    Unknown

  • Representing Text for Joint Embedding of Text and Knowledge Bases

    Kristina Toutanova;Danqi Chen;Patrick Pantel;Hoifung Poon

  • Sum-product networks: a new deep architecture

    Hoifung Poon;Pedro Domingos

  • Cross-Sentence N-ary Relation Extraction with Graph LSTMs

    Nanyun Peng;Hoifung Poon;Chris Quirk;Kristina Toutanova

  • Sound and efficient inference with probabilistic and deterministic dependencies

    Hoifung Poon;Pedro Domingos

  • Joint inference in information extraction

    Hoifung Poon;Pedro Domingos

  • Unsupervised Semantic Parsing

    Hoifung Poon;Pedro Domingos

  • LLaVA-Med: Training a Large Language-and-Vision Assistant for Biomedicine in One Day

    Unknown

  • Joint Unsupervised Coreference Resolution with Markov Logic

    Hoifung Poon;Pedro Domingos

  • Neural-Symbolic Learning and Reasoning: A Survey and Interpretation

    Tarek R. Besold;Artur d'Avila Garcez;Sebastian Bader;Howard Bowman

  • Making the Most of Text Semantics to Improve Biomedical Vision-Language Processing

    Unknown

  • Distant Supervision for Relation Extraction beyond the Sentence Boundary

    Chris Quirk;Hoifung Poon

  • Unsupervised Ontology Induction from Text

    Hoifung Poon;Pedro Domingos

  • Fine-tuning large neural language models for biomedical natural language processing

    Unknown

  • Unsupervised Morphological Segmentation with Log-Linear Models

    Hoifung Poon;Colin Cherry;Kristina Toutanova

  • Document-Level N-ary Relation Extraction with Multiscale Representation Learning.

    Robin Jia;Cliff Wong;Hoifung Poon

  • Can Generalist Foundation Models Outcompete Special-Purpose Tuning? Case Study in Medicine

    Unknown

  • Sum-product networks: A new deep architecture

    Hoifung Poon;Pedro Domingos

  • Unifying Logical and Statistical AI

    Pedro Domingos;Daniel Lowd;Stanley Kok;Aniruddh Nath

  • Compositional Learning of Embeddings for Relation Paths in Knowledge Base and Text

    Kristina Toutanova;Victoria Lin;Wen-tau Yih;Hoifung Poon

  • Joint Inference for Knowledge Extraction from Biomedical Literature

    Hoifung Poon;Lucy Vanderwende

  • Upper Bounds of Dynamic Chromatic Number.

    Hong-Jian Lai;Bruce Montgomery;Hoifung Poon

  • Unifying logical and statistical AI

    Pedro Domingos;Stanley Kok;Hoifung Poon;Matthew Richardson

  • Adversarial Training for Large Neural Language Models

    Xiaodong Liu;Hao Cheng;Pengcheng He;Weizhu Chen

  • Unsupervised semantic parsing.

    Hoifung Poon

Frequent Co-Authors

Pedro Domingos
Pedro Domingos University of Washington
Kristina Toutanova
Kristina Toutanova Google (United States)
Chris Quirk
Chris Quirk Microsoft (United States)
Matthew Richardson
Matthew Richardson Microsoft (United States)
Jianfeng Gao
Jianfeng Gao Microsoft (United States)
Wen-tau Yih
Wen-tau Yih Facebook (United States)
Eric Horvitz
Eric Horvitz Microsoft (United States)
Bill Howe
Bill Howe University of Washington
David Heckerman
David Heckerman Microsoft (United States)
Lucy Vanderwende
Lucy Vanderwende University of Washington

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