World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
38
Citations
5450
World Ranking
10329
National Ranking
1278

Ping Li 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 Ping Li 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: 145 publications — 25th percentile

25% of scientists in this discipline score the same or lower.

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

Ping Li 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 Ping Li 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: 38 D-Index — 30th percentile

30% 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

  • 2010 - IEEE Fellow For contributions to iterative signal processing, multi-user detection and concatenated error control codes

Overview

Ping Li is affiliated with Baidu (China) and has contributed extensively to research in Computer Science, with a focus on subfields such as Computer Vision and Pattern Recognition, Artificial Intelligence, Molecular Biology, Signal Processing, and Ecology. Their research output reflects significant involvement in areas related to image and video processing, machine learning, and multimodal data analysis.

Their recent publications span a range of topics and appeared in notable venues. Among these are:

  • S2-MLP: Spatial-Shift MLP Architecture for Vision (2022), published in the 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)
  • Exploring global diverse attention via pairwise temporal relation for video summarization (2020), published in Pattern Recognition
  • Impact of carbon tax on enterprise operation and production strategy for low-carbon products in a co-opetition supply chain (2020), published in Journal of Cleaner Production
  • Distributed Hierarchical GPU Parameter Server for Massive Scale Deep Learning Ads Systems (2020), published in arXiv (Cornell University)
  • Peroxisomal oxidation of erucic acid suppresses mitochondrial fatty acid oxidation by stimulating malonyl-CoA formation in the rat liver (2020), published in Journal of Biological Chemistry

Frequent coauthors include:

  • Xianghua Xu
  • Xin Zhou
  • Tao Wang
  • Tan Yu
  • Xiaoyun Li

Their work has been prominently published in venues such as:

  • arXiv (Cornell University)
  • SSRN Electronic Journal
  • Pattern Recognition
  • Neurocomputing
  • Proceedings of the AAAI Conference on Artificial Intelligence

Research topics they have addressed include:

  • Advanced Image and Video Retrieval Techniques
  • Multimodal Machine Learning Applications
  • Domain Adaptation and Few-Shot Learning
  • Video Analysis and Summarization
  • Anomaly Detection Techniques and Applications
  • Human Pose and Action Recognition
  • Face and Expression Recognition

Ping Li has been recognized as an IEEE Fellow since 2010 for contributions to iterative signal processing, multi-user detection, and concatenated error control codes.

Best Publications

  • Very sparse random projections

    Ping Li;Trevor J. Hastie;Kenneth W. Church

  • McRank: Learning to Rank Using Multiple Classification and Gradient Boosting

    Ping Li;Qiang Wu;Christopher J. Burges

  • Asymmetric LSH (ALSH) for Sublinear Time Maximum Inner Product Search (MIPS)

    Anshumali Shrivastava;Ping Li

  • b-Bit minwise hashing

    Ping Li;Christian König

  • Robust logitboost and adaptive base class (ABC) logitboost

    Ping Li

  • Hashing Algorithms for Large-Scale Learning

    Ping Li;Anshumali Shrivastava;Joshua L. Moore;Arnd C. König

  • Theory and applications of b-bit minwise hashing

    Ping Li;Arnd Christian König

  • One Permutation Hashing

    Ping Li;Art Owen;Cun-hui Zhang

  • Recovery of sparse signals via generalized orthogonal matching pursuit: A new analysis

    Jian Wang;Suhyuk Kwon;Ping Li;Byonghyo Shim

  • Cross-Domain Person Reidentification Using Domain Adaptation Ranking SVMs

    Andy J. Ma;Jiawei Li;Pong C. Yuen;Ping Li

  • Densifying One Permutation Hashing via Rotation for Fast Near Neighbor Search

    Anshumali Shrivastava;Ping Li

  • In Defense of MinHash Over SimHash

    Anshumali Shrivastava;Ping Li

  • Efficient document clustering via online nonnegative matrix factorizations

    Fei Wang;Ping Li;Arnd Christian König

  • Asymmetric Minwise Hashing for Indexing Binary Inner Products and Set Containment

    Anshumali Shrivastava;Ping Li

  • Improved asymmetric locality sensitive hashing (ALSH) for Maximum Inner Product Search (MIPS)

    Anshumali Shrivastava;Ping Li

  • Improving random projections using marginal information

    Ping Li;Trevor J. Hastie;Kenneth W. Church

  • Recovery of Sparse Signals Using Multiple Orthogonal Least Squares

    Jian Wang;Ping Li

  • 0-Bit Consistent Weighted Sampling

    Ping Li

  • Learning to Rank Using Classification and Gradient Boosting

    Ping Li;Chris J.C. Burges;Qiang Wu

  • Nonlinear Estimators and Tail Bounds for Dimension Reduction in l 1 Using Cauchy Random Projections

    Ping Li;Trevor J. Hastie;Kenneth W. Church

  • b-Bit Minwise Hashing

    Ping Li;Arnd Christian Konig

  • Nonlinear Estimators and Tail Bounds for Dimension Reduction in $l_1$ Using Cauchy Random Projections

    Ping Li;Trevor J. Hastie;Kenneth W. Church

Frequent Co-Authors

Tong Zhang
Tong Zhang University of Illinois at Urbana-Champaign
Kenneth Church
Kenneth Church Baidu (China)
Huan Xu
Huan Xu Alibaba Group (China)
Trevor Hastie
Trevor Hastie Stanford University
Cun-Hui Zhang
Cun-Hui Zhang Rutgers, The State University of New Jersey
Peilin Zhao
Peilin Zhao Tencent (China)
Michael Mitzenmacher
Michael Mitzenmacher Harvard University
Guangcan Liu
Guangcan Liu Southeast University
John E. Hopcroft
John E. Hopcroft Cornell University
Matthias Hein
Matthias Hein University of Tübingen

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