World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
45
Citations
10013
World Ranking
7118
National Ranking
945

Ya Zhang 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 Ya Zhang 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: 214 publications — 51st percentile

51% of scientists in this discipline score the same or lower.

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

Ya Zhang 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 Ya Zhang 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: 45 D-Index — 51st percentile

51% of scientists in this discipline score the same or lower.

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

Overview

Ya Zhang is affiliated with Shanghai Jiao Tong University in China and has contributed extensively to the field of computer science, with a particular focus on artificial intelligence and its applications.

Their research spans several subfields, including:

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Molecular Biology
  • Radiology, Nuclear Medicine and Imaging
  • Biomedical Engineering

Key topics in Ya Zhang's work include:

  • Multimodal Machine Learning Applications
  • Domain Adaptation and Few-Shot Learning
  • Human Pose and Action Recognition
  • Anomaly Detection Techniques and Applications
  • Topic Modeling
  • COVID-19 diagnosis using AI
  • Advanced Neural Network Applications

Among recent papers authored or co-authored by Ya Zhang are:

  • "Symbiotic Graph Neural Networks for 3D Skeleton-Based Human Action Recognition and Motion Prediction" (2021) published in IEEE Transactions on Pattern Analysis and Machine Intelligence
  • "PMC-LLaMA: toward building open-source language models for medicine" (2024) published in Journal of the American Medical Informatics Association
  • "Attribute Restoration Framework for Anomaly Detection" (2020) published in IEEE Transactions on Multimedia
  • "GroupNet: Multiscale Hypergraph Neural Networks for Trajectory Prediction with Relational Reasoning" (2022) published in 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • "Neoadjuvant Immunotherapy for MSI-H/dMMR Locally Advanced Colorectal Cancer: New Strategies and Unveiled Opportunities" (2022) published in Frontiers in Immunology

Ya Zhang frequently publishes in the following venues:

  • arXiv (Cornell University)
  • Research Square (Research Square)
  • SSRN Electronic Journal
  • IEEE Transactions on Pattern Analysis and Machine Intelligence
  • IEEE Transactions on Multimedia

Collaborations are a significant aspect of Ya Zhang's research activity. The most frequent co-authors include:

  • Yanfeng Wang
  • Jiangchao Yao
  • Weidi Xie
  • Siheng Chen
  • Chaoyi Wu

Best Publications

  • Actional-Structural Graph Convolutional Networks for Skeleton-Based Action Recognition

    Maosen Li;Siheng Chen;Xu Chen;Ya Zhang

  • Expected reciprocal rank for graded relevance

    Olivier Chapelle;Donald Metlzer;Ya Zhang;Pierre Grinspan

  • A dynamic bayesian network click model for web search ranking

    Olivier Chapelle;Ya Zhang

  • Part-Stacked CNN for Fine-Grained Visual Categorization

    Shaoli Huang;Zhe Xu;Dacheng Tao;Ya Zhang

  • A Fourier-based Framework for Domain Generalization

    Qinwei Xu;Ruipeng Zhang;Ya Zhang;Yanfeng Wang

  • Dynamic Multiscale Graph Neural Networks for 3D Skeleton Based Human Motion Prediction

    Maosen Li;Siheng Chen;Yangheng Zhao;Ya Zhang

  • Symbiotic Graph Neural Networks for 3D Skeleton-based Human Action Recognition and Motion Prediction

    Maosen Li;Siheng Chen;Xu Chen;Ya Zhang

  • Deep feature for text-dependent speaker verification

    Yuan Liu;Yanmin Qian;Nanxin Chen;Tianfan Fu

  • Separating Style and Content for Generalized Style Transfer

    Yexun Zhang;Ya Zhang;Wenbin Cai

  • Masking: A New Perspective of Noisy Supervision

    Bo Han;Jiangchao Yao;Gang Niu;Mingyuan Zhou

  • Maximizing Expected Model Change for Active Learning in Regression

    Wenbin Cai;Ya Zhang;Jun Zhou

  • Bottom-Up Temporal Action Localization with Mutual Regularization

    Peisen Zhao;Lingxi Xie;Chen Ju;Ya Zhang

  • Attribute Restoration Framework for Anomaly Detection

    Ye Fei;Chaoqin Huang;Cao Jinkun;Maosen Li

  • Active Learning for Ranking through Expected Loss Optimization

    Bo Long;Jiang Bian;Olivier Chapelle;Ya Zhang

  • Multi-task learning for boosting with application to web search ranking

    Olivier Chapelle;Pannagadatta Shivaswamy;Srinivas Vadrevu;Kilian Weinberger

  • Deep Learning From Noisy Image Labels With Quality Embedding

    Jiangchao Yao;Jiajie Wang;Ivor W. Tsang;Ya Zhang

  • Multiscale Spatio-Temporal Graph Neural Networks for 3D Skeleton-Based Motion Prediction

    Maosen Li;Siheng Chen;Yangheng Zhao;Ya Zhang

  • Architectural Style Classification Using Multinomial Latent Logistic Regression

    Zhe Xu;Dacheng Tao;Ya Zhang;Junjie Wu

  • Knowledge distillation from multi-modal to mono-modal segmentation networks

    Minhao Hu;Matthis Maillard;Ya Zhang;Tommaso Ciceri

  • Active learning for ranking through expected loss optimization

    Bo Long;Olivier Chapelle;Ya Zhang;Yi Chang

  • Webly-Supervised Fine-Grained Visual Categorization via Deep Domain Adaptation

    Zhe Xu;Shaoli Huang;Ya Zhang;Dacheng Tao

  • Attribute Restoration Framework for Anomaly Detection

    Chaoqin Huang;Fei Ye;Jinkun Cao;Maosen Li

Frequent Co-Authors

Qi Tian
Qi Tian Huawei Technologies (China)
Ivor W. Tsang
Ivor W. Tsang Agency for Science, Technology and Research
Lingxi Xie
Lingxi Xie Huawei Technologies (China)
Hongyuan Zha
Hongyuan Zha Chinese University of Hong Kong, Shenzhen
Alan L. Yuille
Alan L. Yuille Johns Hopkins University
Dacheng Tao
Dacheng Tao Nanyang Technological University
Chris Ding
Chris Ding Chinese University of Hong Kong, Shenzhen
Wenjun Zhang
Wenjun Zhang Shanghai Jiao Tong University
Olivier Chapelle
Olivier Chapelle Google (United States)
Longbing Cao
Longbing Cao University of Technology Sydney

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