World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
51
Citations
10563
World Ranking
5334
National Ranking
159

Junbin Gao 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 Junbin Gao 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: 417 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.

Junbin Gao 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 Junbin Gao 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: 51 D-Index — 63rd percentile

63% of scientists in this discipline score the same or lower.

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

Overview

Junbin Gao is affiliated with the University of Sydney in Australia and has a research portfolio centered on computer science with a focus on artificial intelligence and its applications. Their work spans multiple subfields including artificial intelligence, computer vision and pattern recognition, statistical and nonlinear physics, signal processing, and building and construction.

The scientist has published extensively in topics related to advanced graph neural networks, face and expression recognition, traffic prediction and management techniques, complex network analysis techniques, topic modeling, domain adaptation and few-shot learning, as well as text and document classification technologies. These research topics reflect a broad engagement with contemporary challenges in machine learning and data analysis.

Frequent collaborators have included Baocai Yin, Yongli Hu, Yanfeng Sun, Andi Han, and Boyue Wang. This network of coauthors indicates active collaboration within their research community.

The venues where Gao's work has been regularly published include:

  • arXiv (Cornell University)
  • IEEE Transactions on Neural Networks and Learning Systems
  • SSRN Electronic Journal
  • Neural Networks
  • IEEE Transactions on Knowledge and Data Engineering

Notable recent papers include:

  • Optimized Graph Convolution Recurrent Neural Network for Traffic Prediction, 2020, IEEE Transactions on Intelligent Transportation Systems
  • Image Segmentation for MR Brain Tumor Detection Using Machine Learning: A Review, 2022, IEEE Reviews in Biomedical Engineering
  • Neighborhood Linear Discriminant Analysis, 2021, Pattern Recognition
  • Hierarchical Graph Convolution Network for Traffic Forecasting, 2021, Proceedings of the AAAI Conference on Artificial Intelligence
  • Dynamic Graph Convolution Network for Traffic Forecasting Based on Latent Network of Laplace Matrix Estimation, 2020, IEEE Transactions on Intelligent Transportation Systems

The volume and scope of Gao's research reflect a significant concentration on graph neural networks and their application to real-world problems such as traffic forecasting and medical image analysis. The publication venues indicate a focus on high-impact journals and conferences within computer science and engineering disciplines.

Best Publications

  • Laplacian Regularized Low-Rank Representation and Its Applications

    Ming Yin;Junbin Gao;Zhouchen Lin

  • Multiview Spectral Clustering via Structured Low-Rank Matrix Factorization

    Yang Wang;Lin Wu;Xuemin Lin;Junbin Gao

  • Image Segmentation for MR Brain Tumor Detection Using Machine Learning: A Review

    Unknown

  • Optimized Graph Convolution Recurrent Neural Network for Traffic Prediction

    Kan Guo;Yongli Hu;Zhen Qian;Hao Liu

  • A review on applications of wavelet transform techniques in chemical analysis: 1989–1997

    Alexander Kai-man Leung;Foo-tim Chau;Jun-bin Gao

  • Some Remarks on Kalman Filters for the Multisensor Fusion

    Junbin Gao;Chris J. Harris

  • Deep Attention-Based Spatially Recursive Networks for Fine-Grained Visual Recognition

    Lin Wu;Yang Wang;Xue Li;Junbin Gao

  • Where-and-When to Look: Deep Siamese Attention Networks for Video-Based Person Re-Identification

    Lin Wu;Yang Wang;Junbin Gao;Xue Li

  • Chemometrics: From Basics to Wavelet Transform

    F.T. Chau;Y.Z. Liang;Junbin Gao;X.G. Shao

  • Neighborhood Linear Discriminant Analysis

    Fa Zhu;Junbin Gao;Jian Yang;Ning Ye

  • Multiview Subspace Clustering via Tensorial t-Product Representation

    Ming Yin;Junbin Gao;Shengli Xie;Yi Guo

  • A Probabilistic Framework for SVM Regression and Error Bar Estimation

    J. B. Gao;S. R. Gunn;C. J. Harris;M. Brown

  • Hierarchical Graph Convolution Network for Traffic Forecasting

    Kan Guo;Yongli Hu;Yanfeng Sun;Sean Qian

  • Deep adaptive feature embedding with local sample distributions for person re-identification

    Lin Wu;Yang Wang;Junbin Gao;Xue Li

  • Dynamic Graph Convolution Network for Traffic Forecasting Based on Latent Network of Laplace Matrix Estimation

    Kan Guo;Yongli Hu;Zhen Qian;Yanfeng Sun

  • Sparse kernel learning with LASSO and Bayesian inference algorithm.

    Junbin Gao;Paul Wing Hing Kwan;Daming Shi

  • Dual Graph Regularized Latent Low-Rank Representation for Subspace Clustering

    Ming Yin;Junbin Gao;Zhouchen Lin;Qinfeng Shi

  • What-and-where to match: Deep spatially multiplicative integration networks for person re-identification

    Lin Wu;Yang Wang;Xue Li;Xue Li;Junbin Gao

  • Deep Learning Models for Retinal Blood Vessels Segmentation: A Review

    Toufique Ahmed Soomro;Ahmed J. Afifi;Lihong Zheng;Shafiullah Soomro

  • Subspace Clustering for Sequential Data

    Stephen Tierney;Junbin Gao;Yi Guo

  • Simulated maximum likelihood method for estimating kinetic rates in gene expression

    Tianhai Tian;Songlin Xu;Junbin Gao;Kevin Burrage

Frequent Co-Authors

Manoranjan Paul
Manoranjan Paul Charles Sturt University
Sheng Chen
Sheng Chen University of Southampton
Baocai Yin
Baocai Yin Beijing University of Technology
Jian Yang
Jian Yang University of Birmingham
Zhouchen Lin
Zhouchen Lin Peking University
Steve R. Gunn
Steve R. Gunn University of Southampton
Shengli Xie
Shengli Xie Guangdong University of Technology
Xue Li
Xue Li University of Queensland
Qinfeng Shi
Qinfeng Shi University of Adelaide
Shuicheng Yan
Shuicheng Yan National University of Singapore

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