World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
43
Citations
7176
World Ranking
8027
National Ranking
1050

Bao-Gang Hu 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 Bao-Gang Hu 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: 202 publications — 47th percentile

47% of scientists in this discipline score the same or lower.

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

Bao-Gang Hu 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 Bao-Gang Hu 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: 43 D-Index — 46th percentile

46% of scientists in this discipline score the same or lower.

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

Overview

Bao-Gang Hu is affiliated with the Chinese Academy of Sciences in China and focuses research primarily within the field of Computer Science. Their work spans several subfields including Artificial Intelligence, Computer Vision and Pattern Recognition, Control and Systems Engineering, Plant Science, and Ecology, Evolution, Behavior and Systematics.

Their research covers a range of topics, notably Domain Adaptation and Few-Shot Learning, Multimodal Machine Learning Applications, Advanced Image and Video Retrieval Techniques, Machine Learning and Data Classification, Climate Change Impacts on Agriculture, Energy Load and Power Forecasting, and Solar Radiation and Photovoltaics.

Recent publications by Bao-Gang Hu include:

  • "Machine learning versus crop growth models: an ally, not a rival" (2022, AoB Plants)
  • "Two decades of research with the GreenLab model in agronomy" (2020, Annals of Botany)
  • "Revisiting Image Aesthetic Assessment via Self-Supervised Feature Learning" (2020, Proceedings of the AAAI Conference on Artificial Intelligence)
  • "Incremental Concept Learning via Online Generative Memory Recall" (2020, IEEE Transactions on Neural Networks and Learning Systems)
  • "Learning to assess visual aesthetics of food images" (2020, Computational Visual Media)

Bao-Gang Hu has published multiple works in various venues, including:

  • arXiv (Cornell University)
  • AoB Plants
  • Annals of Botany
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • IEEE Transactions on Neural Networks and Learning Systems

Collaborations have been an important element of Bao-Gang Hu's research, with frequent co-authors including Weiming Dong, Kekai Sheng, Baoyuan Wu, Mengzhen Kang, and Menglei Chai. These collaborations have contributed to the breadth and interdisciplinarity of research outputs.

Best Publications

  • Maximum Correntropy Criterion for Robust Face Recognition

    Ran He;Wei-Shi Zheng;Bao-Gang Hu

  • Analysis of direct action fuzzy PID controller structures

    G.K.I. Mann;Bao-Gang Hu;R.G. Gosine

  • New methodology for analytical and optimal design of fuzzy PID controllers

    Baogang Hu;G.K.I. Mann;R.G. Gosine

  • Robust Principal Component Analysis Based on Maximum Correntropy Criterion

    Ran He;Bao-Gang Hu;Wei-Shi Zheng;Xiang-Wei Kong

  • A systematic study of fuzzy PID controllers-function-based evaluation approach

    Bao-Gang Hu;G.K.I. Mann;R.G. Gosine

  • Robust feature extraction via information theoretic learning

    Xiao-Tong Yuan;Bao-Gang Hu

  • Constrained Clustering and Its Application to Face Clustering in Videos

    Baoyuan Wu;Yifan Zhang;Bao-Gang Hu;Qiang Ji

  • Structural Factorization of Plants to Compute Their Functional and Architectural Growth

    Paul-Henry Cournède;Meng-Zhen Kang;Amélie Mathieu;Jean-François Barczi

  • Two-Stage Nonnegative Sparse Representation for Large-Scale Face Recognition

    Ran He;Wei-Shi Zheng;Bao-Gang Hu;Xiang-Wei Kong

  • Fast Hydraulic Erosion Simulation and Visualization on GPU

    Xing Mei;Philippe Decaudin;Bao-Gang Hu

  • Nonnegative sparse coding for discriminative semi-supervised learning

    Ran He;Wei-Shi Zheng;Bao-Gang Hu;Xiang-Wei Kong

  • Multi-label learning with missing labels for image annotation and facial action unit recognition

    Baoyuan Wu;Siwei Lyu;Bao-Gang Hu;Qiang Ji

  • A regularized correntropy framework for robust pattern recognition

    Ran He;Wei-Shi Zheng;Bao-Gang Hu;Xiang-Wei Kong

  • Fast Hydraulic Erosion Simulation and Visualization on GPU

    Unknown

  • Robust support vector machines based on the rescaled hinge loss function

    Guibiao Xu;Zheng Cao;Bao-Gang Hu;Jose C. Principe

  • Two-level tuning of fuzzy PID controllers

    G.K.I. Mann;Bao-Gang Hu;R.G. Gosine

  • Attention-based Multi-Patch Aggregation for Image Aesthetic Assessment

    Kekai Sheng;Weiming Dong;Chongyang Ma;Xing Mei

  • Multi-label Learning with Missing Labels

    Baoyuan Wu;Zhilei Liu;Shangfei Wang;Bao-Gang Hu

  • Analytical study of a stochastic plant growth model: Application to the GreenLab model

    M. Z. Kang;P. H. Cournède;P. de Reffye;D. Auclair

  • Time-domain based design and analysis of new PID tuning rules

    G.K.I. Mann;B.-G. Hu;R.G. Gosine

  • Simultaneous Clustering and Tracklet Linking for Multi-face Tracking in Videos

    Baoyuan Wu;Siwei Lyu;Bao-Gang Hu;Qiang Ji

Frequent Co-Authors

Ran He
Ran He Chinese Academy of Sciences
Qiang Ji
Qiang Ji Rensselaer Polytechnic Institute
Siwei Lyu
Siwei Lyu University at Buffalo, State University of New York
Feiyue Huang
Feiyue Huang Tencent (China)
Baoyuan Wu
Baoyuan Wu Chinese University of Hong Kong, Shenzhen
Wei-Shi Zheng
Wei-Shi Zheng Sun Yat-sen University
Xiaopeng Zhang
Xiaopeng Zhang Chinese Academy of Sciences
Ep Heuvelink
Ep Heuvelink Wageningen University & Research
Jose C. Principe
Jose C. Principe University of Florida
Oliver Deussen
Oliver Deussen University of Konstanz

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