World's Best Scientists 2026 revealed!
Award Badge
Computer Science
France
2025

D-Index & Metrics

Computer Science

D-Index
63
Citations
112545
World Ranking
2664
National Ranking
37

Gaël Varoquaux 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 Gaël Varoquaux 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: 250 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.

Gaël Varoquaux 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 Gaël Varoquaux 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: 63 D-Index — 81st percentile

81% 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

  • 2025 - Research.com Computer Science in France Leader Award
  • 2023 - Research.com Computer Science in France Leader Award
  • 2022 - Research.com Computer Science in France Leader Award

Overview

Gaël Varoquaux is affiliated with the French Institute for Research in Computer Science and Automation (INRIA) in France. Their research primarily focuses on the field of computer science, with significant contributions to artificial intelligence and its applications in healthcare and neuroscience.

The main fields of study for Varoquaux include:

  • Computer Science

Their subfields of study emphasize specialized areas such as:

  • Artificial Intelligence
  • Cognitive Neuroscience
  • Statistics and Probability
  • Radiology, Nuclear Medicine and Imaging
  • Health Informatics

Varoquaux's research covers several important topics, including:

  • Functional Brain Connectivity Studies
  • Machine Learning in Healthcare
  • Statistical Methods and Inference
  • Artificial Intelligence in Healthcare and Education
  • Explainable Artificial Intelligence (XAI)
  • Topic Modeling
  • Advanced Causal Inference Techniques

They have coauthored multiple papers with several frequent collaborators, including:

  • Bertrand Thirion
  • Demián Wassermann
  • Alexandre Gramfort
  • Olivier Grisel
  • Julie Josse

Their recent papers exemplify the diverse range of their research interests and contributions:

  • "Machine learning for medical imaging: methodological failures and recommendations for the future," 2022, npj Digital Medicine
  • "Metrics reloaded: recommendations for image analysis validation," 2024, Nature Methods
  • "International electronic health record-derived COVID-19 clinical course profiles: the 4CE consortium," 2020, npj Digital Medicine
  • "Combining magnetoencephalography with magnetic resonance imaging enhances learning of surrogate-biomarkers," 2020, eLife
  • "Understanding metric-related pitfalls in image analysis validation," 2024, Nature Methods

Varoquaux frequently publishes in several leading venues, with multiple publications in:

  • arXiv (Cornell University)
  • Zenodo (CERN European Organization for Nuclear Research)
  • GigaScience
  • HAL (Le Centre pour la Communication Scientifique Directe)
  • bioRxiv (Cold Spring Harbor Laboratory)

Best Publications

  • Scikit-learn: Machine Learning in Python

    Fabian Pedregosa;Gaël Varoquaux;Alexandre Gramfort;Vincent Michel

  • The NumPy Array: A Structure for Efficient Numerical Computation

    Stéfan van der Walt;S Chris Colbert;Gaël Varoquaux

  • Machine learning for neuroimaging with scikit-learn.

    Alexandre Abraham;Alexandre Abraham;Fabian Pedregosa;Fabian Pedregosa;Michael Eickenberg;Michael Eickenberg;Philippe Gervais;Philippe Gervais

  • The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments.

    Krzysztof J. Gorgolewski;Tibor Auer;Vince D. Calhoun;R. Cameron Craddock

  • API design for machine learning software: experiences from the scikit-learn project

    Lars Buitinck;Gilles Louppe;Mathieu Blondel;Fabian Pedregosa

  • Establishment of Best Practices for Evidence for Prediction: A Review.

    Russell A Poldrack;Grace Huckins;Gael Varoquaux

  • Mayavi: 3D Visualization of Scientific Data

    P Ramachandran;G Varoquaux

  • Assessing and tuning brain decoders: cross-validation, caveats, and guidelines

    Gaël Varoquaux;Pradeep Reddy Raamana;Denis A. Engemann;Andrés Hoyos-Idrobo

  • Deriving reproducible biomarkers from multi-site resting-state data: An Autism-based example

    Alexandre Abraham;Michael P. Milham;Adriana Di Martino;R. Cameron Craddock

  • NeuroVault.org: a web-based repository for collecting and sharing unthresholded statistical maps of the human brain

    Krzysztof J. Gorgolewski;Krzysztof J. Gorgolewski;Gael Varoquaux;Gabriel Rivera;Yannick Schwarz

  • Scikit-learn: Machine Learning Without Learning the Machinery

    G. Varoquaux;L. Buitinck;G. Louppe;O. Grisel

  • Cross-validation failure: Small sample sizes lead to large error bars.

    Gaël Varoquaux

  • Scikit-learn: Machine Learning in Python

    Fabian Pedregosa;Gaël Varoquaux;Alexandre Gramfort;Vincent Michel

  • Mayavi: a package for 3D visualization of scientific data

    Prabhu Ramachandran;Gaël Varoquaux

  • Predicting brain-age from multimodal imaging data captures cognitive impairment

    Franziskus Liem;Gaël Varoquaux;Gaël Varoquaux;Jana Kynast;Frauke Beyer;Frauke Beyer

  • Which fMRI clustering gives good brain parcellations

    Bertrand Thirion;Gaël Varoquaux;Elvis Dohmatob;Jean-Baptiste Poline;Jean-Baptiste Poline

  • Seeing it all: Convolutional network layers map the function of the human visual system

    Michael Eickenberg;Michael Eickenberg;Michael Eickenberg;Alexandre Gramfort;Gaël Varoquaux;Bertrand Thirion;Bertrand Thirion

  • Benchmarking functional connectome-based predictive models for resting-state fMRI.

    Kamalaker Dadi;Mehdi Rahim;Alexandre Abraham;Darya Chyzhyk

  • Why do tree-based models still outperform deep learning on tabular data?

    Unknown

  • Brain covariance selection: better individual functional connectivity models using population prior

    Gael Varoquaux;Alexandre Gramfort;Jean-baptiste Poline;Bertrand Thirion

  • Machine Learning for Neuroimaging with Scikit-Learn

    Alexandre Abraham;Fabian Pedregosa;Michael Eickenberg;Philippe Gervais

  • Which fMRI clustering gives good brain parcellations

    Bertrand Thirion;Gael Varoquaux;Elvis Dohmatob;Jean-Baptiste Poline

Frequent Co-Authors

Bertrand Thirion
Bertrand Thirion University of Paris-Saclay
Jean-Baptiste Poline
Jean-Baptiste Poline Montreal Neurological Institute and Hospital
Danilo Bzdok
Danilo Bzdok Montreal Neurological Institute and Hospital
Russell A. Poldrack
Russell A. Poldrack Stanford University
R. Cameron Craddock
R. Cameron Craddock Facebook (United States)
Krzysztof J. Gorgolewski
Krzysztof J. Gorgolewski Stanford University
Julien Mairal
Julien Mairal French Institute for Research in Computer Science and Automation - INRIA
Tal Yarkoni
Tal Yarkoni The University of Texas at Austin

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

Exploring career opportunities in Computer Science can extend far beyond traditional degree paths. Aspiring professionals are increasingly looking for programs that offer both quality and affordability. Today, many accredited institutions offer flexible options like the cheapest bachelor degree online, making it easier to start a tech-focused career without incurring heavy debt.

For those interested in specialized fields, pursuing the cheapest online engineering degree can be a smart investment. These programs typically deliver rigorous coursework and hands-on projects, preparing graduates for high-demand roles in software and hardware engineering.

Ambitious professionals seeking leadership roles might consider flexible executive mba programs. These online MBAs provide advanced management skills, helping shape the next generation of tech leaders.

Not all tech careers are limited to programming or engineering. Support areas like digital information management benefit greatly from a masters in library science. This pathway equips students with expertise in data organization, digital archiving, and research—all vital in today's information-driven world.

Best Scientists Citing Gaël Varoquaux

Trending Scientists