World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
57
Citations
14395
World Ranking
3802
National Ranking
507

Yu 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 Yu 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: 412 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.

Yu 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 Yu 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: 57 D-Index — 74th percentile

74% of scientists in this discipline score the same or lower.

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

Overview

Yu Zhang is affiliated with the Southern University of Science and Technology in China, focusing on research within computer science, with particular emphasis on artificial intelligence. Their scholarly output encompasses a wide range of topics including domain adaptation and few-shot learning, topic modeling, speech recognition and synthesis, natural language processing techniques, multimodal machine learning applications, quantum information and cryptography, and speech and audio processing.

The scientist has contributed extensively to the literature, with notable recent publications including:

  • A Survey on Multi-Task Learning, 2021, IEEE Transactions on Knowledge and Data Engineering
  • Google USM: Scaling Automatic Speech Recognition Beyond 100 Languages, 2023, arXiv (Cornell University)

Other significant works linked to Yu Zhang include papers published in venues such as Proceedings of the AAAI Conference on Artificial Intelligence and Frontiers in Pharmacology, demonstrating interdisciplinary engagement with machine learning applications in pharmacology and artificial intelligence.

Yu Zhang frequently collaborates with a number of researchers, the most frequent co-authors including Qiang Yang, Wenyuan Dai, Sinno Jialin Pan, Jiawei Han, and Chui-Ping Yang. These collaborations reflect sustained partnerships across several areas of computer science research.

The majority of Yu Zhang's research papers are published on arXiv (Cornell University), with additional publications appearing in Physical Review A, SSRN Electronic Journal, Proceedings of the AAAI Conference on Artificial Intelligence, and Interspeech 2022. The diverse set of publication venues illustrates a focus on both theoretical foundations and applied methodologies in computer science.

Yu Zhang has also contributed to academic publishing through book authorship, with a publication titled Transfer Learning released by Cambridge University Press in 2020.

The researcher's expertise crosses multiple subfields of study including artificial intelligence, computer vision and pattern recognition, signal processing, atomic and molecular physics and optics, as well as industrial and manufacturing engineering. This multidisciplinary approach supports wide-ranging investigations into computational techniques and their applications.

Best Publications

  • A Survey on Multi-Task Learning

    Yu Zhang;Qiang Yang

  • An Overview of Multi-task Learning

    Yu Zhang;Qiang Yang

  • A Survey on Multi-Task Learning

    Yu Zhang;Qiang Yang

  • A convex formulation for learning task relationships in multi-task learning

    Yu Zhang;Dit-Yan Yeung

  • Learning from facial aging patterns for automatic age estimation

    Xin Geng;Zhi-Hua Zhou;Yu Zhang;Gang Li

  • Comprehensive Learning Particle Swarm Optimization Algorithm With Local Search for Multimodal Functions

    Yulian Cao;Han Zhang;Wenfeng Li;Mengchu Zhou

  • CoNet: Collaborative Cross Networks for Cross-Domain Recommendation

    Guangneng Hu;Yu Zhang;Qiang Yang

  • Plan explanations as model reconciliation: Moving beyond explanation as soliloquy

    Tathagata Chakraborti;Sarath Sreedharan;Yu Zhang;Subbarao Kambhampati

  • Multi-task warped Gaussian process for personalized age estimation

    Yu Zhang;Dit-Yan Yeung

  • Overlapping community detection via bounded nonnegative matrix tri-factorization

    Yu Zhang;Dit-Yan Yeung

  • Transfer Learning

    Unknown

  • End-to-end adversarial memory network for cross-domain sentiment classification

    Zheng Li;Yu Zhang;Ying Wei;Yuxiang Wu

  • Differentially Private High-Dimensional Data Publication via Sampling-Based Inference

    Rui Chen;Qian Xiao;Yu Zhang;Jianliang Xu

  • Multi-domain collaborative filtering

    Yu Zhang;Bin Cao;Dit-Yan Yeung

  • Google USM: Scaling Automatic Speech Recognition Beyond 100 Languages

    Unknown

  • Hierarchical Attention Transfer Network for Cross-domain Sentiment Classification

    Zheng Li;Ying Wei;Yu Zhang;Qiang Yang

  • Plan explicability and predictability for robot task planning

    Yu Zhang;Sarath Sreedharan;Anagha Kulkarni;Tathagata Chakraborti

  • Deep neural networks for high dimension, low sample size data

    Bo Liu;Ying Wei;Yu Zhang;Qiang Yang

  • Adaptive transfer learning

    Bin Cao;Sinno Jialin Pan;Yu Zhang;Dit-Yan Yeung

  • Knowledge Distillation from Internal Representations

    Unknown

  • Distant Domain Transfer Learning

    Unknown

  • Distant Domain Transfer Learning

    Ben Tan;Yu Zhang;Sinno Jialin Pan;Qiang Yang

  • A Regularization Approach to Learning Task Relationships in Multitask Learning

    Yu Zhang;Dit-Yan Yeung

  • Transfer metric learning by learning task relationships

    Yu Zhang;Dit-Yan Yeung

  • Flexible End-to-End Dialogue System for Knowledge Grounded Conversation

    Wenya Zhu;Kaixiang Mo;Yu Zhang;Zhangbin Zhu

  • Interactive Attention Transfer Network for Cross-Domain Sentiment Classification.

    Kai Zhang;Hefu Zhang;Qi Liu;Hongke Zhao

  • Transfer Learning via Learning to Transfer.

    Ying Wei;Yu Zhang;Junzhou Huang;Qiang Yang

  • Transfer Learning via Learning to Transfer: Supplementary Material

    Ying Wei;Yu Zhang;Junzhou Huang;Qiang Yang

Frequent Co-Authors

Qiang Yang
Qiang Yang Hong Kong University of Science and Technology
Dit-Yan Yeung
Dit-Yan Yeung Hong Kong University of Science and Technology
Sinno Jialin Pan
Sinno Jialin Pan Chinese University of Hong Kong
Yunming Ye
Yunming Ye Harbin Institute of Technology
Xiang Zhang
Xiang Zhang University of Hong Kong
James T. Kwok
James T. Kwok Hong Kong University of Science and Technology
Xuan Song
Xuan Song University of Tokyo
Ruigang Yang
Ruigang Yang University of Kentucky
Dinesh Manocha
Dinesh Manocha University of Maryland, College Park
Tong Zhang
Tong Zhang University of Illinois at Urbana-Champaign

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