World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
51
Citations
7502
World Ranking
5439
National Ranking
112

Michèle Sebag 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 Michèle Sebag 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: 278 publications — 69th percentile

69% of scientists in this discipline score the same or lower.

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

Michèle Sebag 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 Michèle Sebag 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: 51 D-Index — 63rd percentile

63% of scientists in this discipline score the same or lower.

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

Overview

Michèle Sebag is affiliated with the University of Paris-Saclay in France and has a research focus predominantly in computer science, with a significant contribution to artificial intelligence. Their work spans various subfields including artificial intelligence, molecular biology, computer vision and pattern recognition, statistical and nonlinear physics, and computer networks and communications.

The scientist's research covers a range of topics, with notable emphasis on Bayesian modeling and causal inference, machine learning and data classification, neural networks and applications, generative adversarial networks and image synthesis, topic modeling, single-cell and spatial transcriptomics, and model reduction and neural networks.

Michèle Sebag has contributed to multiple publication venues, including.

  • arXiv (Cornell University)
  • AEA Randomized Controlled Trials
  • Frontiers in Big Data
  • Bioinformatics
  • IEEE Computer Graphics and Applications

Frequently collaborating with other researchers, Sebag's coauthors include:

  • Victor Berger
  • Shuyu Dong
  • Kento Uemura
  • Akito Fujii
  • Shuang Chang

Recent publications by Michèle Sebag comprise:

  • Interdisciplinary Research in Artificial Intelligence: Challenges and Opportunities, 2020, Frontiers in Big Data
  • GAN-based data augmentation for transcriptomics: survey and comparative assessment, 2023, Bioinformatics
  • Cartolabe: A Web-Based Scalable Visualization of Large Document Collections, 2020, IEEE Computer Graphics and Applications
  • Variational Auto-Encoder: not all failures are equal, 2020, arXiv (Cornell University)
  • In Silico Generation of Gene Expression profiles using Diffusion Models, 2024, bioRxiv (Cold Spring Harbor Laboratory)

In addition to journal and conference publications, Sebag has authored books published by the Centre National de la Recherche Scientifique, including "Actes de la conférence CAID 2020" released in 2021.

Best Publications

  • Extending Population-Based Incremental Learning to Continuous Search Spaces

    Michèle Sebag;Michèle Sebag;Antoine Ducoulombier;Antoine Ducoulombier

  • The grand challenge of computer Go: Monte Carlo tree search and extensions

    Sylvain Gelly;Levente Kocsis;Marc Schoenauer;Michèle Sebag

  • TreeFinder: a first step towards XML data mining

    A. Termier;M.-C. Rousset;M. Sebag

  • Xyleme, a dynamic warehouse for XML data of the Web

    S. Abiteboul;V. Aguilera;S. Ailleret;B. Amann

  • Adaptive operator selection with dynamic multi-armed bandits

    Luis DaCosta;Alvaro Fialho;Marc Schoenauer;Michèle Sebag

  • Analyzing bandit-based adaptive operator selection mechanisms

    Álvaro Fialho;Luis Da Costa;Marc Schoenauer;Michèle Sebag

  • BenchNN: On the broad potential application scope of hardware neural network accelerators

    Tianshi Chen;Yunji Chen;Marc Duranton;Qi Guo

  • APRIL: active preference learning-based reinforcement learning

    Riad Akrour;Marc Schoenauer;Michèle Sebag

  • Tractable induction and classification in first order logic via stochastic matching

    Michele Sebag;Celine Rouveirol

  • Extreme Value Based Adaptive Operator Selection

    Álvaro Fialho;Luís Costa;Marc Schoenauer;Michèle Sebag

  • Analysis of the AutoML Challenge Series 2015–2018

    Isabelle Guyon;Isabelle Guyon;Lisheng Sun-Hosoya;Lisheng Sun-Hosoya;Marc Boullé;Hugo Jair Escalante

  • Preference-based policy learning

    Riad Akrour;Marc Schoenauer;Michele Sebag

  • Data Streaming with Affinity Propagation

    Xiangliang Zhang;Cyril Furtlehner;Michèle Sebag

  • Comparison-based optimizers need comparison-based surrogates

    Ilya Loshchilov;Marc Schoenauer;Michèle Sebag

  • Data Stream Clustering With Affinity Propagation

    Xiangliang Zhang;Cyril Furtlehner;Cécile Germain-Renaud;Michèle Sebag

  • Multi-armed Bandit, Dynamic Environments and Meta-Bandits

    Cédric Hartland;Sylvain Gelly;Nicolas Baskiotis;Olivier Teytaud

  • Compact Unstructured Representations for Evolutionary Design

    Hatem Hamda;François Jouve;Evelyne Lutton;Marc Schoenauer

  • Self-adaptive surrogate-assisted covariance matrix adaptation evolution strategy

    Ilya Loshchilov;Marc Schoenauer;Michele Sebag

  • Feature Selection as a One-Player Game

    Romaric Gaudel;Michele Sebag

  • Interdisciplinary Research in Artificial Intelligence: Challenges and Opportunities.

    Remy Kusters;Dusan Misevic;Hugues Berry;Antoine Cully

  • Exploration vs Exploitation vs Safety: Risk-averse Multi-Armed Bandits

    Nicolas Galichet;Michèle Sebag;Olivier Teytaud

Frequent Co-Authors

Marc Schoenauer
Marc Schoenauer French Institute for Research in Computer Science and Automation - INRIA
Olivier Teytaud
Olivier Teytaud Facebook (United States)
Xiangliang Zhang
Xiangliang Zhang University of Notre Dame
Sylvain Gelly
Sylvain Gelly Google (United States)
Isabelle Guyon
Isabelle Guyon University of Paris-Saclay
Francesco Bonchi
Francesco Bonchi Institute for Scientific Interchange
Elena Marchiori
Elena Marchiori Radboud University
Aristides Gionis
Aristides Gionis Royal Institute of Technology
A. E. Eiben
A. E. Eiben Vrije Universiteit Amsterdam
David Lopez-Paz
David Lopez-Paz Facebook AI Research (FAIR) in Paris

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