World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
50
Citations
33529
World Ranking
5457
National Ranking
2489

Mikhail Belkin 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 Mikhail Belkin 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: 128 publications — 18th percentile

18% of scientists in this discipline score the same or lower.

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

Mikhail Belkin 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 Mikhail Belkin 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: 50 D-Index — 62nd percentile

62% 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

  • 2018 - SPIE Fellow

Overview

Mikhail Belkin is affiliated with the University of California, San Diego in the United States. Their research spans primarily the fields of Computer Science and Engineering, with a particular focus on several key subfields including Artificial Intelligence, Electrical and Electronic Engineering, Computer Vision and Pattern Recognition, Computational Mechanics, and Statistics and Probability.

The scientist's work covers critical topics such as Neural Networks and Applications, Advanced Photonic Communication Systems, Stochastic Gradient Optimization Techniques, Machine Learning and Data Classification, Optical Network Technologies, Sparse and Compressive Sensing Techniques, and Model Reduction and Neural Networks.

Among Mikhail Belkin's recent publications are:

  • Loss landscapes and optimization in over-parameterized non-linear systems and neural networks (2022, Applied and Computational Harmonic Analysis)
  • Evaluation of Neural Architectures Trained with Square Loss vs Cross-Entropy in Classification Tasks (2020, arXiv (Cornell University))
  • Classification vs regression in overparameterized regimes: Does the loss function matter? (2020, arXiv (Cornell University))
  • Mechanism for feature learning in neural networks and backpropagation-free machine learning models (2024, Science)
  • On the linearity of large non-linear models: when and why the tangent kernel is constant (2020, arXiv (Cornell University))

Frequent co-authors collaborating with Mikhail Belkin include Adityanarayanan Radhakrishnan, Parthe Pandit, А. С. Сигов, Daniel Beaglehole, and Dmitriy Fofanov.

The most common publication venues for their work include:

  • arXiv (Cornell University)
  • Proceedings of the National Academy of Sciences
  • Nano- i Mikrosistemnaya Tehnika
  • SIAM Journal on Mathematics of Data Science
  • Applied and Computational Harmonic Analysis

Mikhail Belkin has been recognized as a SPIE Fellow since 2018, reflecting involvement in fields overlapping optics and photonics among other areas.

Best Publications

  • Laplacian Eigenmaps for dimensionality reduction and data representation

    Mikhail Belkin;Partha Niyogi

  • Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering

    Mikhail Belkin;Partha Niyogi

  • Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples

    Mikhail Belkin;Partha Niyogi;Vikas Sindhwani

  • Reconciling modern machine-learning practice and the classical bias-variance trade-off.

    Mikhail Belkin;Daniel Hsu;Siyuan Ma;Soumik Mandal

  • Semi-Supervised Learning on Riemannian Manifolds

    Mikhail Belkin;Partha Niyogi

  • Towards a theoretical foundation for Laplacian-based manifold methods

    Mikhail Belkin;Partha Niyogi

  • Regularization and Semi-supervised Learning on Large Graphs

    Mikhail Belkin;Irina Matveeva;Partha Niyogi

  • Consistency of spectral clustering

    U von Luxburg;M Belkin;O Bousquet

  • Beyond the point cloud: from transductive to semi-supervised learning

    Vikas Sindhwani;Partha Niyogi;Mikhail Belkin

  • A Co-Regularization Approach to Semi-supervised Learning with Multiple Views

    Vikas Sindhwani;Partha Niyogi;Mikhail Belkin

  • Laplacian Support Vector Machines Trained in the Primal

    Stefano Melacci;Mikhail Belkin

  • Convergence of Laplacian Eigenmaps

    Mikhail Belkin;Partha Niyogi

  • Two Models of Double Descent for Weak Features

    Mikhail Belkin;Daniel Hsu;Ji Xu

  • Semi-Supervised Learning

    Xueyuan Zhou;Mikhail Belkin

  • Consistency of spectral clustering

    Ulrike von Luxburg;Mikhail Belkin;Olivier Bousquet

  • Discrete laplace operator on meshed surfaces

    Mikhail Belkin;Jian Sun;Yusu Wang

  • Polynomial Learning of Distribution Families

    Mikhail Belkin;Kaushik Sinha

  • Loss landscapes and optimization in over-parameterized non-linear systems and neural networks

    Unknown

  • To understand deep learning we need to understand kernel learning

    Mikhail Belkin;Siyuan Ma;Soumik Mandal

  • On Learning with Integral Operators

    Lorenzo Rosasco;Mikhail Belkin;Ernesto De Vito

  • Using manifold structure for partially labelled classification

    Mikhail Belkin;Partha Niyogi

Frequent Co-Authors

Partha Niyogi
Partha Niyogi University of Chicago
Yusu Wang
Yusu Wang University of California, San Diego
Daniel Hsu
Daniel Hsu Columbia University
Olivier Bousquet
Olivier Bousquet Google (United States)
Bin Yu
Bin Yu University of California, Berkeley
Vikas Sindhwani
Vikas Sindhwani Google (United States)
Ulrike von Luxburg
Ulrike von Luxburg University of Tübingen
Dong Xuan
Dong Xuan The Ohio State University

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