World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
47
Citations
7716
World Ranking
6573
National Ranking
2908

Tianbao Yang 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 Tianbao Yang 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: 228 publications — 56th percentile

56% of scientists in this discipline score the same or lower.

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

Tianbao Yang 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 Tianbao Yang 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: 47 D-Index — 56th percentile

56% of scientists in this discipline score the same or lower.

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

Overview

Tianbao Yang is affiliated with Texas A&M University in the United States, with a research focus primarily in computer science. Their work spans multiple subfields including artificial intelligence, computational mechanics, computer vision and pattern recognition, management science and operations research, as well as statistics and probability.

The main topics explored in their research include stochastic gradient optimization techniques, sparse and compressive sensing techniques, machine learning and algorithms, machine learning and extreme learning machines, advanced bandit algorithms research, imbalanced data classification techniques, and machine learning and data classification.

Among recent publications, notable papers authored by or involving Tianbao Yang are:

  • "AUC Maximization in the Era of Big Data and AI: A Survey" (2022, ACM Computing Surveys)
  • "Large-scale Robust Deep AUC Maximization: A New Surrogate Loss and Empirical Studies on Medical Image Classification" (2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV))
  • "Weakly-convex-concave min-max optimization: provable algorithms and applications in machine learning" (2021, Optimization methods & software)
  • "Stochastic Optimization of Areas Under Precision-Recall Curves with Provable Convergence" (2021, arXiv (Cornell University))
  • "Optimal Epoch Stochastic Gradient Descent Ascent Methods for Min-Max Optimization" (2020, arXiv (Cornell University))

Frequent coauthors collaborating with Tianbao Yang include:

  • Zhishuai Guo
  • Zhuoning Yuan
  • Qihang Lin
  • Quanqi Hu
  • Yiming Ying

Yang's work has been published mainly in the following venues:

  • arXiv (Cornell University)
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • Machine Learning
  • IEEE Transactions on Pattern Analysis and Machine Intelligence
  • ACM Computing Surveys

Best Publications

  • Combining link and content for community detection: a discriminative approach

    Tianbao Yang;Rong Jin;Yun Chi;Shenghuo Zhu

  • Detecting communities and their evolutions in dynamic social networks--a Bayesian approach

    Tianbao Yang;Yun Chi;Shenghuo Zhu;Yihong Gong

  • Hetero-ConvLSTM: A Deep Learning Approach to Traffic Accident Prediction on Heterogeneous Spatio-Temporal Data

    Zhuoning Yuan;Xun Zhou;Tianbao Yang

  • Nyström Method vs Random Fourier Features: A Theoretical and Empirical Comparison

    Tianbao Yang;Yu-feng Li;Mehrdad Mahdavi;Rong Jin

  • AUC Maximization in the Era of Big Data and AI: A Survey

    Unknown

  • Online AUC Maximization

    Peilin Zhao;Rong Jin;Tianbao Yang;Steven C. Hoi

  • Hyper-class augmented and regularized deep learning for fine-grained image classification

    Saining Xie;Tianbao Yang;Xiaoyu Wang;Yuanqing Lin

  • Learning Attributes Equals Multi-Source Domain Generalization

    Chuang Gan;Tianbao Yang;Boqing Gong

  • Trading regret for efficiency: online convex optimization with long term constraints

    Mehrdad Mahdavi;Rong Jin;Tianbao Yang

  • A Machine Learning Approach for Air Quality Prediction: Model Regularization and Optimization

    Dixian Zhu;Changjie Cai;Tianbao Yang;Xun Zhou

  • Online Optimization with Gradual Variations

    Chao Kai Chiang;Chao Kai Chiang;Tianbao Yang;Chia Jung Lee;Mehrdad Mahdavi

  • Trading Computation for Communication: Distributed Stochastic Dual Coordinate Ascent

    Tianbao Yang

  • Non-Convex Min-Max Optimization: Provable Algorithms and Applications in Machine Learning

    Hassan Rafique;Mingrui Liu;Qihang Lin;Tianbao Yang

  • Online Multiple Kernel Classification

    Steven C. Hoi;Rong Jin;Peilin Zhao;Tianbao Yang

  • Weakly-convex–concave min–max optimization: provable algorithms and applications in machine learning

    Hassan Rafique;Mingrui Liu;Qihang Lin;Tianbao Yang

  • Unified Convergence Analysis of Stochastic Momentum Methods for Convex and Non-convex Optimization

    Tianbao Yang;Qihang Lin;Zhe Li

  • First-order Stochastic Algorithms for Escaping From Saddle Points in Almost Linear Time

    Yi Xu;Rong Jin;Tianbao Yang

  • A Unified Analysis of Stochastic Momentum Methods for Deep Learning

    Yan Yan;Yan Yan;Tianbao Yang;Zhe Li;Qihang Lin

  • Semi-Crowdsourced Clustering: Generalizing Crowd Labeling by Robust Distance Metric Learning

    Jinfeng Yi;Rong Jin;Shaili Jain;Tianbao Yang

  • Online multiple kernel learning: algorithms and mistake bounds

    Rong Jin;Steven C. H. Hoi;Tianbao Yang

  • Tracking slowly moving clairvoyant: optimal dynamic regret of online learning with true and noisy gradient

    Tianbao Yang;Lijun Zhang;Rong Jin;Jinfeng Yi

  • Improved Dynamic Regret for Non-degenerate Functions

    Lijun Zhang;Tianbao Yang;Jinfeng Yi;Rong Jin

  • Dynamic Regret of Strongly Adaptive Methods

    Lijun Zhang;Tianbao Yang;Rong Jin;Zhi-Hua Zhou

Frequent Co-Authors

Rong Jin
Rong Jin Alibaba Group (China)
Yi Xu
Yi Xu Dalian University of Technology
Yan Yan
Yan Yan Illinois Institute of Technology
Zhi-Hua Zhou
Zhi-Hua Zhou Nanjing University
Jinfeng Yi
Jinfeng Yi IBM (United States)
Boqing Gong
Boqing Gong Google (United States)
Anil K. Jain
Anil K. Jain Michigan State University
Wei Liu
Wei Liu Tencent (China)
Yuanqing Lin
Yuanqing Lin Aibee Inc.

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