World's Best Scientists 2026 revealed!
Alain Rakotomamonjy

Alain Rakotomamonjy

D-Index & Metrics

Computer Science

D-Index
37
Citations
9814
World Ranking
10493
National Ranking
257

Alain Rakotomamonjy 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 Alain Rakotomamonjy 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: 163 publications — 32nd percentile

32% of scientists in this discipline score the same or lower.

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

Alain Rakotomamonjy 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 Alain Rakotomamonjy 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: 37 D-Index — 27th percentile

27% of scientists in this discipline score the same or lower.

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

Overview

Alain Rakotomamonjy is affiliated with Criteo in France and has a significant body of research contributions primarily in the field of Computer Science. Their work spans various subfields, including Artificial Intelligence, Computer Vision and Pattern Recognition, Cognitive Neuroscience, Signal Processing, and Computational Mechanics.

Their main research topics cover diverse aspects of machine learning and data science, including:

  • Domain Adaptation and Few-Shot Learning
  • Generative Adversarial Networks and Image Synthesis
  • EEG and Brain-Computer Interfaces
  • Multimodal Machine Learning Applications
  • Adversarial Robustness in Machine Learning
  • Privacy-Preserving Technologies in Data
  • Stochastic Gradient Optimization Techniques

Rakotomamonjy has collaborated frequently with several co-authors, including Gilles Gasso, Mokhtar Z. Alaya, Maxime Bérar, Matthieu Kirchmeyer, and Rémi Flamary. These collaborations often lead to publications in well-recognized venues.

The scientist's publication record shows a preference for journals and repositories such as:

  • arXiv (Cornell University)
  • HAL (Le Centre pour la Communication Scientifique Directe)
  • Pattern Recognition Letters
  • Procedia Computer Science
  • Machine Learning

Highlighted recent papers by or connected to their research interests include:

  • POT Python Optimal Transport, 2025, HAL (Le Centre pour la Communication Scientifique Directe)
  • Optimal Transport Applied To Transfer Learning For P300 Detection, 2020, HAL (Le Centre pour la Communication Scientifique Directe)
  • Continuous PDE Dynamics Forecasting with Implicit Neural Representations, 2022, arXiv (Cornell University)
  • Approximating dynamic time warping with a convolutional neural network on EEG data, 2023, Pattern Recognition Letters
  • Diverse Weight Averaging for Out-of-Distribution Generalization, 2022, arXiv (Cornell University)

These publications reflect a focus on topics such as optimal transport methods, transfer learning applied to EEG data, neural network approaches for signal processing, and strategies for improving generalization in machine learning models.

Best Publications

  • A Review of Classification Algorithms for EEG-based Brain-Computer Interfaces: A 10-year Update

    Fabien Lotte;Laurent Bougrain;Andrzej Cichocki;Andrzej Cichocki;Maureen Clerc

  • Optimal Transport for Domain Adaptation

    Nicolas Courty;Remi Flamary;Devis Tuia;Alain Rakotomamonjy

  • Variable selection using svm based criteria

    Alain Rakotomamonjy

  • BCI Competition III: Dataset II- Ensemble of SVMs for BCI P300 Speller

    A. Rakotomamonjy;V. Guigue

  • Joint Distribution Optimal Transportation for Domain Adaptation

    Nicolas Courty;Rémi Flamary;Amaury Habrard;Alain Rakotomamonjy

  • Pedestrian Detection using Infrared images and Histograms of Oriented Gradients

    F. Suard;A. Rakotomamonjy;A. Bensrhair;A. Broggi

  • More efficiency in multiple kernel learning

    Alain Rakotomamonjy;Francis Bach;Stéphane Canu;Yves Grandvalet

  • Recovering Sparse Signals With a Certain Family of Nonconvex Penalties and DC Programming

    G. Gasso;A. Rakotomamonjy;S. Canu

  • Composite kernel learning

    Marie Szafranski;Yves Grandvalet;Alain Rakotomamonjy

  • Histogram of gradients of time-frequency representations for audio scene classification

    Alain Rakotomamonjy;Gilles Gasso

  • Review: Surveying and comparing simultaneous sparse approximation (or group-lasso) algorithms

    A. Rakotomamonjy

  • A Pedestrian Detector Using Histograms of Oriented Gradients and a Support Vector Machine Classifier

    M. Bertozzi;A. Broggi;M. Del Rose;M. Felisa

  • Optimizing Area Under Roc Curve with SVMs

    Alain Rakotomamonjy

  • Ensemble of SVMs for improving brain computer interface p300 speller performances

    A. Rakotomamonjy;V. Guigue;G. Mallet;V. Alvarado

  • Support Vector Machines with a Reject Option

    Yves Grandvalet;Alain Rakotomamonjy;Joseph Keshet;Stéphane Canu

  • Frames, Reproducing Kernels, Regularization and Learning

    Alain Rakotomamonjy;Stéphane Canu

  • Wasserstein Discriminant Analysis

    Rémi Flamary;Marco Cuturi;Nicolas Courty;Alain Rakotomamonjy

  • Automatic Feature Learning for Spatio-Spectral Image Classification With Sparse SVM

    Devis Tuia;Michele Volpi;Mauro Dalla Mura;Alain Rakotomamonjy

  • Operator-valued kernels for learning from functional response data

    Hachem Kadri;Emmanuel Duflos;Philippe Preux;Stéphane Canu

  • $ll_{p}-ll_{q}$ Penalty for Sparse Linear and Sparse Multiple Kernel Multitask Learning

    A. Rakotomamonjy;R. Flamary;G. Gasso;S. Canu

  • Pedestrian Detection usingInfraredimages and Histograms of Oriented Gradients

    F. Suard;A. Rakotomamonjy;A. Bensrhair;A. Broggi

Frequent Co-Authors

Stéphane Canu
Stéphane Canu Institut National des Sciences Appliquées de Rouen
Devis Tuia
Devis Tuia École Polytechnique Fédérale de Lausanne
Marco Congedo
Marco Congedo Centre national de la recherche scientifique, CNRS
Patrick Gallinari
Patrick Gallinari Sorbonne University
Christophe Rosenberger
Christophe Rosenberger Université de Caen Normandie
Alberto Broggi
Alberto Broggi University of Parma
Francis Bach
Francis Bach École Normale Supérieure
Mauro Dalla Mura
Mauro Dalla Mura Grenoble Alpes University
Fabien Lotte
Fabien Lotte French Institute for Research in Computer Science and Automation - INRIA
Andrzej Cichocki
Andrzej Cichocki Systems Research Institute

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