World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
55
Citations
11018
World Ranking
4352
National Ranking
2035

Quanquan Gu 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 Quanquan Gu 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: 247 publications — 62nd percentile

62% of scientists in this discipline score the same or lower.

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

Quanquan Gu 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 Quanquan Gu 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: 55 D-Index — 71st percentile

71% of scientists in this discipline score the same or lower.

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

Overview

Quanquan Gu is affiliated with the University of California, Los Angeles in the United States. Their primary field of research is Computer Science, with a strong focus on several key subfields, including Artificial Intelligence, Management Science and Operations Research, Computer Vision and Pattern Recognition, Molecular Biology, and Computer Networks and Communications.

The scientist's work spans a range of prominent topics within these domains. These include Advanced Bandit Algorithms Research, Reinforcement Learning in Robotics, Adversarial Robustness in Machine Learning, Stochastic Gradient Optimization Techniques, Machine Learning and Algorithms, Machine Learning and Extreme Learning Machines (ELM), and Advanced Neural Network Applications.

Quanquan Gu has contributed extensively to academic literature, with a substantial number of publications appearing in notable venues. The most frequent publication platforms include arXiv (Cornell University), bioRxiv (Cold Spring Harbor Laboratory), the Proceedings of the AAAI Conference on Artificial Intelligence, the Proceedings of the National Academy of Sciences, and Nature Communications.

Several recent papers highlight the range and focus of their work:

  • "Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States," 2022, Proceedings of the National Academy of Sciences
  • "On the Convergence and Robustness of Adversarial Training," 2021, arXiv (Cornell University)
  • "Ensemble Forecasts of Coronavirus Disease 2019 (COVID-19) in the U.S.," 2020, bioRxiv (Cold Spring Harbor Laboratory)
  • "Epidemic Model Guided Machine Learning for COVID-19 Forecasts in the United States," 2020, bioRxiv (Cold Spring Harbor Laboratory)
  • "A pre-registered short-term forecasting study of COVID-19 in Germany and Poland during the second wave," 2021, Nature Communications

Frequent collaborators in Quanquan Gu's research include Dongruo Zhou, Difan Zou, Weitong Zhang, Zixiang Chen, and Jinghui Chen. These co-authors have worked closely on numerous projects, with collaboration counts ranging from sixteen to over thirty joint works.

Best Publications

  • Personalized entity recommendation: a heterogeneous information network approach

    Xiao Yu;Xiang Ren;Yizhou Sun;Quanquan Gu

  • Generalized Fisher score for feature selection

    Quanquan Gu;Zhenhui Li;Jiawei Han

  • Gradient descent optimizes over-parameterized deep ReLU networks

    Difan Zou;Yuan Cao;Dongruo Zhou;Quanquan Gu

  • Stochastic Gradient Descent Optimizes Over-parameterized Deep ReLU Networks

    Difan Zou;Yuan Cao;Dongruo Zhou;Quanquan Gu

  • Collaborative filtering: Weighted nonnegative matrix factorization incorporating user and item graphs

    Quanquan Gu;Jie Zhou;Chris H. Q. Ding

  • Improving Adversarial Robustness Requires Revisiting Misclassified Examples

    Yisen Wang;Difan Zou;Jinfeng Yi;James Bailey

  • Co-clustering on manifolds

    Quanquan Gu;Jie Zhou

  • Recommendation in heterogeneous information networks with implicit user feedback

    Xiao Yu;Xiang Ren;Yizhou Sun;Bradley Sturt

  • Joint feature selection and subspace learning

    Quanquan Gu;Zhenhui Li;Jiawei Han

  • Generalization Bounds of Stochastic Gradient Descent for Wide and Deep Neural Networks

    Yuan Cao;Quanquan Gu

  • Ensemble Forecasts of Coronavirus Disease 2019 (COVID-19) in the U.S.

    Evan L Ray;Nutcha Wattanachit;Jarad Niemi;Abdul Hannan Kanji

  • On the convergence and robustness of adversarial training

    Yisen Wang;Xingjun Ma;James Bailey;Jinfeng Yi

  • Learning the Shared Subspace for Multi-task Clustering and Transductive Transfer Classification

    Quanquan Gu;Jie Zhou

  • Closing the generalization gap of adaptive gradient methods in training deep neural networks

    Jinghui Chen;Dongruo Zhou;Yiqi Tang;Ziyan Yang

  • Citation Prediction in Heterogeneous Bibliographic Networks.

    Xiao Yu;Quanquan Gu;Mianwei Zhou;Jiawei Han

  • ClusCite: effective citation recommendation by information network-based clustering

    Xiang Ren;Jialu Liu;Xiao Yu;Urvashi Khandelwal

  • Towards Understanding the Spectral Bias of Deep Learning

    Yuan Cao;Zhiying Fang;Yue Wu;Ding-Xuan Zhou

  • Is neuron coverage a meaningful measure for testing deep neural networks

    Fabrice Harel-Canada;Lingxiao Wang;Muhammad Ali Gulzar;Quanquan Gu

  • Clustered Support Vector Machines

    Quanquan Gu;Jiawei Han

  • Stochastic Nested Variance Reduction for Nonconvex Optimization

    Dongruo Zhou;Pan Xu;Quanquan Gu

  • Distributed Learning without Distress: Privacy-Preserving Empirical Risk Minimization

    Bargav Jayaraman;Lingxiao Wang;David Evans;Quanquan Gu

  • Active Learning: A Survey

    Charu C. Aggarwal;Xiangnan Kong;Quanquan Gu;Jiawei Han

  • Generalization error bounds of gradient descent for learning over-parameterized deep relu networks

    Yuan Cao;Quanquan Gu

  • Layer-Dependent Importance Sampling for Training Deep and Large Graph Convolutional Networks

    Difan Zou;Ziniu Hu;Yewen Wang;Song Jiang

  • Global Convergence of Langevin Dynamics Based Algorithms for Nonconvex Optimization

    Pan Xu;Jinghui Chen;Quanquan Gu

Frequent Co-Authors

Jiawei Han
Jiawei Han University of Illinois at Urbana-Champaign
Jie Zhou
Jie Zhou Tsinghua University
Han Liu
Han Liu Northwestern University
Zhenhui Li
Zhenhui Li Pennsylvania State University
David Evans
David Evans University of Virginia
Stanley Osher
Stanley Osher University of California, Los Angeles
Lihong Li
Lihong Li Amazon (United States)
Tong Zhang
Tong Zhang University of Illinois at Urbana-Champaign
Aleksandra B. Djurišić
Aleksandra B. Djurišić University of Hong Kong
W. Skorupa
W. Skorupa Helmholtz-Zentrum Dresden-Rossendorf

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