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Computer Science
China
2025

D-Index & Metrics

Computer Science

D-Index
85
Citations
27662
World Ranking
806
National Ranking
123

Jieping Ye 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 Jieping Ye 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: 250 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: 560 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: 424 scientists 242–251 publications: 408 scientists 252–261 publications: 378 scientists 262–271 publications: 300 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: 336 publications — 80th percentile

80% of scientists in this discipline score the same or lower.

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

Jieping Ye 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 Jieping Ye sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 984 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 969 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 765 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 517 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: 85 D-Index — 94th percentile

94% 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 Computer Science in China Leader Award
  • 2020 - ACM Distinguished Member
  • 2020 - IEEE Fellow For contributions to the methodology and application of machine learning and data mining

Overview

Jieping Ye is affiliated with Alibaba Group in China and has contributed extensively to the fields of computer science and engineering. The main areas of research include artificial intelligence, computer vision and pattern recognition, transportation, automotive engineering, and building and construction.

The scholar's work spans several core research topics:

  • Transportation and Mobility Innovations
  • Transportation Planning and Optimization
  • Advanced Neural Network Applications
  • Domain Adaptation and Few-Shot Learning
  • Sharing Economy and Platforms
  • Traffic Prediction and Management Techniques
  • Topic Modeling

Published papers cover multiple influential venues, with frequent contributions to:

  • arXiv (Cornell University)
  • Transportation Research Part C Emerging Technologies
  • IEEE Transactions on Knowledge and Data Engineering
  • IEEE Transactions on Image Processing
  • bioRxiv (Cold Spring Harbor Laboratory)

Recent notable publications include:

  • "Object Detection in 20 Years: A Survey" (2023) in Proceedings of the IEEE
  • "A Review on Generative Adversarial Networks: Algorithms, Theory, and Applications" (2021) in IEEE Transactions on Knowledge and Data Engineering
  • "A Review on Generative Adversarial Networks: Algorithms, Theory, and Applications" (2020) in arXiv (Cornell University)
  • "Pricing and equilibrium in on-demand ride-pooling markets" (2020) in Transportation Research Part B Methodological
  • "An Attention-Based Graph Neural Network for Heterogeneous Structural Learning" (2020) in Proceedings of the AAAI Conference on Artificial Intelligence

The researcher has collaborated frequently with colleagues including Hongtu Zhu, Zhiwei Qin, Yuhong Guo, Shuang Qiu, and Deyi Ji.

In addition to journal articles and conference papers, Jieping Ye has authored a book titled Reinforcement Learning in the Ridesharing Marketplace, published by Morgan & Claypool Publishers in 2024.

Professional recognition includes the ACM Distinguished Member award in 2020 and the IEEE Fellow honor the same year, the latter awarded for contributions to the methodology and application of machine learning and data mining.

Best Publications

  • Tensor completion for estimating missing values in visual data

    Ji Liu;Przemyslaw Musialski;Peter Wonka;Jieping Ye

  • Fast and Accurate Matrix Completion via Truncated Nuclear Norm Regularization

    Yao Hu;Debing Zhang;Jieping Ye;Xuelong Li

  • Spatiotemporal Multi-Graph Convolution Network for Ride-Hailing Demand Forecasting

    Xu Geng;Yaguang Li;Leye Wang;Lingyu Zhang

  • Multi-task feature learning via efficient l 2, 1 -norm minimization

    Jun Liu;Shuiwang Ji;Jieping Ye

  • Generalized Low Rank Approximations of Matrices

    Unknown

  • An accelerated gradient method for trace norm minimization

    Shuiwang Ji;Jieping Ye

  • Learning brain connectivity of Alzheimer's disease by sparse inverse covariance estimation.

    Shuai Huang;Jing Li;Liang Sun;Jieping Ye

  • On Similarity Preserving Feature Selection

    Zheng Zhao;Lei Wang;Huan Liu;Jieping Ye

  • Canonical Correlation Analysis for Multilabel Classification: A Least-Squares Formulation, Extensions, and Analysis

    Liang Sun;Shuiwang Ji;Jieping Ye

  • An optimization criterion for generalized discriminant analysis on undersampled problems

    Jieping Ye;R. Janardan;C.H. Park;H. Park

  • Least squares linear discriminant analysis

    Jieping Ye

  • Robust multi-task feature learning

    Pinghua Gong;Jieping Ye;Changshui Zhang

  • A two-stage linear discriminant analysis via QR-decomposition

    Unknown

  • Efficient Methods for Overlapping Group Lasso

    Lei Yuan;Jun Liu;Jieping Ye

  • The Simpler The Better: A Unified Approach to Predicting Original Taxi Demands based on Large-Scale Online Platforms

    Yongxin Tong;Yuqiang Chen;Zimu Zhou;Lei Chen

  • Hypergraph spectral learning for multi-label classification

    Liang Sun;Shuiwang Ji;Jieping Ye

  • Integrating low-rank and group-sparse structures for robust multi-task learning

    Jianhui Chen;Jiayu Zhou;Jieping Ye

  • A multi-task learning formulation for predicting disease progression

    Jiayu Zhou;Lei Yuan;Jun Liu;Jieping Ye

  • A new optimization criterion for generalized discriminant analysis on undersampled problems

    J. Ye;Ravi Janardan;C.H. Park;H. Park

  • Multi-task Representation Learning for Travel Time Estimation

    Yaguang Li;Kun Fu;Zheng Wang;Cyrus Shahabi

  • Clustered Multi-Task Learning Via Alternating Structure Optimization

    Jiayu Zhou;Jianhui Chen;Jieping Ye

  • Discriminative K-means for Clustering

    Jieping Ye;Zheng Zhao;Mingrui Wu

  • Multi-Task Feature Learning Via Efficient l2,1-Norm Minimization

    Jun Liu;Shuiwang Ji;Jieping Ye

Frequent Co-Authors

Shuiwang Ji
Shuiwang Ji Texas A&M University
Jun Liu
Jun Liu Infinia ML (United States)
Sudhir Kumar
Sudhir Kumar Temple University
Jiayu Zhou
Jiayu Zhou Michigan State University
Paul M. Thompson
Paul M. Thompson University of Southern California
Peter Wonka
Peter Wonka King Abdullah University of Science and Technology
Ji Liu
Ji Liu Facebook (United States)
Wei Fan
Wei Fan Tencent (China)
Sethuraman Panchanathan
Sethuraman Panchanathan Arizona State University
Eric M. Reiman
Eric M. Reiman Arizona State University

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