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Computer Science
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2026
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Computer Science
China
2023

D-Index & Metrics

Computer Science

D-Index
119
Citations
53153
World Ranking
152
National Ranking
89

Tong 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 Tong 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: 442 publications — 90th percentile

90% of scientists in this discipline score the same or lower.

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

Tong 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 Tong 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: 119 D-Index — 99th percentile

99% 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

  • 2026 - Research.com Computer Science in United States Leader Award
  • 2025 - Research.com Computer Science in United States Leader Award
  • 2023 - Research.com Computer Science in China Leader Award
  • 2022 - Research.com Computer Science in China Leader Award
  • 2013 - Fellow of the American Statistical Association (ASA)

Overview

Tong Zhang is affiliated with the University of Illinois at Urbana-Champaign in the United States and specializes in computer science with a focus on computer vision and pattern recognition, artificial intelligence, automotive engineering, renewable energy, sustainability, and media technology. Their work prominently covers the intersection of advanced image processing and machine learning techniques.

Their research topics include:

  • Advanced Image and Video Retrieval Techniques
  • Visual Attention and Saliency Detection
  • Topic Modeling
  • Natural Language Processing Techniques
  • Advanced Vision and Imaging
  • Advanced Neural Network Applications
  • Machine Learning and Data Classification

Tong Zhang has published extensively, with 74 publications primarily in computer science. Their frequent publication venues include:

  • arXiv (Cornell University)
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • IEEE Transactions on Pattern Analysis and Machine Intelligence
  • SSRN Electronic Journal
  • Pattern Recognition

Selected recent papers include:

  • Optimal Feature Transport for Cross-View Image Geo-Localization (2020), Proceedings of the AAAI Conference on Artificial Intelligence
  • UC-Net: Uncertainty Inspired RGB-D Saliency Detection via Conditional Variational Autoencoders (2020), arXiv (Cornell University)
  • Semi-supervised Active Salient Object Detection (2021), Pattern Recognition
  • Learning Saliency From Single Noisy Labelling: A Robust Model Fitting Perspective (2020), IEEE Transactions on Pattern Analysis and Machine Intelligence
  • RAFT: Reward rAnked FineTuning for Generative Foundation Model Alignment (2023), arXiv (Cornell University)

Frequent collaborators of Tong Zhang include:

  • Sabine Süsstrunk
  • Yuchao Dai
  • Mathieu Salzmann
  • Jipeng Zhang
  • Shizhe Diao

Tong Zhang was awarded the title of Fellow of the American Statistical Association (ASA) in 2013.

Best Publications

  • Accelerating Stochastic Gradient Descent using Predictive Variance Reduction

    Rie Johnson;Tong Zhang

  • A Framework for Learning Predictive Structures from Multiple Tasks and Unlabeled Data

    Rie Kubota Ando;Tong Zhang

  • Solving large scale linear prediction problems using stochastic gradient descent algorithms

    Tong Zhang

  • Stochastic dual coordinate ascent methods for regularized loss

    Shai Shalev-Shwartz;Tong Zhang

  • Text Mining: Predictive Methods for Analyzing Unstructured Information

    Sholom M. Weiss;Nitin Indurkhya;Tong Zhang;Fred Damerau

  • Statistical behavior and consistency of classification methods based on convex risk minimization

    Tong Zhang

  • Nonlinear Learning using Local Coordinate Coding

    Kai Yu;Tong Zhang;Yihong Gong

  • Effective Use of Word Order for Text Categorization with Convolutional Neural Networks

    Rie Johnson;Tong Zhang

  • Efficient mini-batch training for stochastic optimization

    Mu Li;Tong Zhang;Yuqiang Chen;Alexander J. Smola

  • A PROXIMAL STOCHASTIC GRADIENT METHOD WITH PROGRESSIVE VARIANCE REDUCTION

    Lin Xiao;Tong Zhang;Tong Zhang

  • Deep pyramid convolutional neural networks for text categorization

    Rie Johnson;Tong Zhang

  • Image classification using super-vector coding of local image descriptors

    Xi Zhou;Kai Yu;Tong Zhang;Thomas S. Huang

  • The Epoch-Greedy Algorithm for Multi-armed Bandits with Side Information

    John Langford;Tong Zhang

  • Named entity recognition through classifier combination

    Radu Florian;Abe Ittycheriah;Hongyan Jing;Tong Zhang

  • The Benefit of Group Sparsity

    Junzhou Huang;Tong Zhang

  • Learning with Structured Sparsity

    Junzhou Huang;Tong Zhang;Dimitris Metaxas

  • Sparse Online Learning via Truncated Gradient

    John Langford;Lihong Li;Tong Zhang

  • Analysis of Multi-stage Convex Relaxation for Sparse Regularization

    Tong Zhang

  • Spatial–Temporal Recurrent Neural Network for Emotion Recognition

    Tong Zhang;Wenming Zheng;Zhen Cui;Yuan Zong

  • Rank-One Approximation to High Order Tensors

    Tong Zhang;Gene H. Golub

  • Stochastic Gradient Descent for Non-smooth Optimization: Convergence Results and Optimal Averaging Schemes

    Ohad Shamir;Tong Zhang

  • Stochastic Dual Coordinate Ascent Methods for Regularized Loss Minimization

    Shai Shalev-Shwartz;Tong Zhang

  • Image classification using supervector coding of local image descriptors

    Xi Zhou;Kai Yu;Tong Zhang;Thomas S. Huang

Frequent Co-Authors

Daniel Hsu
Daniel Hsu Columbia University
Wei Liu
Wei Liu Tencent (China)
Han Liu
Han Liu Northwestern University
Sham M. Kakade
Sham M. Kakade Harvard University
Zhaopeng Tu
Zhaopeng Tu Tencent (China)
Ping Li
Ping Li Baidu (China)
Ji Liu
Ji Liu Facebook (United States)
John Langford
John Langford Microsoft (United States)
Shuming Shi
Shuming Shi Tencent (China)
Peilin Zhao
Peilin Zhao Tencent (China)

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