World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
51
Citations
11518
World Ranking
5301
National Ranking
72

Han Yu 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 Han Yu 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: 250 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: 560 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: 424 scientists 242–251 publications: 408 scientists 252–261 publications: 378 scientists 262–271 publications: 300 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: 416 publications — 88th percentile

88% of scientists in this discipline score the same or lower.

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

Han Yu 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 Han Yu sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 984 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 969 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 765 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 517 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: 51 D-Index — 63rd percentile

63% of scientists in this discipline score the same or lower.

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

Overview

Han Yu is affiliated with Nanyang Technological University in Singapore and has contributed extensively to the field of computer science, with a specialization in artificial intelligence and its related subfields.

The scientist's research spans a variety of subfields, including:

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Information Systems
  • Molecular Biology
  • Electrical and Electronic Engineering

Han Yu's work covers key topics within these areas, focusing on:

  • Privacy-Preserving Technologies in Data
  • Cryptography and Data Security
  • Mobile Crowdsensing and Crowdsourcing
  • Stochastic Gradient Optimization Techniques
  • Advanced Graph Neural Networks
  • Domain Adaptation and Few-Shot Learning
  • Blockchain Technology Applications and Security

Frequent coauthors include Qiang Yang, Yang Liu, Dusit Niyato, Tianjian Chen, and Chunyan Miao, indicating strong collaborative ties within the research community.

Han Yu has published extensively across multiple venues. The most frequent publication platforms are:

  • arXiv (Cornell University)
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • IEEE Internet of Things Journal
  • IEEE Transactions on Neural Networks and Learning Systems
  • Knowledge-Based Systems

Notable recent papers authored or coauthored by Han Yu include:

  • A systematic literature review of the capabilities and performance metrics of supply chain resilience, 2020, International Journal of Production Research
  • Privacy and Robustness in Federated Learning: Attacks and Defenses, 2022, IEEE Transactions on Neural Networks and Learning Systems
  • FedVision: An Online Visual Object Detection Platform Powered by Federated Learning, 2020, Proceedings of the AAAI Conference on Artificial Intelligence
  • Threats to Federated Learning: A Survey, 2020, arXiv (Cornell University)
  • Towards Out-Of-Distribution Generalization: A Survey, 2021, arXiv (Cornell University)

In addition to journal and conference papers, Han Yu has contributed to scholarly books, including "Federated Learning," published in 2020 by Morgan & Claypool Publishers.

Best Publications

  • Advances and Open Problems in Federated Learning

    Peter Kairouz;H. Brendan McMahan;Brendan Avent;Aurélien Bellet

  • Advances and open problems in federated learning

    Peter Kairouz;H. Brendan McMahan;Brendan Avent;Aurélien Bellet

  • Visual Domain Adaptation with Manifold Embedded Distribution Alignment

    Jindong Wang;Wenjie Feng;Yiqiang Chen;Han Yu

  • A Survey of Zero-Shot Learning: Settings, Methods, and Applications

    Wei Wang;Vincent W. Zheng;Han Yu;Chunyan Miao

  • Privacy and Robustness in Federated Learning: Attacks and Defenses.

    Lingjuan Lyu;Han Yu;Xingjun Ma;Lichao Sun

  • A Survey of Trust and Reputation Management Systems in Wireless Communications

    Han Yu;Zhiqi Shen;Chunyan Miao;Cyril Leung

  • Transfer Learning with Dynamic Distribution Adaptation

    Jindong Wang;Yiqiang Chen;Wenjie Feng;Han Yu

  • Threats to Federated Learning

    Lingjuan Lyu;Han Yu;Jun Zhao;Qiang Yang

  • FedVision: An Online Visual Object Detection Platform Powered by Federated Learning

    Yang Liu;Anbu Huang;Yun Luo;He Huang

  • A Survey of Multi-Agent Trust Management Systems

    Han Yu;Zhiqi Shen;Cyril Leung;Chunyan Miao

  • Threats to Federated Learning: A Survey

    Lingjuan Lyu;Han Yu;Qiang Yang

  • Federated Learning

    Unknown

  • Incentive Design for Efficient Federated Learning in Mobile Networks: A Contract Theory Approach

    Jiawen Kang;Zehui Xiong;Dusit Niyato;Han Yu

  • Building ethics into artificial intelligence

    Han Yu;Zhiqi Shen;Chunyan Miao;Cyril Leung;Cyril Leung

  • Towards Out-Of-Distribution Generalization: A Survey

    Zheyan Shen;Jiashuo Liu;Yue He;Xingxuan Zhang

  • Towards Fair and Privacy-Preserving Federated Deep Models

    Lingjuan Lyu;Jiangshan Yu;Karthik Nandakumar;Yitong Li

  • A Fairness-aware Incentive Scheme for Federated Learning

    Han Yu;Zelei Liu;Yang Liu;Tianjian Chen

  • Federated Learning

    Unknown

  • Collaborative Fairness in Federated Learning

    Lingjuan Lyu;Xinyi Xu;Qian Wang;Han Yu

  • A study on factors affecting service quality and loyalty intention in mobile banking

    Qingji Zhou;Qingji Zhou;Fong Jie Lim;Han Yu;Gaoqian Xu

  • Deep Model for Dropout Prediction in MOOCs

    Wei Wang;Han Yu;Chunyan Miao

  • Easy Transfer Learning By Exploiting Intra-Domain Structures

    Jindong Wang;Yiqiang Chen;Han Yu;Meiyu Huang

  • Mitigating Herding in Hierarchical Crowdsourcing Networks.

    Han Yu;Chunyan Miao;Cyril Leung;Cyril Leung;Yiqiang Chen;Yiqiang Chen

  • Towards a trust aware cognitive radio architecture

    Tao Qin;Han Yu;Cyril Leung;Zhiqi Shen

Frequent Co-Authors

Chunyan Miao
Chunyan Miao Nanyang Technological University
Zhiqi Shen
Zhiqi Shen Nanyang Technological University
Cyril Leung
Cyril Leung University of British Columbia
Qiang Yang
Qiang Yang Hong Kong University of Science and Technology
Yiqiang Chen
Yiqiang Chen Chinese Academy of Sciences
Pär Nordlund
Pär Nordlund Karolinska Institute
Jun Lin
Jun Lin Chinese Academy of Sciences
Victor Lesser
Victor Lesser University of Massachusetts Amherst
Dusit Niyato
Dusit Niyato Nanyang Technological University
Bo An
Bo An Nanyang Technological University

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