World's Best Scientists 2026 revealed!

D-Index & Metrics

Mathematics

D-Index
42
Citations
7164
World Ranking
1793
National Ranking
104

Frédéric Chazal publication distribution in Mathematics in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Mathematics in 2026. The highlighted bar marks where Frédéric Chazal sits on this spectrum.

42–46 publications: 3 scientists 47–51 publications: 5 scientists 52–56 publications: 7 scientists 57–61 publications: 20 scientists 62–66 publications: 14 scientists 67–71 publications: 25 scientists 72–76 publications: 19 scientists 77–81 publications: 35 scientists 82–86 publications: 50 scientists 87–91 publications: 60 scientists 92–96 publications: 86 scientists 97–101 publications: 84 scientists 102–106 publications: 83 scientists 107–111 publications: 90 scientists 112–116 publications: 99 scientists 117–121 publications: 90 scientists 122–126 publications: 91 scientists 127–131 publications: 109 scientists 132–136 publications: 110 scientists 137–141 publications: 98 scientists 142–146 publications: 112 scientists 147–151 publications: 102 scientists 152–156 publications: 88 scientists 157–161 publications: 106 scientists 162–166 publications: 83 scientists 167–171 publications: 102 scientists 172–176 publications: 77 scientists 177–181 publications: 81 scientists 182–186 publications: 78 scientists 187–191 publications: 71 scientists 192–196 publications: 92 scientists 197–201 publications: 64 scientists 202–206 publications: 69 scientists 207–211 publications: 64 scientists 212–216 publications: 62 scientists 217–221 publications: 58 scientists 222–226 publications: 53 scientists 227–231 publications: 50 scientists 232–236 publications: 46 scientists 237–241 publications: 46 scientists 242–246 publications: 46 scientists 247–251 publications: 43 scientists 252–256 publications: 29 scientists 257–261 publications: 45 scientists 262–266 publications: 30 scientists 267–271 publications: 33 scientists 272–276 publications: 34 scientists 277–281 publications: 30 scientists 282–286 publications: 31 scientists 287–291 publications: 21 scientists 292–296 publications: 34 scientists 297–301 publications: 26 scientists 302–306 publications: 10 scientists 307–311 publications: 17 scientists 312–316 publications: 23 scientists 317–321 publications: 13 scientists 322–326 publications: 16 scientists 327–331 publications: 26 scientists 332–336 publications: 13 scientists 337–341 publications: 13 scientists 342–346 publications: 16 scientists 347–351 publications: 17 scientists 352–356 publications: 12 scientists 357–361 publications: 18 scientists 362–366 publications: 18 scientists 367–371 publications: 9 scientists 372–376 publications: 11 scientists 377–381 publications: 8 scientists 382–386 publications: 8 scientists 387–391 publications: 9 scientists 392–396 publications: 9 scientists 397–401 publications: 8 scientists 402–406 publications: 11 scientists 407–411 publications: 6 scientists 412–416 publications: 6 scientists 417–421 publications: 9 scientists 422–426 publications: 8 scientists 427–431 publications: 5 scientists 432–436 publications: 8 scientists 437–441 publications: 8 scientists 442–446 publications: 4 scientists 447–451 publications: 4 scientists 452–456 publications: 4 scientists 457–461 publications: 2 scientists 462–466 publications: 2 scientists 467–471 publications: 4 scientists 472–476 publications: 3 scientists 477–481 publications: 3 scientists 482–486 publications: 6 scientists 487–491 publications: 3 scientists 492–496 publications: 5 scientists 497–501 publications: 5 scientists 502–506 publications: 1 scientists 507–511 publications: 6 scientists 512–516 publications: 4 scientists 517–521 publications: 1 scientists 522–526 publications: 3 scientists 527–531 publications: 1 scientists 532–536 publications: 4 scientists 537+ publications: 100 scientists
42 publications 537+

This scientist: 111 publications — 16th percentile

16% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 537 publications or more.

Frédéric Chazal D-index placement in Mathematics in 2026

The chart shows the D-index (discipline H-index) distribution of Mathematics scientists ranked by Research.com in 2026. The highlighted bar marks where Frédéric Chazal sits on this spectrum.

30 D-Index: 174 scientists 31 D-Index: 151 scientists 32 D-Index: 174 scientists 33 D-Index: 117 scientists 34 D-Index: 136 scientists 35 D-Index: 127 scientists 36 D-Index: 145 scientists 37 D-Index: 153 scientists 38 D-Index: 150 scientists 39 D-Index: 150 scientists 40 D-Index: 138 scientists 41 D-Index: 136 scientists 42 D-Index: 93 scientists 43 D-Index: 108 scientists 44 D-Index: 115 scientists 45 D-Index: 112 scientists 46 D-Index: 103 scientists 47 D-Index: 75 scientists 48 D-Index: 59 scientists 49 D-Index: 67 scientists 50 D-Index: 60 scientists 51 D-Index: 57 scientists 52 D-Index: 59 scientists 53 D-Index: 62 scientists 54 D-Index: 60 scientists 55 D-Index: 50 scientists 56 D-Index: 42 scientists 57 D-Index: 54 scientists 58 D-Index: 50 scientists 59 D-Index: 42 scientists 60 D-Index: 41 scientists 61 D-Index: 35 scientists 62 D-Index: 40 scientists 63 D-Index: 21 scientists 64 D-Index: 31 scientists 65 D-Index: 27 scientists 66 D-Index: 29 scientists 67 D-Index: 19 scientists 68 D-Index: 25 scientists 69 D-Index: 17 scientists 70 D-Index: 18 scientists 71 D-Index: 12 scientists 72 D-Index: 14 scientists 73 D-Index: 13 scientists 74 D-Index: 18 scientists 75 D-Index: 9 scientists 76 D-Index: 11 scientists 77 D-Index: 10 scientists 78 D-Index: 9 scientists 79 D-Index: 16 scientists 80 D-Index: 12 scientists 81 D-Index: 10 scientists 82 D-Index: 5 scientists 83 D-Index: 5 scientists 84 D-Index: 13 scientists 85 D-Index: 6 scientists 86+ D-Index: 99 scientists
30 D-Index 86+

This scientist: 42 D-Index — 51st percentile

51% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 86 D-Index or more.

Overview

Frédéric Chazal is affiliated with the French Institute for Research in Computer Science and Automation (INRIA) in France. Their research spans several areas within computer science and molecular biology, with a particular focus on topological and geometric data analysis.

The main fields of study associated with Chazal include:

  • Computer Science
  • Biochemistry, Genetics and Molecular Biology

Their work relates to various subfields, including:

  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Biophysics
  • Molecular Biology

Key research topics addressed in their publications are:

  • Topological and Geometric Data Analysis
  • Cell Image Analysis Techniques
  • Advanced Neuroimaging Techniques and Applications
  • Single-cell and Spatial Transcriptomics
  • Fractal and DNA Sequence Analysis
  • Image Retrieval and Classification Techniques
  • Machine Learning and Algorithms

Chazal has authored multiple papers, notable recent publications include:

  • "An Introduction to Topological Data Analysis: Fundamental and Practical Aspects for Data Scientists" (2021, Frontiers in Artificial Intelligence)
  • "Optimizing persistent homology based functions" (2020, arXiv (Cornell University))
  • "PLLay: Efficient Topological Layer based on Persistence Landscapes" (2020, arXiv (Cornell University))
  • "Topological Data Analysis and its usefulness for precision medicine studies" (2022, HAL (Le Centre pour la Communication Scientifique Directe))
  • "Optimal quantization of the mean measure and applications to statistical learning" (2020, arXiv (Cornell University))

Frequent collaborators in their research include:

  • Bertrand Michel
  • Clément Levrard
  • Martin Royer
  • Wojciech Reise
  • Marc Glisse

Publications by Chazal appear regularly in venues such as:

  • arXiv (Cornell University)
  • Frontiers in Artificial Intelligence
  • Electronic Journal of Statistics
  • HAL (Le Centre pour la Communication Scientifique Directe)
  • BMC Bioinformatics

Best Publications

  • An Introduction to Topological Data Analysis: Fundamental and Practical Aspects for Data Scientists

    Frédéric Chazal;Bertrand Michel

  • Proximity of persistence modules and their diagrams

    Frédéric Chazal;David Cohen-Steiner;Marc Glisse;Leonidas J. Guibas

  • The Structure and Stability of Persistence Modules

    Frédéric Chazal;Vin de Silva;Marc Glisse;Steve Oudot

  • Persistence-Based Clustering in Riemannian Manifolds

    Frédéric Chazal;Leonidas J. Guibas;Steve Y. Oudot;Primoz Skraba

  • Gromov-Hausdorff stable signatures for shapes using persistence

    Frédéric Chazal;David Cohen-Steiner;Leonidas J. Guibas;Facundo Mémoli

  • Persistence stability for geometric complexes

    Frédéric Chazal;Vin de Silva;Steve Y. Oudot

  • The λ-medial axis

    Frédéric Chazal;André Lieutier

  • A Sampling Theory for Compact Sets in Euclidean Space

    Frédéric Chazal;David Cohen-Steiner;André Lieutier

  • Geometric Inference for Probability Measures

    Frédéric Chazal;David Cohen-Steiner;Quentin Mérigot

  • Towards persistence-based reconstruction in euclidean spaces

    Frédéric Chazal;Steve Yann Oudot

  • Map-based exploration of intrinsic shape differences and variability

    Raif M. Rustamov;Maks Ovsjanikov;Omri Azencot;Mirela Ben-Chen

  • Persistence-based segmentation of deformable shapes

    Primoz Skraba;Maks Ovsjanikov;Frederic Chazal;Leonidas Guibas

  • Molecular shape analysis based upon the morse-smale complex and the connolly function

    F. Cazals;F. Chazal;T. Lewiner

  • Persistence-Based Structural Recognition

    Chunyuan Li;Maks Ovsjanikov;Frederic Chazal

  • Stochastic Convergence of Persistence Landscapes and Silhouettes

    Frédéric Chazal;Brittany Terese Fasy;Fabrizio Lecci;Alessandro Rinaldo

  • Robust Topological Inference: Distance To a Measure and Kernel Distance

    Frédéric Chazal;Brittany Terese Fasy;Fabrizio Lecci;Bertrand Michel

  • Computing and processing correspondences with functional maps

    Maks Ovsjanikov;Etienne Corman;Michael Bronstein;Emanuele Rodolà

  • Weak feature size and persistent homology: computing homology of solids in Rn from noisy data samples

    Frédéric Chazal;André Lieutier

  • Metric graph reconstruction from noisy data

    Mridul Aanjaneya;Frederic Chazal;Daniel Chen;Marc Glisse

  • Geometric and Topological Inference

    Jean-Daniel Boissonnat;Frédéric Chazal;Mariette Yvinec

  • Metric Graph Reconstruction From Noisy Data

    Mridul Aanjaneya;Frédéric Chazal;Daniel Chen;Marc Glisse

  • Stochastic convergence of persistence landscapes and silhouettes

    Frédéric Chazal;Brittany Terese Fasy;Fabrizio Lecci;Alessandro Rinaldo

Frequent Co-Authors

David Cohen-Steiner
David Cohen-Steiner French Institute for Research in Computer Science and Automation - INRIA
Larry Wasserman
Larry Wasserman Carnegie Mellon University
Alessandro Rinaldo
Alessandro Rinaldo The University of Texas at Austin
Leonidas J. Guibas
Leonidas J. Guibas Stanford University
Maks Ovsjanikov
Maks Ovsjanikov École Polytechnique
Mirela Ben-Chen
Mirela Ben-Chen Technion – Israel Institute of Technology
Aarti Singh
Aarti Singh Carnegie Mellon University
Jian Sun
Jian Sun Megvii
Tamal K. Dey
Tamal K. Dey Purdue University West Lafayette
Jean-Daniel Boissonnat
Jean-Daniel Boissonnat Université Côte d'Azur

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