World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
41
Citations
9034
World Ranking
8698
National Ranking
1127

Kaizhu Huang 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 Kaizhu Huang 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: 401 publications — 87th percentile

87% of scientists in this discipline score the same or lower.

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

Kaizhu Huang 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 Kaizhu Huang 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: 41 D-Index — 40th percentile

40% of scientists in this discipline score the same or lower.

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

Overview

Kaizhu Huang is affiliated with Duke Kunshan University in China and specializes in the field of Computer Science. Their research contributions span 467 publications, prominently in subfields such as Computer Vision and Pattern Recognition, Artificial Intelligence, Computer Networks and Communications, Biomedical Engineering, and Computational Mechanics.

Their work addresses multiple core topics including Domain Adaptation and Few-Shot Learning, Generative Adversarial Networks and Image Synthesis, Handwritten Text Recognition Techniques, Advanced Neural Network Applications, Adversarial Robustness in Machine Learning, Topic Modeling, and Multimodal Machine Learning Applications.

Kaizhu Huang has published extensively in well-known venues. Frequent publication outlets include:

  • arXiv (Cornell University)
  • Cognitive Computation
  • SSRN Electronic Journal
  • Neural Networks
  • Pattern Recognition

Several recent papers illustrate the breadth of Huang's research interests. These include:

  • Interpreting Black-Box Models: A Review on Explainable Artificial Intelligence, 2023, Cognitive Computation
  • Deep learning for brain age estimation: A systematic review, 2023, Information Fusion
  • EPtask: Deep Reinforcement Learning Based Energy-Efficient and Priority-Aware Task Scheduling for Dynamic Vehicular Edge Computing, 2023, IEEE Transactions on Intelligent Vehicles
  • Rethinking Data Augmentation for Single-Source Domain Generalization in Medical Image Segmentation, 2023, Proceedings of the AAAI Conference on Artificial Intelligence
  • Gradient Distribution Alignment Certificates Better Adversarial Domain Adaptation, 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV)

Collaboration has been an integral part of their scientific output. Frequent co-authors include:

  • Qiufeng Wang
  • Amir Hussain
  • Xi Yang
  • Rui Zhang
  • Yuyao Yan

Kaizhu Huang's research undertakings demonstrate a multidisciplinary approach within computer science, focusing on artificial intelligence methods, deep learning, and their applications in domains such as medical imaging and vehicular edge computing. Their scholarly contributions are distributed across a variety of collaborative partnerships and well-established academic forums.

Best Publications

  • Robust Text Detection in Natural Scene Images

    Xu-Cheng Yin;Xuwang Yin;Kaizhu Huang;Hong-Wei Hao

  • Customer churn prediction in the telecommunication sector using a rough set approach

    Adnan Amin;Sajid Anwar;Awais Adnan;Muhammad Nawaz

  • Cross-modality interactive attention network for multispectral pedestrian detection

    Lu Zhang;Zhiyong Liu;Shifeng Zhang;Xu Yang

  • Hybrid metaheuristic algorithms: past, present, and future

    T. O. Ting;Xin-She Yang;Shi Cheng;Kaizhu Huang

  • A Unified Gradient Regularization Family for Adversarial Examples

    Chunchuan Lyu;Kaizhu Huang;Hai-Ning Liang

  • Localized support vector regression for time series prediction

    Haiqin Yang;Kaizhu Huang;Irwin King;Michael R. Lyu

  • A scalable deep neural network architecture for multi-building and multi-floor indoor localization based on Wi-Fi fingerprinting

    Kyeong Soo Kim;Sanghyuk Lee;Kaizhu Huang

  • IAN: The Individual Aggregation Network for Person Search

    Jimin Xiao;Yanchun Xie;Tammam Tillo;Kaizhu Huang

  • Reliability Does Matter: An End-to-End Weakly Supervised Semantic Segmentation Approach

    Bingfeng Zhang;Jimin Xiao;Yunchao Wei;Mingjie Sun

  • Deep Learning for Brain Age Estimation: A Systematic Review

    Unknown

  • Learning classifiers from imbalanced data based on biased minimax probability machine

    Kaizhu Huang;Haiqin Yang;I. King;M.R. Lyu

  • Sparse Metric Learning via Smooth Optimization

    Yiming Ying;Kaizhu Huang;Colin Campbell

  • The Minimum Error Minimax Probability Machine

    Kaizhu Huang;Haiqin Yang;Irwin King;Michael R. Lyu

  • Fast k NN graph construction with locality sensitive hashing

    Yan-Ming Zhang;Kaizhu Huang;Guanggang Geng;Cheng-Lin Liu

  • Biased support vector machine for relevance feedback in image retrieval

    Chu-Hong Hoi;Chi-Hang Chan;Kaizhu Huang;M.R. Lyu

  • Maxi–Min Margin Machine: Learning Large Margin Classifiers Locally and Globally

    Kaizhu Huang;Haiqin Yang;I. King;M.R. Lyu

  • Sparse learning for support vector classification

    Kaizhu Huang;Danian Zheng;Jun Sun;Yoshinobu Hotta

  • Imbalanced learning with a biased minimax probability machine

    Kaizhu Huang;Haiqin Yang;Irwin King;M.R. Lyu

  • EPtask: Deep Reinforcement Learning Based Energy-Efficient and Priority-Aware Task Scheduling for Dynamic Vehicular Edge Computing

    Unknown

  • Rethinking Data Augmentation for Single-Source Domain Generalization in Medical Image Segmentation

    Unknown

  • Zero-Shot Learning via Attribute Regression and Class Prototype Rectification

    Changzhi Luo;Zhetao Li;Kaizhu Huang;Jiashi Feng

  • Learning large margin classifiers locally and globally

    Kaizhu Huang;Haiqin Yang;Irwin King;Michael R. Lyu

  • Machine Learning: Modeling Data Locally and Globally

    Kai-Zhu Huang;Hai-Qin Yang;Irwin King;Michael Lyu

Frequent Co-Authors

Amir Hussain
Amir Hussain Edinburgh Napier University
Irwin King
Irwin King Chinese University of Hong Kong
Michael R. Lyu
Michael R. Lyu Chinese University of Hong Kong
Cheng-Lin Liu
Cheng-Lin Liu Chinese Academy of Sciences
Zenglin Xu
Zenglin Xu Harbin Institute of Technology
Frans Coenen
Frans Coenen University of Liverpool
Zhiyong Liu
Zhiyong Liu University of Science and Technology Beijing
Jianke Zhu
Jianke Zhu Zhejiang University
Hong Qiao
Hong Qiao Chinese Academy of Sciences
Bo Xu
Bo Xu Chinese Academy of Sciences

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