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Rising Stars

D-Index
35
Citations
4768
World Ranking
834
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274

Computer Science

D-Index
36
Citations
5471
World Ranking
11267
National Ranking
1380

Chang Tang 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 Chang Tang 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: 72 publications — 2nd percentile

2% of scientists in this discipline score the same or lower.

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

Chang Tang 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 Chang Tang 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: 36 D-Index — 23rd percentile

23% of scientists in this discipline score the same or lower.

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

Research.com Recognitions

  • 2025 - Research.com Rising Stars Award

Overview

Chang Tang is affiliated with the China University of Geosciences in China and has an extensive publication record primarily in the fields of Computer Science and Engineering. Their research emphasizes areas such as Computer Vision and Pattern Recognition, Artificial Intelligence, and Media Technology, alongside contributions in Molecular Biology and Atmospheric Science.

The main research topics associated with Chang Tang include:

  • Face and Expression Recognition
  • Remote-Sensing Image Classification
  • Video Surveillance and Tracking Methods
  • Advanced Image and Video Retrieval Techniques
  • Remote Sensing and Land Use
  • Advanced Clustering Algorithms Research
  • Advanced Computing and Algorithms

Chang Tang has published in several key academic venues, with frequent contributions to:

  • arXiv (Cornell University)
  • IEEE Transactions on Geoscience and Remote Sensing
  • IEEE Transactions on Knowledge and Data Engineering
  • IEEE Transactions on Multimedia
  • IEEE Transactions on Neural Networks and Learning Systems

Some of the recent papers authored or co-authored by Chang Tang reflect a focus on multi-view clustering and feature selection in machine learning contexts. These include:

  • "Unified One-Step Multi-View Spectral Clustering," 2022, IEEE Transactions on Knowledge and Data Engineering
  • "Cross-View Locality Preserved Diversity and Consensus Learning for Multi-View Unsupervised Feature Selection," 2021, IEEE Transactions on Knowledge and Data Engineering
  • "Efficient and Effective Regularized Incomplete Multi-view Clustering," 2020, IEEE Transactions on Pattern Analysis and Machine Intelligence (lead author: Xinwang Liu)
  • "Consensus Graph Learning for Multi-View Clustering," 2021, IEEE Transactions on Multimedia (lead author: Zhenglai Li)
  • "A network traffic forecasting method based on SA optimized ARIMA-BP neural network," 2021, Computer Networks (lead author: Hanyu Yang)

Their frequent collaborators include researchers such as Xinwang Liu, Xiao Zheng, Wei Zhang, Zhenglai Li, and En Zhu. These co-authors have worked extensively with Chang Tang on various projects related to clustering algorithms, pattern recognition, and multi-view learning frameworks.

Best Publications

  • Action Recognition From Depth Maps Using Deep Convolutional Neural Networks

    Pichao Wang;Wanqing Li;Zhimin Gao;Jing Zhang

  • Late Fusion Incomplete Multi-View Clustering

    Xinwang Liu;Xinzhong Zhu;Miaomiao Li;Lei Wang

  • RGB-D-based action recognition datasets

    Jing Zhang;Wanqing Li;Philip O. Ogunbona;Pichao Wang

  • Consensus Graph Learning for Multi-view Clustering

    Zhenglai Li;Chang Tang;Xinwang Liu;Xiao Zheng

  • Learning a Joint Affinity Graph for Multiview Subspace Clustering

    Chang Tang;Xinzhong Zhu;Xinwang Liu;Miaomiao Li

  • Efficient and Effective Regularized Incomplete Multi-View Clustering

    Xinwang Liu;Miaomiao Li;Chang Tang;Jingyuan Xia

  • Multi-view Clustering via Late Fusion Alignment Maximization

    Siwei Wang;Xinwang Liu;En Zhu;Chang Tang

  • Cross-view Locality Preserved Diversity and Consensus Learning for Multi-view Unsupervised Feature Selection

    Chang Tang;Xiao Zheng;Xinwang Liu;Wei Zhang

  • A network traffic forecasting method based on SA optimized ARIMA–BP neural network

    Hanyu Yang;Xutao Li;Wenhao Qiang;Yuhan Zhao

  • Depth Pooling Based Large-Scale 3-D Action Recognition With Convolutional Neural Networks

    Pichao Wang;Wanqing Li;Zhimin Gao;Chang Tang

  • Scene Flow to Action Map: A New Representation for RGB-D Based Action Recognition with Convolutional Neural Networks

    Pichao Wang;Wanqing Li;Zhimin Gao;Yuyao Zhang

  • CGD: Multi-View Clustering via Cross-View Graph Diffusion

    Chang Tang;Xinwang Liu;Xinzhong Zhu;En Zhu

  • Feature Selective Projection with Low-Rank Embedding and Dual Laplacian Regularization

    Chang Tang;Xinwang Liu;Xinzhong Zhu;Jian Xiong

  • Robust unsupervised feature selection via dual self-representation and manifold regularization

    Chang Tang;Xinwang Liu;Miaomiao Li;Pichao Wang

  • ConvNets-Based Action Recognition from Depth Maps through Virtual Cameras and Pseudocoloring

    Pichao Wang;Wanqing Li;Zhimin Gao;Chang Tang

  • Unsupervised feature selection via latent representation learning and manifold regularization.

    Chang Tang;Meiru Bian;Xinwang Liu;Miaomiao Li

  • Defocus map estimation from a single image via spectrum contrast.

    Chang Tang;Chunping Hou;Zhanjie Song

  • Large-scale Isolated Gesture Recognition using Convolutional Neural Networks

    Pichao Wang;Wanqing Li;Song Liu;Zhimin Gao

  • Beyond Covariance: Feature Representation with Nonlinear Kernel Matrices

    Lei Wang;Jianjia Zhang;Luping Zhou;Chang Tang

  • Consensus learning guided multi-view unsupervised feature selection

    Chang Tang;Jiajia Chen;Xinwang Liu;Miaomiao Li

  • Depth Pooling Based Large-scale 3D Action Recognition with Convolutional Neural Networks

    Pichao Wang;Wanqing Li;Zhimin Gao;Chang Tang

Frequent Co-Authors

Xinwang Liu
Xinwang Liu National University of Defense Technology
Wanqing Li
Wanqing Li University of Wollongong
Philip Ogunbona
Philip Ogunbona University of Wollongong
Lizhe Wang
Lizhe Wang China University of Geosciences
Jiyuan Liu
Jiyuan Liu Chinese Academy of Sciences
Chunping Hou
Chunping Hou Tianjin University
Changqing Zhang
Changqing Zhang Tianjin University
Lei Wang
Lei Wang University of Wollongong
Deke Guo
Deke Guo Sun Yat-sen University
Albert Y. Zomaya
Albert Y. Zomaya University of Sydney

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