World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
47
Citations
32520
World Ranking
6275
National Ranking
2805

Joan Bruna 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 Joan Bruna 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: 250 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: 560 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: 424 scientists 242–251 publications: 408 scientists 252–261 publications: 378 scientists 262–271 publications: 300 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: 148 publications — 26th percentile

26% of scientists in this discipline score the same or lower.

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

Joan Bruna 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 Joan Bruna sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 984 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 969 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 765 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 517 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

Joan Bruna is affiliated with New York University in the United States. Their primary field of study is Computer Science, with a specialized focus on several subfields including Artificial Intelligence, Computer Vision and Pattern Recognition, Statistical and Nonlinear Physics, Statistics and Probability, and Computational Mechanics.

The scientist's research covers a variety of topics, including:

  • Model Reduction and Neural Networks
  • Neural Networks and Applications
  • Generative Adversarial Networks and Image Synthesis
  • Stochastic Gradient Optimization Techniques
  • Machine Learning and Algorithms
  • Advanced Graph Neural Networks
  • Gaussian Processes and Bayesian Inference

Joan Bruna has contributed to numerous scientific publications, frequently publishing in venues such as:

  • arXiv (Cornell University)
  • Leibniz-Zentrum für Informatik (Schloss Dagstuhl)
  • IEEE Transactions on Signal Processing
  • Monthly Notices of the Royal Astronomical Society
  • 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Some recent papers authored or co-authored by Joan Bruna include:

  • Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges, 2021, arXiv (Cornell University)
  • Can We Trust AI-Powered Real-Time Embedded Systems? (Invited Paper), 2022, Leibniz-Zentrum für Informatik (Schloss Dagstuhl)
  • Stability Properties of Graph Neural Networks, 2020, IEEE Transactions on Signal Processing
  • A new approach to observational cosmology using the scattering transform, 2020, Monthly Notices of the Royal Astronomical Society
  • Neural Fields as Learnable Kernels for 3D Reconstruction, 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

The scientist has collaborated frequently with several co-authors, including:

  • Alberto Bietti
  • Eric Vanden-Eijnden
  • Aaron Zweig
  • Gitta Kutyniok
  • Carles Domingo-Enrich

Joan Bruna has also contributed to academic literature as an author of books, notably publishing a book with Cambridge University Press titled Mathematical Aspects of Deep Learning in 2022.

Best Publications

  • Intriguing properties of neural networks

    Christian Szegedy;Wojciech Zaremba;Ilya Sutskever;Joan Bruna

  • Spectral Networks and Locally Connected Networks on Graphs

    Joan Bruna;Wojciech Zaremba;Arthur Szlam;Yann LeCun

  • Geometric Deep Learning: Going beyond Euclidean data

    Michael M. Bronstein;Joan Bruna;Yann LeCun;Arthur Szlam

  • Invariant Scattering Convolution Networks

    J. Bruna;S. Mallat

  • Deep Convolutional Networks on Graph-Structured Data.

    Mikael Henaff;Joan Bruna;Yann LeCun

  • Exploiting Linear Structure Within Convolutional Networks for Efficient Evaluation

    Emily L Denton;Wojciech Zaremba;Joan Bruna;Yann LeCun

  • Few-Shot Learning with Graph Neural Networks

    Victor Garcia;Joan Bruna

  • Training Convolutional Networks with Noisy Labels

    Sainbayar Sukhbaatar;Joan Bruna;Manohar Paluri;Lubomir Bourdev

  • Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges

    Michael M. Bronstein;Joan Bruna;Taco Cohen;Petar Veličković

  • Video (language) modeling: a baseline for generative models of natural videos.

    Marc'Aurelio Ranzato;Arthur Szlam;Joan Bruna;Michaël Mathieu

  • Super-Resolution with Deep Convolutional Sufficient Statistics

    Joan Bruna;Pablo Sprechmann;Yann LeCun;Yann LeCun

  • Stability Properties of Graph Neural Networks

    Fernando Gama;Joan Bruna;Alejandro Ribeiro

  • Deep Geometric Prior for Surface Reconstruction

    Francis Williams;Teseo Schneider;Claudio Silva;Denis Zorin

  • Classification with scattering operators

    Joan Bruna;Stephane Mallat

  • Unsupervised Learning of Spatiotemporally Coherent Metrics

    Ross Goroshin;Joan Bruna;Jonathan Tompson;David Eigen

  • Supervised community detection with line graph neural networks

    Zhengdao Chen;Lisha Li;Joan Bruna

  • On the equivalence between graph isomorphism testing and function approximation with GNNs

    Zhengdao Chen;Soledad Villar;Lei Chen;Joan Bruna

  • Mathematics of Deep Learning

    Rene Vidal;Joan Bruna;Raja Giryes;Stefano Soatto

  • A Note on Learning Algorithms for Quadratic Assignment with Graph Neural Networks.

    Alex Nowak;Soledad Villar;Afonso S. Bandeira;Joan Bruna

  • A new approach to observational cosmology using the scattering transform

    Sihao Cheng;Yuan Sen Ting;Brice Menard;Joan Bruna;Joan Bruna;Joan Bruna

  • Can Graph Neural Networks Count Substructures

    Zhengdao Chen;Lei Chen;Soledad Villar;Joan Bruna

  • Kymatio: Scattering Transforms in Python

    Mathieu Andreux;Tomás Angles;Georgios Exarchakis;Roberto Leonarduzzi

  • Supervised Community Detection with Line Graph Neural Networks

    Zhengdao Chen;Xiang Li;Joan Bruna

Frequent Co-Authors

Yann LeCun
Yann LeCun Facebook (United States)
Stéphane Mallat
Stéphane Mallat École Normale Supérieure
Arthur Szlam
Arthur Szlam DeepMind (United Kingdom)
Denis Zorin
Denis Zorin New York University
Rob Fergus
Rob Fergus New York University
Daniele Panozzo
Daniele Panozzo New York University
Alejandro Ribeiro
Alejandro Ribeiro University of Pennsylvania
Kyunghyun Cho
Kyunghyun Cho New York University
Rajesh Ranganath
Rajesh Ranganath New York University

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