World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
72
Citations
21444
World Ranking
1672
National Ranking
30

Chunyan Miao 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 Chunyan Miao 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: 566 publications — 95th percentile

95% of scientists in this discipline score the same or lower.

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

Chunyan Miao 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 Chunyan Miao 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: 72 D-Index — 89th percentile

89% of scientists in this discipline score the same or lower.

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

Overview

Chunyan Miao is affiliated with Nanyang Technological University in Singapore and works primarily within the field of Computer Science. Their research encompasses a range of specialized subfields including Artificial Intelligence, Computer Vision and Pattern Recognition, Information Systems, Computer Networks and Communications, and Electrical and Electronic Engineering.

The scientist's recent research contributions are documented in publications such as:

  • Federated Learning in Mobile Edge Networks: A Comprehensive Survey, 2020, IEEE Communications Surveys & Tutorials
  • EEG-Based Emotion Recognition Using Regularized Graph Neural Networks, 2020, IEEE Transactions on Affective Computing
  • A Full Dive Into Realizing the Edge-Enabled Metaverse: Visions, Enabling Technologies, and Challenges, 2022, IEEE Communications Surveys & Tutorials
  • Semantic Communications for Future Internet: Fundamentals, Applications, and Challenges, 2022, IEEE Communications Surveys & Tutorials
  • Towards Federated Learning in UAV-Enabled Internet of Vehicles: A Multi-Dimensional Contract-Matching Approach, 2021, IEEE Transactions on Intelligent Transportation Systems

Their frequent coauthors include Dusit Niyato, Zehui Xiong, Wei Yang Bryan Lim, Cyril Leung, and Jiawen Kang.

Chunyan Miao has contributed to notable publication venues such as:

  • arXiv (Cornell University)
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • IEEE Internet of Things Journal
  • IEEE Wireless Communications
  • ICC 2022 - IEEE International Conference on Communications

A book titled Federated Learning Over Wireless Edge Networks was published in 2022 by Springer International Publishing, credited to Chunyan Miao.

Their research topics include:

  • Privacy-Preserving Technologies in Data
  • Topic Modeling
  • Recommender Systems and Techniques
  • Advanced Graph Neural Networks
  • Multimodal Machine Learning Applications
  • Blockchain Technology Applications and Security
  • IoT and Edge/Fog Computing

Best Publications

  • Federated Learning in Mobile Edge Networks: A Comprehensive Survey

    Wei Yang Bryan Lim;Nguyen Cong Luong;Dinh Thai Hoang;Yutao Jiao

  • EEG-Based Emotion Recognition Using Regularized Graph Neural Networks

    Peixiang Zhong;Di Wang;Chunyan Miao

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

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

  • Exploiting Geographical Neighborhood Characteristics for Location Recommendation

    Yong Liu;Wei Wei;Aixin Sun;Chunyan Miao

  • Neighborhood Regularized Logistic Matrix Factorization for Drug-Target Interaction Prediction.

    Yong Liu;Yong Liu;Min Wu;Chunyan Miao;Peilin Zhao

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

    Han Yu;Zhiqi Shen;Chunyan Miao;Cyril Leung

  • 3D CNN Based Automatic Diagnosis of Attention Deficit Hyperactivity Disorder Using Functional and Structural MRI

    Liang Zou;Jiannan Zheng;Chunyan Miao;Martin J. Mckeown

  • Realizing the Metaverse with Edge Intelligence: A Match Made in Heaven

    Unknown

  • Personalized point-of-interest recommendation by mining users' preference transition

    Xin Liu;Yong Liu;Karl Aberer;Chunyan Miao

  • Towards Federated Learning in UAV-Enabled Internet of Vehicles: A Multi-Dimensional Contract-Matching Approach

    Wei Yang Bryan Lim;Jianqiang Huang;Zehui Xiong;Jiawen Kang

  • Optimal Electric Vehicle Fast Charging Station Placement Based on Game Theoretical Framework

    Yanhai Xiong;Jiarui Gan;Bo An;Chunyan Miao

  • Knowledge-Enriched Transformer for Emotion Detection in Textual Conversations

    Peixiang Zhong;Di Wang;Chunyan Miao

  • A Survey of Multi-Agent Trust Management Systems

    Han Yu;Zhiqi Shen;Cyril Leung;Chunyan Miao

  • Dynamical cognitive network - an extension of fuzzy cognitive map

    Yuan Miao;Zhi-Qiang Liu;Chee Kheong Siew;Chun Yan Miao

  • Decentralized Edge Intelligence: A Dynamic Resource Allocation Framework for Hierarchical Federated Learning

    Wei Yang Bryan Lim;Jer Shyuan Ng;Zehui Xiong;Jiangming Jin

  • Building ethics into artificial intelligence

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

  • Online multimodal deep similarity learning with application to image retrieval

    Pengcheng Wu;Steven C.H. Hoi;Hao Xia;Peilin Zhao

  • Hierarchical Incentive Mechanism Design for Federated Machine Learning in Mobile Networks

    Wei Yang Bryan Lim;Zehui Xiong;Chunyan Miao;Dusit Niyato

  • Federated Learning in the Sky: Aerial-Ground Air Quality Sensing Framework With UAV Swarms

    Yi Liu;Jiangtian Nie;Xuandi Li;Syed Hassan Ahmed

  • Comparing the learning effectiveness of BP, ELM, I-ELM, and SVM for corporate credit ratings

    Haoming Zhong;Chunyan Miao;Zhiqi Shen;Yuhong Feng

  • Deep Model for Dropout Prediction in MOOCs

    Wei Wang;Han Yu;Chunyan Miao

  • Distilling Causal Effect of Data in Class-Incremental Learning

    Xinting Hu;Kaihua Tang;Chunyan Miao;Xian-Sheng Hua

Frequent Co-Authors

Zhiqi Shen
Zhiqi Shen Nanyang Technological University
Cyril Leung
Cyril Leung University of British Columbia
Han Yu
Han Yu Nanyang Technological University
Ah-Hwee Tan
Ah-Hwee Tan Singapore Management University
Bo An
Bo An Nanyang Technological University
Dusit Niyato
Dusit Niyato Nanyang Technological University
Yiqiang Chen
Yiqiang Chen Chinese Academy of Sciences
Qiang Yang
Qiang Yang Hong Kong University of Science and Technology
Zehui Xiong
Zehui Xiong Queen's University Belfast
Jun Lin
Jun Lin Chinese Academy of Sciences

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