World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
38
Citations
8681
World Ranking
10033
National Ranking
4227

Chun-Nan Hsu 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 Chun-Nan Hsu 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: 157 publications — 30th percentile

30% of scientists in this discipline score the same or lower.

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

Chun-Nan Hsu 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 Chun-Nan Hsu 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: 38 D-Index — 30th percentile

30% of scientists in this discipline score the same or lower.

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

Research.com Recognitions

  • 2011 - ACM Senior Member

Overview

Chun-Nan Hsu is affiliated with the University of California, San Diego in the United States and has an extensive publication record spanning multiple fields related to computer science and medicine. Their research contributions cover a broad range of topics primarily focusing on artificial intelligence applications in healthcare and biomedical informatics.

The main fields of study for Chun-Nan Hsu are:

  • Computer Science
  • Medicine

The scientist's subfields of study include:

  • Artificial Intelligence
  • Radiology, Nuclear Medicine and Imaging
  • Molecular Biology
  • Health Informatics
  • Computer Vision and Pattern Recognition

Chun-Nan Hsu has worked extensively on key research topics, including:

  • Topic Modeling
  • Natural Language Processing Techniques
  • Biomedical Text Mining and Ontologies
  • Artificial Intelligence in Healthcare and Education
  • Monoclonal and Polyclonal Antibodies Research
  • Machine Learning in Healthcare
  • Advanced Graph Neural Networks

Their recent notable publications include:

  • Federated learning for predicting clinical outcomes in patients with COVID-19 (2021), published in Nature Medicine
  • RadBERT: Adapting Transformer-based Language Models to Radiology (2022), published in Radiology Artificial Intelligence
  • SPOT (2022), published in Proceedings of the 31st ACM International Conference on Information & Knowledge Management
  • Robust and Interpretable Medical Image Classifiers via Concept Bottleneck Models (2023), published on arXiv (Cornell University)
  • Theoretical Rule-based Knowledge Graph Reasoning by Connectivity Dependency Discovery (2022), published in the 2022 International Joint Conference on Neural Networks (IJCNN)

Frequent co-authors who have collaborated with Chun-Nan Hsu include:

  • Amilcare Gentili
  • Julian McAuley
  • Yannis Katsis
  • Anita Bandrowski
  • İbrahim Burak Özyurt

The scientist has contributed publications to several prominent venues, among which the most frequent are:

  • arXiv (Cornell University)
  • Zenodo (CERN European Organization for Nuclear Research)
  • Proceedings on CD-ROM, International Society for Magnetic Resonance in Medicine Scientific Meeting and Exhibition
  • Nature Medicine
  • Radiology Artificial Intelligence

Among academic distinctions, Chun-Nan Hsu was recognized as an ACM Senior Member in 2011.

Best Publications

  • Generating finite-state transducers for semi-structured data extraction from the Web

    Chun-Nan Hsu;Ming-Tzung Dung

  • Retrieving and Integrating Data from Multiple Information Sources

    Yigal Arens;Chin Y. Chee;Chun-Nan Hsu;Craig A. Knoblock

  • Evaluation and accurate diagnoses of pediatric diseases using artificial intelligence

    Huiying Liang;Brian Y. Tsui;Hao Ni;Carolina C. S. Valentim

  • Federated learning for predicting clinical outcomes in patients with COVID-19.

    Ittai Dayan;Holger R. Roth;Aoxiao Zhong;Ahmed Harouni

  • FASTSNP: an always up-to-date and extendable service for SNP function analysis and prioritization

    Hsiang-Yu Yuan;Jen-Jie Chiou;Wen-Hsien Tseng;Chia-Hung Liu

  • Overview of BioCreative II gene mention recognition

    Larry Smith;Lorraine K Tanabe;Rie Johnson nee Ando;Cheng-Ju Kuo

  • Weakly supervised learning of biomedical information extraction from curated data

    Suvir Jain;R Kashyap;Tsung-Ting Kuo;Shitij Bhargava

  • Overview of BioCreative II gene normalization.

    Alexander A. Morgan;Zhiyong Lu;Xinglong Wang;Aaron M. Cohen

  • Query processing in the SIMS information mediator

    Yigal Arens;Chun-Nan Hsu;Craig A. Knoblock

  • Automatic information extraction from semi-structured Web pages by pattern discovery

    Chia-Hui Chang;Chun-Nan Hsu;Shao-Cheng Lui

  • The use of phenome-wide association studies (PheWAS) for exploration of novel genotype-phenotype relationships and pleiotropy discovery.

    S.A. Pendergrass;K. Brown-Gentry;S.M. Dudek;E.S. Torstenson

  • RadBERT: Adapting Transformer-based Language Models to Radiology.

    Unknown

  • The gene normalization task in BioCreative III

    Zhiyong Lu;Hung-Yu Kao;Chih-Hsuan Wei;Minlie Huang

  • The ANNIGMA-wrapper approach to fast feature selection for neural nets

    Chun-Nan Hsu;Hung-Ju Huang;S. Dietrich

  • Automatic morphological subtyping reveals new roles of caspases in mitochondrial dynamics.

    Jyh Ying Peng;Jyh Ying Peng;Chung Chih Lin;Yen Jen Chen;Lung Sen Kao

  • Cooperating agents for information retrieval

    Craig A. Knoblock;Yigal Arens;Chun-Nan Hsu

  • Speech Recognition on Code-Switching Among the Chinese Dialects

    Dau-cheng Lyu;Ren-yuan Lyu;Yuang-chin Chiang;Chun-nan Hsu

  • Integrating high dimensional bi-directional parsing models for gene mention tagging

    Chun-Nan Hsu;Yu-Ming Chang;Cheng-Ju Kuo;Yu-Shi Lin

  • Introducing meta-services for biomedical information extraction

    Florian Leitner;Martin Krallinger;Carlos Rodríguez-Penagos;Jörg Hakenberg;Jörg Hakenberg

  • Gene Expression-Based Chemical Genomics Identifies Potential Therapeutic Drugs in Hepatocellular Carcinoma

    Ming Huang Chen;Wu Lung R. Yang;Kuan Ting Lin;Kuan Ting Lin;Chia Hung Liu;Chia Hung Liu

  • Mining Skewed and Sparse Transaction Data for Personalized Shopping Recommendation

    Chun-Nan Hsu;Hao-Hsiang Chung;Han-Shen Huang

  • Why Discretization Works for Naive Bayesian Classifiers

    Chun-Nan Hsu;Hung-Ju Huang;Tzu-Tsung Wong

Frequent Co-Authors

Wen-chang Lin
Wen-chang Lin Academia Sinica
Craig A. Knoblock
Craig A. Knoblock University of Southern California
Julian McAuley
Julian McAuley University of California, San Diego
Chi-Ying F. Huang
Chi-Ying F. Huang National Yang Ming University
José Luis Ambite
José Luis Ambite University of Southern California
Ho-Cheol Kim
Ho-Cheol Kim IBM (United States)
Lucila Ohno-Machado
Lucila Ohno-Machado University of California, San Diego
Susan L. Forsburg
Susan L. Forsburg University of Southern California
Hannah Carter
Hannah Carter University of California, San Diego
Patrick Ruch
Patrick Ruch Swiss Institute of Bioinformatics

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