World's Best Scientists 2026 revealed!

D-Index & Metrics

Engineering and Technology

D-Index
46
Citations
17980
World Ranking
5026
National Ranking
101

Gilles Celeux publication distribution in Engineering and Technology in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Engineering and Technology in 2026. The highlighted bar marks where Gilles Celeux sits on this spectrum.

38–47 publications: 20 scientists 48–57 publications: 35 scientists 58–67 publications: 96 scientists 68–77 publications: 135 scientists 78–87 publications: 190 scientists 88–97 publications: 259 scientists 98–107 publications: 283 scientists 108–117 publications: 369 scientists 118–127 publications: 341 scientists 128–137 publications: 386 scientists 138–147 publications: 372 scientists 148–157 publications: 457 scientists 158–167 publications: 415 scientists 168–177 publications: 407 scientists 178–187 publications: 421 scientists 188–197 publications: 378 scientists 198–207 publications: 403 scientists 208–217 publications: 317 scientists 218–227 publications: 346 scientists 228–237 publications: 321 scientists 238–247 publications: 260 scientists 248–257 publications: 280 scientists 258–267 publications: 240 scientists 268–277 publications: 214 scientists 278–287 publications: 242 scientists 288–297 publications: 203 scientists 298–307 publications: 166 scientists 308–317 publications: 154 scientists 318–327 publications: 175 scientists 328–337 publications: 159 scientists 338–347 publications: 99 scientists 348–357 publications: 131 scientists 358–367 publications: 106 scientists 368–377 publications: 118 scientists 378–387 publications: 97 scientists 388–397 publications: 108 scientists 398–407 publications: 82 scientists 408–417 publications: 71 scientists 418–427 publications: 64 scientists 428–437 publications: 55 scientists 438–447 publications: 54 scientists 448–457 publications: 60 scientists 458–467 publications: 47 scientists 468–477 publications: 40 scientists 478–487 publications: 30 scientists 488–497 publications: 29 scientists 498–507 publications: 38 scientists 508–517 publications: 40 scientists 518–527 publications: 32 scientists 528–537 publications: 23 scientists 538–547 publications: 28 scientists 548–557 publications: 23 scientists 558–567 publications: 19 scientists 568–577 publications: 16 scientists 578–587 publications: 17 scientists 588–597 publications: 18 scientists 598–607 publications: 22 scientists 608–617 publications: 15 scientists 618–627 publications: 9 scientists 628–637 publications: 11 scientists 638–647 publications: 21 scientists 648–657 publications: 12 scientists 658–667 publications: 9 scientists 668–677 publications: 11 scientists 678–687 publications: 9 scientists 688–697 publications: 6 scientists 698–707 publications: 14 scientists 708–717 publications: 7 scientists 718–727 publications: 8 scientists 728–737 publications: 10 scientists 738–747 publications: 9 scientists 748–757 publications: 5 scientists 758–767 publications: 5 scientists 768–777 publications: 11 scientists 778–787 publications: 7 scientists 788–797 publications: 2 scientists 798–803 publications: 4 scientists 804+ publications: 100 scientists
38 publications 804+

This scientist: 155 publications — 29th percentile

29% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 804 publications or more.

Gilles Celeux D-index placement in Engineering and Technology in 2026

The chart shows the D-index (discipline H-index) distribution of Engineering and Technology scientists ranked by Research.com in 2026. The highlighted bar marks where Gilles Celeux sits on this spectrum.

30 D-Index: 59 scientists 31 D-Index: 114 scientists 32 D-Index: 129 scientists 33 D-Index: 189 scientists 34 D-Index: 200 scientists 35 D-Index: 262 scientists 36 D-Index: 311 scientists 37 D-Index: 312 scientists 38 D-Index: 350 scientists 39 D-Index: 385 scientists 40 D-Index: 348 scientists 41 D-Index: 362 scientists 42 D-Index: 426 scientists 43 D-Index: 380 scientists 44 D-Index: 310 scientists 45 D-Index: 341 scientists 46 D-Index: 301 scientists 47 D-Index: 306 scientists 48 D-Index: 271 scientists 49 D-Index: 246 scientists 50 D-Index: 210 scientists 51 D-Index: 253 scientists 52 D-Index: 213 scientists 53 D-Index: 221 scientists 54 D-Index: 195 scientists 55 D-Index: 186 scientists 56 D-Index: 170 scientists 57 D-Index: 167 scientists 58 D-Index: 166 scientists 59 D-Index: 144 scientists 60 D-Index: 152 scientists 61 D-Index: 141 scientists 62 D-Index: 138 scientists 63 D-Index: 131 scientists 64 D-Index: 118 scientists 65 D-Index: 114 scientists 66 D-Index: 119 scientists 67 D-Index: 95 scientists 68 D-Index: 87 scientists 69 D-Index: 77 scientists 70 D-Index: 89 scientists 71 D-Index: 69 scientists 72 D-Index: 54 scientists 73 D-Index: 46 scientists 74 D-Index: 55 scientists 75 D-Index: 54 scientists 76 D-Index: 49 scientists 77 D-Index: 53 scientists 78 D-Index: 46 scientists 79 D-Index: 28 scientists 80 D-Index: 39 scientists 81 D-Index: 36 scientists 82 D-Index: 24 scientists 83 D-Index: 26 scientists 84 D-Index: 36 scientists 85 D-Index: 18 scientists 86 D-Index: 25 scientists 87 D-Index: 19 scientists 88 D-Index: 26 scientists 89 D-Index: 27 scientists 90 D-Index: 23 scientists 91 D-Index: 15 scientists 92 D-Index: 12 scientists 93 D-Index: 9 scientists 94 D-Index: 15 scientists 95 D-Index: 10 scientists 96 D-Index: 13 scientists 97 D-Index: 13 scientists 98 D-Index: 9 scientists 99 D-Index: 7 scientists 100 D-Index: 7 scientists 101 D-Index: 8 scientists 102 D-Index: 7 scientists 103 D-Index: 7 scientists 104 D-Index: 9 scientists 105 D-Index: 6 scientists 106 D-Index: 9 scientists 107+ D-Index: 99 scientists
30 D-Index 107+

This scientist: 46 D-Index — 49th percentile

49% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 107 D-Index or more.

Overview

Gilles Celeux is affiliated with the French Institute for Research in Computer Science and Automation (INRIA) in France. Their research primarily spans the field of computer science, with a focus on artificial intelligence, statistics and probability, signal processing, media technology, and analytical chemistry.

Celeux's recent publications reflect a concentration on advanced clustering methods, statistical modeling, and data management. Notable works include:

  • "Hierarchical Clustering of Spectral Images with Spatial Constraints for the Rapid Processing of Large and Heterogeneous Data Sets," 2022, SN Computer Science
  • "Model-based clustering with missing not at random data," 2024, Statistics and Computing
  • "Combining Methods in Supervised Classification: a Comparative Study on Discrete and Continuous Problems," 2022, DOAJ (DOAJ: Directory of Open Access Journals)
  • "Model-based Clustering with Missing Not At Random Data," 2021, arXiv (Cornell University)
  • "Estimating Parameters of the Weibull Competing Risk Model with Masked Causes and Heavily Censored Data," 2021, Proceedings of the 31st European Safety and Reliability Conference (ESREL 2021)

The main topics addressed in their work include:

  • Bayesian Methods and Mixture Models
  • Advanced Clustering Algorithms Research
  • Data Management and Algorithms
  • Statistical and Computational Modeling
  • Neural Networks and Applications
  • Statistical Methods and Inference
  • Geochemistry and Geologic Mapping

Frequent co-authors collaborating with Celeux are:

  • Aude Sportisse
  • Matthieu Marbac
  • Fabien Laporte
  • Claire Boyer
  • Christophe Biernacki

Celeux's research contributions have been published in several venues, with multiple papers appearing in "Statistique et société," as well as journals such as "Statistics and Computing," "DOAJ," "SN Computer Science," and "arXiv (Cornell University)." These platforms underline the interdisciplinary and methodological aspects of their research in statistics and computer science.

Best Publications

  • An entropy criterion for assessing the number of clusters in a mixture model

    Gilles Celeux;Gilda Soromenho

  • Assessing a mixture model for clustering with the integrated completed likelihood

    C. Biernacki;G. Celeux;G. Govaert

  • Gaussian parsimonious clustering models

    Gilles Celeux;Gérard Govaert

  • A Classification EM algorithm for clustering and two stochastic versions

    Gilles Celeux;Gérard Govaert

  • Deviance information criteria for missing data models

    G. Celeux;F. Forbes;C. P. Robert;D. M. Titterington

  • Choosing starting values for the EM algorithm for getting the highest likelihood in multivariate Gaussian mixture models

    Christophe Biernacki;Gilles Celeux;Gérard Govaert

  • Computational and inferential difficulties with mixture posterior distributions

    Gilles Celeux;Merrilee Hurn;Christian P. Robert

  • Introduction to the special section on video surveillance

    R.T. Collins;A.J. Lipton;T. Kanade

  • EM Procedures Using Mean Field-Like Approximations for Markov Model-Based Image Segmentation

    Gilles Celeux;Florence Forbes;Nathalie Peyrard

  • Combining Mixture Components for Clustering

    Jean Patrick Baudry;Adrian E. Raftery;Gilles Celeux;Kenneth Lo

  • On Bayesian analysis of mixtures with an unknown number of components. Discussion. Author's reply

    S. Richardson;P. J. Green;C. P. Robert;M. Aitkin

  • Variable selection for clustering with Gaussian mixture models.

    Cathy Maugis;Gilles Celeux;Marie-Laure Martin-Magniette;Marie-Laure Martin-Magniette

  • Inference in model-based cluster analysis

    Halima Bensmail;Gilles Celeux;Adrian E. Raftery;Christian P. Robert

  • Data-based filtering for replicated high-throughput transcriptome sequencing experiments

    Andrea Rau;Mélina Gallopin;Gilles Celeux;Florence Jaffrézic

  • Stochastic versions of the em algorithm: an experimental study in the mixture case

    Gilles Celeux;Didier Chauveau;Jean Diebolt

  • Regularized Gaussian Discriminant Analysis through Eigenvalue Decomposition

    Halima Bensmail;Gilles Celeux

  • Bayesian estimation of hidden Markov chains: a stochastic implementation

    Christian P. Robert;Gilles Celeux;Jean Diebolt

  • Model-based cluster and discriminant analysis with the MIXMOD software

    Christophe Biernacki;Gilles Celeux;Gérard Govaert;Florent Langrognet

  • An improvement of the NEC criterion for assessing the number of clusters in a mixture model

    Christophe Biernacki;Gilles Celeux;Gérand Govaert

  • Selecting hidden Markov model state number with cross-validated likelihood

    Gilles Celeux;Jean-Baptiste Durand

Frequent Co-Authors

Christian P. Robert
Christian P. Robert Paris Dauphine University
Jean-Michel Marin
Jean-Michel Marin University of Montpellier
Adrian E. Raftery
Adrian E. Raftery University of Washington
D. M. Titterington
D. M. Titterington University of Glasgow
Noel A Cressie
Noel A Cressie University of Wollongong
Walter R. Gilks
Walter R. Gilks University of Leeds
Jorge S. Marques
Jorge S. Marques Instituto Superior Técnico
Geoffrey J. McLachlan
Geoffrey J. McLachlan University of Queensland
Raphael Gottardo
Raphael Gottardo Fred Hutchinson Cancer Research Center
Jean-Pierre Renou
Jean-Pierre Renou University of Angers

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