World's Best Scientists 2026 revealed!
Alexandre Gramfort

Alexandre Gramfort

D-Index & Metrics

Computer Science

D-Index
56
Citations
99800
World Ranking
3924
National Ranking
1861

Alexandre Gramfort 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 Alexandre Gramfort 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: 244 publications — 61st percentile

61% of scientists in this discipline score the same or lower.

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

Alexandre Gramfort 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 Alexandre Gramfort 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: 56 D-Index — 72nd percentile

72% of scientists in this discipline score the same or lower.

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

Overview

Alexandre Gramfort is affiliated with META in the United States. Their research spans multiple domains within neuroscience and computer science. The primary fields of study addressed in their publications include Neuroscience and Computer Science, with significant contributions also found in Cognitive Neuroscience, Artificial Intelligence, Signal Processing, Radiology, Nuclear Medicine and Imaging, and Computer Vision and Pattern Recognition.

The scientist's work focuses on key topics such as EEG and Brain-Computer Interfaces, Functional Brain Connectivity Studies, Neural dynamics and brain function, Blind Source Separation Techniques, Sparse and Compressive Sensing Techniques, Neurobiology of Language and Bilingualism, and Topic Modeling.

Alexandre Gramfort has published extensively in a variety of venues, among which the most frequent are arXiv (Cornell University), bioRxiv (Cold Spring Harbor Laboratory), NeuroImage, Zenodo (CERN European Organization for Nuclear Research), and Imaging Neuroscience.

Some of their recent papers include the following:

  • Evidence of a predictive coding hierarchy in the human brain listening to speech, 2023, Nature Human Behaviour
  • Issues and recommendations from the OHBM COBIDAS MEEG committee for reproducible EEG and MEG research, 2020, Nature Neuroscience
  • Uncovering the structure of clinical EEG signals with self-supervised learning, 2020, arXiv (Cornell University)
  • Combining magnetoencephalography with magnetic resonance imaging enhances learning of surrogate-biomarkers, 2020, eLife
  • A large, curated, open-source stroke neuroimaging dataset to improve lesion segmentation algorithms, 2022, Scientific Data

The scientist has collaborated frequently with several coauthors, including Denis A. Engemann, Jean-Rémi King, Bertrand Thirion, Gaël Varoquaux, and Charlotte Caucheteux.

Best Publications

  • Scikit-learn: Machine Learning in Python

    Fabian Pedregosa;Gaël Varoquaux;Alexandre Gramfort;Vincent Michel

  • MEG and EEG data analysis with MNE-Python

    Alexandre Gramfort;Martin Luessi;Eric Larson;Denis A. Engemann

  • Machine learning for neuroimaging with scikit-learn.

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

  • MNE software for processing MEG and EEG data

    Alexandre Gramfort;Martin Luessi;Eric Larson;Denis A. Engemann

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

    Lars Buitinck;Gilles Louppe;Mathieu Blondel;Fabian Pedregosa

  • OpenMEEG: opensource software for quasistatic bioelectromagnetics

    Alexandre Gramfort;Théodore Papadopoulo;Emmanuel Olivi;Maureen Clerc

  • Deep learning-based electroencephalography analysis: a systematic review.

    Yannick Roy;Hubert J. Banville;Isabela Albuquerque;Alexandre Gramfort

  • Large scale screening of neural signatures of consciousness in patients in a vegetative or minimally conscious state.

    Jacobo Diego Sitt;Jacobo Diego Sitt;Jacobo Diego Sitt;Jean-Remi King;Jean-Remi King;Jean-Remi King;Imen El Karoui;Benjamin Rohaut

  • Scikit-learn: Machine Learning in Python

    Fabian Pedregosa;Gaël Varoquaux;Alexandre Gramfort;Vincent Michel

  • A Deep Learning Architecture for Temporal Sleep Stage Classification Using Multivariate and Multimodal Time Series

    Stanislas Chambon;Mathieu N. Galtier;Pierrick J. Arnal;Gilles Wainrib

  • Autoreject: Automated artifact rejection for MEG and EEG data.

    Mainak Jas;Denis A. Engemann;Yousra Bekhti;Federico Raimondo

  • 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

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

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

  • Evidence of a predictive coding hierarchy in the human brain listening to speech

    Unknown

  • Time-frequency mixed-norm estimates: Sparse M/EEG imaging with non-stationary source activations

    Alexandre Gramfort;Daniel Strohmeier;Jens Haueisen;Matti S. Hämäläinen

  • Mixed-norm estimates for the M/EEG inverse problem using accelerated gradient methods

    Alexandre Gramfort;Matthieu Kowalski;Matti Hämäläinen

  • Machine Learning for Neuroimaging with Scikit-Learn

    Alexandre Abraham;Fabian Pedregosa;Michael Eickenberg;Philippe Gervais

  • Multi-subject dictionary learning to segment an atlas of brain spontaneous activity

    Gael Varoquaux;Alexandre Gramfort;Fabian Pedregosa;Vincent Michel

  • Automated model selection in covariance estimation and spatial whitening of MEG and EEG signals.

    Denis A. Engemann;Alexandre Gramfort

  • Issues and recommendations from the OHBM COBIDAS MEEG committee for reproducible EEG and MEG research.

    Cyril Pernet;Marta I. Garrido;Alexandre Gramfort;Natasha Maurits

  • Single-trial decoding of auditory novelty responses facilitates the detection of residual consciousness.

    Jean-Remi King;Frédéric Faugeras;Alexandre Gramfort;Alexandre Gramfort;Alexandre Gramfort;Aaron Schurger;Aaron Schurger

  • Total Variation Regularization for fMRI-Based Prediction of Behavior

    V. Michel;A. Gramfort;G. Varoquaux;E. Eger

Frequent Co-Authors

Gaël Varoquaux
Gaël Varoquaux French Institute for Research in Computer Science and Automation - INRIA
Bertrand Thirion
Bertrand Thirion University of Paris-Saclay
Matti Hämäläinen
Matti Hämäläinen Harvard Medical School
Marco Cuturi
Marco Cuturi École Nationale de la Statistique et de l'Administration Économique
Jens Haueisen
Jens Haueisen Ilmenau University of Technology
Virginie van Wassenhove
Virginie van Wassenhove University of Paris-Saclay
Jean-Rémi King
Jean-Rémi King École Normale Supérieure
Cédric Lemogne
Cédric Lemogne Université Paris Cité
Aapo Hyvärinen
Aapo Hyvärinen University of Helsinki
Francis Bach
Francis Bach École Normale Supérieure

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