World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
79
Citations
24133
World Ranking
1147
National Ranking
609

Heng 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 Heng 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: 471 publications — 92nd percentile

92% of scientists in this discipline score the same or lower.

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

Heng 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 Heng 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: 79 D-Index — 92nd percentile

92% 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

  • 2020 - Fellow of the Indian National Academy of Engineering (INAE)

Overview

Heng Huang is affiliated with the University of Pittsburgh in the United States and has produced a significant body of work primarily in the field of computer science. Their research contributions span several subfields, including artificial intelligence, computer vision and pattern recognition, molecular biology, cognitive neuroscience, and radiology, nuclear medicine, and imaging.

The scientist's main topics of research focus on stochastic gradient optimization techniques, domain adaptation and few-shot learning, privacy-preserving technologies in data, advanced neural network applications, machine learning and extreme learning machines (ELM), sparse and compressive sensing techniques, as well as functional brain connectivity studies.

Heng Huang has published extensively in prominent venues, with frequent publications appearing in:

  • arXiv (Cornell University)
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • IEEE Transactions on Neural Networks and Learning Systems
  • SSRN Electronic Journal
  • bioRxiv (Cold Spring Harbor Laboratory)

Some recent publications include:

  • Low-Rank Matrix Recovery via Efficient Schatten p-Norm Minimization (2021), Proceedings of the AAAI Conference on Artificial Intelligence
  • Privacy-Preserving Asynchronous Vertical Federated Learning Algorithms for Multiparty Collaborative Learning (2021), IEEE Transactions on Neural Networks and Learning Systems
  • Deep-learning-based prediction of late age-related macular degeneration progression (2020), Nature Machine Intelligence
  • BREM-SC: a bayesian random effects mixture model for joint clustering single cell multi-omics data (2020), Nucleic Acids Research
  • Wavelet-Based Dual Recursive Network for Image Super-Resolution (2020), IEEE Transactions on Neural Networks and Learning Systems

Frequent collaborators of Heng Huang include Bin Gu, Paul M. Thompson, Weidong Cai, Liang Zhan, and Cheng Deng.

In recognition of their contributions to engineering and science, Heng Huang was awarded the title of Fellow of the Indian National Academy of Engineering (INAE) in 2020.

Best Publications

  • Efficient and Robust Feature Selection via Joint ℓ2,1-Norms Minimization

    Feiping Nie;Heng Huang;Xiao Cai;Chris H. Ding

  • Clustering and projected clustering with adaptive neighbors

    Feiping Nie;Xiaoqian Wang;Heng Huang

  • The Constrained Laplacian Rank algorithm for graph-based clustering

    Feiping Nie;Xiaoqian Wang;Michael I. Jordan;Heng Huang

  • Deep Clustering via Joint Convolutional Autoencoder Embedding and Relative Entropy Minimization

    Kamran Ghasedi Dizaji;Amirhossein Herandi;Cheng Deng;Weidong Cai

  • Multi-view Subspace Clustering

    Hongchang Gao;Feiping Nie;Xuelong Li;Heng Huang

  • Large-scale multi-view spectral clustering via bipartite graph

    Yeqing Li;Feiping Nie;Heng Huang;Junzhou Huang

  • Using Smart Meter Data to Improve the Accuracy of Intraday Load Forecasting Considering Customer Behavior Similarities

    Franklin L. Quilumba;Wei-Jen Lee;Heng Huang;David Yanshi Wang

  • Multi-view K-means clustering on big data

    Xiao Cai;Feiping Nie;Heng Huang

  • Deep Learning-Based Image Segmentation on Multimodal Medical Imaging

    Zhe Guo;Xiang Li;Heng Huang;Ning Guo

  • Robust nonnegative matrix factorization using L21-norm

    Deguang Kong;Chris Ding;Heng Huang

  • Low-rank matrix recovery via efficient schatten p-norm minimization

    Feiping Nie;Heng Huang;Chris Ding

  • Multi-View Clustering and Feature Learning via Structured Sparsity

    Hua Wang;Feiping Nie;Heng Huang

  • Heterogeneous image feature integration via multi-modal spectral clustering

    Xiao Cai;Feiping Nie;Heng Huang;Farhad Kamangar

  • Heterogeneous Memory Enhanced Multimodal Attention Model for Video Question Answering

    Chenyou Fan;Xiaofan Zhang;Shu Zhang;Wensheng Wang

  • Deep Attributed Network Embedding

    Hongchang Gao;Heng Huang

  • A convex formulation for semi-supervised multi-label feature selection

    Xiaojun Chang;Feiping Nie;Yi Yang;Heng Huang

  • Robust Manifold Nonnegative Matrix Factorization

    Jin Huang;Feiping Nie;Heng Huang;Chris Ding

  • Identifying quantitative trait loci via group-sparse multitask regression and feature selection

    Hua Wang;Feiping Nie;Heng Huang;Sungeun Kim

  • Robust principal component analysis with non-greedy l 1 -norm maximization

    Feiping Nie;Heng Huang;Chris Ding;Dijun Luo

  • Optimal Mean Robust Principal Component Analysis

    Feiping Nie;Jianjun Yuan;Heng Huang

Frequent Co-Authors

Feiping Nie
Feiping Nie Northwestern Polytechnical University
Li Shen
Li Shen University of Pennsylvania
Chris Ding
Chris Ding Chinese University of Hong Kong, Shenzhen
Hua Wang
Hua Wang Victoria University
Weidong Cai
Weidong Cai University of Sydney
Yang Song
Yang Song California Institute of Technology
Andrew J. Saykin
Andrew J. Saykin Indiana University
Fillia Makedon
Fillia Makedon The University of Texas at Arlington
Cheng Deng
Cheng Deng Xidian University
Dinggang Shen
Dinggang Shen ShanghaiTech University

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