World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
47
Citations
15146
World Ranking
6320
National Ranking
2825

Ling Huang 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 Ling Huang 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: 180 publications — 38th percentile

38% of scientists in this discipline score the same or lower.

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

Ling Huang 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 Ling Huang 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

Ling Huang is a researcher affiliated with Intel in the United States. Their body of work predominantly spans the broad fields of Computer Science and Engineering, with a strong focus on Artificial Intelligence, Computer Vision and Pattern Recognition, and Information Systems. Additional expertise includes Control and Systems Engineering and Electrical and Electronic Engineering.

The main research topics covered by Ling Huang include:

  • Recommender Systems and Techniques
  • Advanced Graph Neural Networks
  • Face and Expression Recognition
  • Topic Modeling
  • Advanced Image and Video Retrieval Techniques
  • Complex Network Analysis Techniques
  • Video Surveillance and Tracking Methods

Ling Huang has contributed extensively to academic literature, with at least 103 publications identified in Computer Science and 36 in Engineering. Frequent publication venues include:

  • arXiv (Cornell University)
  • SSRN Electronic Journal
  • IEEE Transactions on Cybernetics
  • IEEE Transactions on Industrial Informatics
  • IEEE Transactions on Neural Networks and Learning Systems

Some of the recent papers authored by or involving Ling Huang are:

  • Multi-View Clustering in Latent Embedding Space (2020), Proceedings of the AAAI Conference on Artificial Intelligence
  • Relaxed multi-view clustering in latent embedding space (2020), Information Fusion
  • An Autoencoder Framework With Attention Mechanism for Cross-Domain Recommendation (2020), IEEE Transactions on Cybernetics
  • Hybrid-Order Gated Graph Neural Network for Session-Based Recommendation (2021), IEEE Transactions on Industrial Informatics
  • Unsupervised Adversarial Instance-Level Image Retrieval (2021), IEEE Transactions on Multimedia

Ling Huang has collaborated frequently with several researchers. Notable co-authors include:

  • Chang-Dong Wang
  • Jianhuang Lai
  • Philip S. Yu
  • Yuefang Gao
  • Dong Huang

Best Publications

  • Tapestry: a resilient global-scale overlay for service deployment

    B.Y. Zhao;Ling Huang;J. Stribling;S.C. Rhea

  • Detecting Large-Scale System Problems by Mining Console Logs

    Wei Xu;Ling Huang;Armando Fox;David A. Patterson

  • Detecting large-scale system problems by mining console logs

    Wei Xu;Ling Huang;Armando Fox;David Patterson

  • Adversarial machine learning

    L Huang;AD Joseph;B Nelson;Bip Rubinstein

  • Adversarial machine learning

    Ling Huang;Anthony D. Joseph;Blaine Nelson;Benjamin I.P. Rubinstein

  • Fast approximate spectral clustering

    Donghui Yan;Ling Huang;Michael I. Jordan

  • ANTIDOTE: understanding and defending against poisoning of anomaly detectors

    Benjamin I.P. Rubinstein;Blaine Nelson;Ling Huang;Anthony D. Joseph

  • Brocade: Landmark Routing on Overlay Networks

    Ben Y. Zhao;Yitao Duan;Ling Huang;Anthony D. Joseph

  • Juxtapp: a scalable system for detecting code reuse among android applications

    Steve Hanna;Ling Huang;Edward Wu;Saung Li

  • Learning in a large function space: Privacy-preserving mechanisms for SVM learning

    Benjamin I. P. Rubinstein;Peter L. Bartlett;Ling Huang;Nina Taft

  • In-Network PCA and Anomaly Detection

    Ling Huang;Long Nguyen;Minos Garofalakis;Michael I. Jordan

  • Evolution of social-attribute networks: measurements, modeling, and implications using google+

    Neil Zhenqiang Gong;Wenchang Xu;Ling Huang;Prateek Mittal

  • Multi-View Clustering in Latent Embedding Space

    Man-Sheng Chen;Ling Huang;Chang-Dong Wang;Dong Huang

  • Online System Problem Detection by Mining Patterns of Console Logs

    Wei Xu;Ling Huang;Armando Fox;David Patterson

  • Joint Link Prediction and Attribute Inference Using a Social-Attribute Network

    Neil Zhenqiang Gong;Ameet Talwalkar;Lester Mackey;Ling Huang

  • Approximate object location and spam filtering on peer-to-peer systems

    Feng Zhou;Li Zhuang;Ben Y. Zhao;Ling Huang

  • Communication-Efficient Online Detection of Network-Wide Anomalies

    Ling Huang;Xuan Long Nguyen;M. Garofalakis;J.M. Hellerstein

  • DeepCF: A Unified Framework of Representation Learning and Matching Function Learning in Recommender System

    Zhi-Hong Deng;Ling Huang;Chang-Dong Wang;Jian-Huang Lai

  • Predicting Execution Time of Computer Programs Using Sparse Polynomial Regression

    Ling Huang;Jinzhu Jia;Bin Yu;Byung-gon Chun

  • Mining console logs for large-scale system problem detection

    Wei Xu;Ling Huang;Armando Fox;David Patterson

  • An Analysis of the Convergence of Graph Laplacians

    Daniel Ting;Ling Huang;Michael I. Jordan

  • I Know Why You Went to the Clinic: Risks and Realization of HTTPS Traffic Analysis

    Brad Miller;Ling Huang;Anthony D. Joseph;J. D. Tygar

  • Query strategies for evading convex-inducing classifiers

    Blaine Nelson;Benjamin I. P. Rubinstein;Ling Huang;Anthony D. Joseph

  • Spectral Clustering with Perturbed Data

    Ling Huang;Donghui Yan;Nina Taft;Michael I. Jordan

Frequent Co-Authors

Anthony D. Joseph
Anthony D. Joseph University of California, Berkeley
J. D. Tygar
J. D. Tygar University of California, Berkeley
Benjamin I. P. Rubinstein
Benjamin I. P. Rubinstein University of Melbourne
Nina Taft
Nina Taft Google (United States)
Michael I. Jordan
Michael I. Jordan University of California, Berkeley
Satish Rao
Satish Rao University of California, Berkeley
Armando Fox
Armando Fox University of California, Berkeley
Ben Y. Zhao
Ben Y. Zhao University of Chicago
David A. Patterson
David A. Patterson University of California, Berkeley
John Kubiatowicz
John Kubiatowicz University of California, Berkeley

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