World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
42
Citations
9910
World Ranking
8247
National Ranking
1080

Yingjie Tian 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 Yingjie Tian 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: 255 publications — 64th percentile

64% of scientists in this discipline score the same or lower.

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

Yingjie Tian 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 Yingjie Tian 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: 42 D-Index — 43rd percentile

43% of scientists in this discipline score the same or lower.

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

Overview

Yingjie Tian is affiliated with the University of Chinese Academy of Sciences in China and has contributed extensively to research in computer science and engineering. Their primary areas of study include artificial intelligence, computer vision and pattern recognition, electrical and electronic engineering, civil and structural engineering, and control and systems engineering.

The main research themes covered in Tian's work include:

  • Face and Expression Recognition
  • Domain Adaptation and Few-Shot Learning
  • Machine Learning and Data Classification
  • Text and Document Classification Technologies
  • Advanced Neural Network Applications
  • Anomaly Detection Techniques and Applications
  • Energy Load and Power Forecasting

Tian has published papers in various journals and conferences, reflecting a focus on machine learning theory and applications, as well as advanced neural networks. Frequent publication venues consist of:

  • Neural Networks
  • SSRN Electronic Journal
  • arXiv (Cornell University)
  • Neurocomputing
  • Information Fusion

Recent notable publications by Yingjie Tian include:

  • A comprehensive survey on regularization strategies in machine learning, 2021, Information Fusion
  • Recent Advances in Stochastic Gradient Descent in Deep Learning, 2023, Mathematics
  • Meta-learning approaches for learning-to-learn in deep learning: A survey, 2022, Neurocomputing

Other well-cited works related to machine learning, although led by different authors, also underline the scientific context within which Tian operates, such as surveys on loss functions and applications of machine learning in waste treatment processes.

Yingjie Tian frequently collaborates with several researchers, including:

  • Saiji Fu
  • Jingjing Tang
  • Zhiquan Qi
  • Yuqi Zhang
  • Yong Shi

The breadth of Tian's publication record covers 247 works in computer science and 114 in engineering, with significant contributions oriented towards artificial intelligence and machine learning subfields. Their research intersects theoretical and applied domains, especially in face recognition, domain adaptation, and data classification.

Best Publications

  • A Comprehensive Survey of Clustering Algorithms

    Dongkuan Xu;Yingjie Tian

  • A Comprehensive Survey of Loss Functions in Machine Learning

    Qi Wang;Yue Ma;Kun Zhao;Yingjie Tian

  • Support Vector Machines: Optimization Based Theory, Algorithms, and Extensions

    Naiyang Deng;Yingjie Tian;Chunhua Zhang

  • Robust twin support vector machine for pattern classification

    Zhiquan Qi;Yingjie Tian;Yong Shi

  • A comprehensive survey on regularization strategies in machine learning

    Unknown

  • Optimization Based Data Mining: Theory and Applications

    Yong Shi;Yingjie Tian;Gang Kou;Yi Peng

  • Credit card churn forecasting by logistic regression and decision tree

    Guangli Nie;Wei Rowe;Lingling Zhang;Yingjie Tian

  • Nonparallel Support Vector Machines for Pattern Classification

    Yingjie Tian;Zhiquan Qi;Xuchan Ju;Yong Shi

  • An effective intrusion detection framework based on MCLP/SVM optimized by time-varying chaos particle swarm optimization

    Seyed Mojtaba Hosseini Bamakan;Huadong Wang;Tian Yingjie;Yong Shi

  • Laplacian twin support vector machine for semi-supervised classification

    Zhiquan Qi;Yingjie Tian;Yong Shi

  • Recent advances on support vector machines research

    Yingjie Tian;Yong Shi;Xiaohui Liu

  • Twin support vector machine with Universum data

    Zhiquan Qi;Yingjie Tian;Yong Shi

  • Structural twin support vector machine for classification

    Zhiquan Qi;Yingjie Tian;Yong Shi

  • Network Intrusion Detection

    Yong Shi;Yong Shi;Yingjie Tian;Gang Kou;Yi Peng

  • Multiview Privileged Support Vector Machines

    Jingjing Tang;Yingjie Tian;Peng Zhang;Xiaohui Liu

  • Support vector machine classifier with truncated pinball loss

    Xin Shen;Lingfeng Niu;Zhiquan Qi;Yingjie Tian

  • Joint Ranking SVM and Binary Relevance with robust Low-rank learning for multi-label classification.

    Guoqiang Wu;Ruobing Zheng;Yingjie Tian;Dalian Liu

  • Ramp loss one-class support vector machine; A robust and effective approach to anomaly detection problems

    Yingjie Tian;Mahboubeh Mirzabagheri;Seyed Mojtaba Hosseini Bamakan;Huadong Wang

  • Survey and experimental study on metric learning methods.

    Dewei Li;Yingjie Tian

  • Predicting DNA- and RNA-binding proteins from sequences with kernel methods.

    Xiaojian Shao;Yingjie Tian;Lingyun Wu;Yong Wang

  • When Ensemble Learning Meets Deep Learning

    Zhiquan Qi;Bo Wang;Yingjie Tian;Peng Zhang

Frequent Co-Authors

Yong Shi
Yong Shi Chinese Academy of Sciences
Gang Kou
Gang Kou Southwestern University of Finance and Economics
Yi Peng
Yi Peng University of Electronic Science and Technology of China
Peng Zhang
Peng Zhang Huazhong University of Science and Technology
Nai-Yang Deng
Nai-Yang Deng China Agricultural University
Xiaohui Liu
Xiaohui Liu Brunel University London
Jia Wu
Jia Wu Macquarie University
Qi Wang
Qi Wang Northwestern Polytechnical University
Panos M. Pardalos
Panos M. Pardalos University of Florida
Chengqi Zhang
Chengqi Zhang Hong Kong Polytechnic University

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