World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
39
Citations
4238
World Ranking
9912
National Ranking
1251

Dao-Qing Dai 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 Dao-Qing Dai 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: 127 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.

Dao-Qing Dai 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 Dao-Qing Dai 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

Dao-Qing Dai is affiliated with Sun Yat-sen University in China and has contributed extensively to the fields of computer science and biochemistry, genetics, and molecular biology. Their research spans multiple interdisciplinary areas, focusing on advanced computational techniques and their applications in biological and imaging sciences.

The main fields of study covered by Dao-Qing Dai include:

  • Computer Science
  • Biochemistry, Genetics and Molecular Biology

Within these broad domains, Dao-Qing Dai's work is concentrated in key subfields such as:

  • Artificial Intelligence
  • Molecular Biology
  • Computer Vision and Pattern Recognition
  • Materials Chemistry
  • Media Technology

The research topics addressed encompass a diverse range of contemporary issues including:

  • Domain Adaptation and Few-Shot Learning
  • Machine Learning and Extreme Learning Machines (ELM)
  • Multimodal Machine Learning Applications
  • Bioinformatics and Genomic Networks
  • Gene Expression and Cancer Classification
  • Remote-Sensing Image Classification
  • Radiomics and Machine Learning in Medical Imaging

Dao-Qing Dai has coauthored publications with several researchers, with frequent collaborators being:

  • Chuan-Xian Ren
  • Weiwen Wang
  • You-Wei Luo
  • Hong Yan
  • Ke-Kun Huang

The scientist has published papers in various venues, with multiple contributions in several journals and platforms such as:

  • Briefings in Bioinformatics
  • IEEE Transactions on Pattern Analysis and Machine Intelligence
  • Pattern Recognition
  • arXiv (Cornell University)
  • The Cambridge Structural Database

Recent papers by Dao-Qing Dai include:

  • "Unsupervised Domain Adaptation via Discriminative Manifold Propagation," 2020, IEEE Transactions on Pattern Analysis and Machine Intelligence
  • "Hyperspectral Image Classification via Discriminant Gabor Ensemble Filter," 2021, IEEE Transactions on Cybernetics
  • "Hyperspectral image classification via discriminative convolutional neural network with an improved triplet loss," 2020, Pattern Recognition
  • "BuresNet: Conditional Bures Metric for Transferable Representation Learning," 2022, IEEE Transactions on Pattern Analysis and Machine Intelligence
  • "Subtype-WESLR: identifying cancer subtype with weighted ensemble sparse latent representation of multi-view data," 2021, Briefings in Bioinformatics

Best Publications

  • Human face recognition using PCA on wavelet subband

    Guo-Can Feng;Pong Chi Yuen;Dao-Qing Dai

  • Regularized discriminant analysis and its application to face recognition

    Dao Qing Dai;Pong Chi Yuen

  • Framelet Algorithms for De-Blurring Images Corrupted by Impulse Plus Gaussian Noise

    Yan-Ran Li;Lixin Shen;Dao-Qing Dai;B W Suter

  • Kernel machine-based one-parameter regularized Fisher discriminant method for face recognition

    Wen-Sheng Chen;P.C. Yuen;Jian Huang;Dao-Qing Dai

  • Coupled Kernel Embedding for Low-Resolution Face Image Recognition

    Chuan-Xian Ren;Dao-Qing Dai;Hong Yan

  • Structured Sparse Error Coding for Face Recognition With Occlusion

    Xiao-Xin Li;Dao-Qing Dai;Xiao-Fei Zhang;Chuan-Xian Ren

  • Face Recognition Using Dual-Tree Complex Wavelet Features

    Chao-Chun Liu;Dao-Qing Dai

  • Face Recognition by Regularized Discriminant Analysis

    Dao-Qing Dai;P.C. Yuen

  • Robust classification using l 2,1 -norm based regression model

    Chuan-Xian Ren;Dao-Qing Dai;Hong Yan

  • Improved discriminate analysis for high-dimensional data and its application to face recognition

    Xiao-Sheng Zhuang;Dao-Qing Dai

  • Unsupervised Domain Adaptation via Discriminative Manifold Propagation.

    You-Wei Luo;Chuan-Xian Ren;Dao-Qing Dai;Hong Yan

  • Detecting temporal protein complexes from dynamic protein-protein interaction networks.

    Le Ou-Yang;Dao-Qing Dai;Xiao-Li Li;Min Wu

  • Wavelets and Face Recognition

    Dao-Qing Dai;Hong Yan

  • Local Discriminant Wavelet Packet Coordinates for Face Recognition

    Chao-Chun Liu;Dao-Qing Dai;Hong Yan

  • Finding Correlated Biclusters from Gene Expression Data

    Wen-Hui Yang;Dao-Qing Dai;Hong Yan

  • Rapid and brief communication: Inverse Fisher discriminate criteria for small sample size problem and its application to face recognition

    Xiao-Sheng Zhuang;Dao-Qing Dai

  • Incremental learning of bidirectional principal components for face recognition

    Chuan-Xian Ren;Dao-Qing Dai

  • Learning Kernel Extended Dictionary for Face Recognition

    Ke-Kun Huang;Dao-Qing Dai;Chuan-Xian Ren;Zhao-Rong Lai

  • Determining minimum set of driver nodes in protein-protein interaction networks.

    Xiao-Fei Zhang;Le Ou-Yang;Yuan Zhu;Meng-Yun Wu

  • Multiscale Logarithm Difference Edgemaps for Face Recognition Against Varying Lighting Conditions

    Zhao-Rong Lai;Dao-Qing Dai;Chuan-Xian Ren;Ke-Kun Huang

  • Nonlocal boundary problems for a third-order one-dimensional nonlinear pseudoparabolic equation

    Dao-Qing Dai;Yu Huang

Frequent Co-Authors

Hong Yan
Hong Yan City University of Hong Kong
Pong C. Yuen
Pong C. Yuen Hong Kong Baptist University
Arye Nehorai
Arye Nehorai Washington University in St. Louis
Xiaohua Hu
Xiaohua Hu Drexel University
Jiashi Feng
Jiashi Feng ByteDance
Xiaoli Li
Xiaoli Li Singapore University of Technology and Design
Shuicheng Yan
Shuicheng Yan National University of Singapore
Zhen Lei
Zhen Lei Chinese Academy of Sciences
Guodong Guo
Guodong Guo West Virginia University
Xiaofei He
Xiaofei He Zhejiang University

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