World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
93
Citations
33639
World Ranking
516
National Ranking
276

Jimeng Sun 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 Jimeng Sun 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: 362 publications — 83rd percentile

83% of scientists in this discipline score the same or lower.

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

Jimeng Sun 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 Jimeng Sun 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: 93 D-Index — 97th percentile

97% of scientists in this discipline score the same or lower.

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

Overview

Jimeng Sun is a researcher affiliated with the University of Illinois at Urbana-Champaign in the United States. Their work primarily spans the fields of Computer Science and Medicine, with a significant focus on Artificial Intelligence and its applications across various domains.

Their scholarly contributions cover multiple subfields including:

  • Artificial Intelligence
  • Molecular Biology
  • Computational Theory and Mathematics
  • Radiology, Nuclear Medicine and Imaging
  • Materials Chemistry

Jimeng Sun's research topics emphasize:

  • Machine Learning in Healthcare
  • Topic Modeling
  • Computational Drug Discovery Methods
  • Biomedical Text Mining and Ontologies
  • Machine Learning in Materials Science
  • Artificial Intelligence in Healthcare and Education
  • Artificial Intelligence in Healthcare

Their recent published papers include:

  • Scientific discovery in the age of artificial intelligence (2023), published in Nature
  • MolTrans: Molecular Interaction Transformer for drug-target interaction prediction (2020), published in Bioinformatics
  • Opportunities and challenges of deep learning methods for electrocardiogram data: A systematic review (2020), published in Computers in Biology and Medicine
  • DeepPurpose: a deep learning library for drug-target interaction prediction (2020), published in Bioinformatics
  • Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States (2022), published in Proceedings of the National Academy of Sciences

Jimeng Sun frequently collaborates with other researchers, including:

  • Cao Xiao
  • Lucas M. Glass
  • Tianfan Fu
  • Brandon Theodorou
  • Zifeng Wang

Their work has been published extensively in venues such as:

  • arXiv (Cornell University)
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • bioRxiv (Cold Spring Harbor Laboratory)
  • Journal of the American Medical Informatics Association
  • Patterns

Best Publications

  • Social influence analysis in large-scale networks

    Jie Tang;Jimeng Sun;Chi Wang;Zi Yang

  • Doctor AI: Predicting Clinical Events via Recurrent Neural Networks

    Edward Choi;Mohammad Taha Bahadori;Andy Schuetz;Walter F. Stewart

  • RETAIN: An interpretable predictive model for healthcare using reverse time attention mechanism

    Edward Choi;Mohammad Taha Bahadori;Jimeng Sun;Joshua Kulas

  • Using recurrent neural network models for early detection of heart failure onset.

    Edward Choi;Andy Schuetz;Walter F Stewart;Jimeng Sun

  • Opportunities and challenges in developing deep learning models using electronic health records data: a systematic review.

    Cao Xiao;Edward Choi;Jimeng Sun

  • The TPR*-tree: an optimized spatio-temporal access method for predictive queries

    Yufei Tao;Dimitris Papadias;Jimeng Sun

  • GRAM: Graph-based Attention Model for Healthcare Representation Learning

    Edward Choi;Mohammad Taha Bahadori;Le Song;Walter F. Stewart

  • GraphScope: parameter-free mining of large time-evolving graphs

    Jimeng Sun;Christos Faloutsos;Spiros Papadimitriou;Philip S. Yu

  • Beyond streams and graphs: dynamic tensor analysis

    Jimeng Sun;Dacheng Tao;Christos Faloutsos

  • Streaming pattern discovery in multiple time-series

    Spiros Papadimitriou;Jimeng Sun;Christos Faloutsos

  • Multi-layer Representation Learning for Medical Concepts

    Edward Choi;Mohammad Taha Bahadori;Elizabeth Searles;Catherine Coffey

  • Explainable Prediction of Medical Codes from Clinical Text

    James Mullenbach;Sarah Wiegreffe;Jon Duke;Jimeng Sun

  • MolTrans: Molecular interaction transformer for drug target interaction prediction

    Kexin Huang;Cao Xiao;Lucas M Glass;Jimeng Sun

  • Temporal recommendation on graphs via long- and short-term preference fusion

    Liang Xiang;Quan Yuan;Shiwan Zhao;Li Chen

  • Scalable Tensor Decompositions for Multi-aspect Data Mining

    T.G. Kolda;Jimeng Sun

  • Generating Multi-label Discrete Patient Records using Generative Adversarial Networks

    Edward Choi;Siddharth Biswal;Bradley A. Malin;Jon Duke

  • Neighborhood formation and anomaly detection in bipartite graphs

    Jimeng Sun;Huiming Qu;D. Chakrabarti;C. Faloutsos

  • From hype to reality: data science enabling personalized medicine

    Holger Fröhlich;Rudi Balling;Niko Beerenwinkel;Oliver Kohlbacher

  • Opportunities and challenges of deep learning methods for electrocardiogram data: A systematic review.

    Shenda Hong;Yuxi Zhou;Junyuan Shang;Cao Xiao

  • Proceedings of the 2017 ACM on Conference on Information and Knowledge Management

    Ee-Peng Lim;Marianne Winslett;Mark Sanderson;Ada Fu

  • Cross-domain collaboration recommendation

    Jie Tang;Sen Wu;Jimeng Sun;Hang Su

  • RETAIN: An Interpretable Predictive Model for Healthcare using Reverse Time Attention Mechanism

    Edward Choi;Mohammad Taha Bahadori;Joshua A. Kulas;Andy Schuetz

  • SUSTain: Scalable Unsupervised Scoring for Tensors and its Application to Phenotyping

    Ioakeim Perros;Evangelos E. Papalexakis;Haesun Park;Richard Vuduc

Frequent Co-Authors

Cao Xiao
Cao Xiao General Electric (United Kingdom)
Christos Faloutsos
Christos Faloutsos Carnegie Mellon University
Bradley A. Malin
Bradley A. Malin Vanderbilt University Medical Center
Walter F. Stewart
Walter F. Stewart Sutter Health
Robert Chen
Robert Chen University Health Network
M. Brandon Westover
M. Brandon Westover Harvard University
Philip S. Yu
Philip S. Yu University of Illinois at Chicago
Richard Vuduc
Richard Vuduc Georgia Institute of Technology
Jie Tang
Jie Tang Tsinghua University
Yufei Tao
Yufei Tao Chinese University of Hong Kong

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