World's Best Scientists 2026 revealed!
Award Badge
Computer Science
France
2026

D-Index & Metrics

Computer Science

D-Index
112
Citations
57983
World Ranking
204
National Ranking
5

Francis Bach 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 Francis Bach 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: 405 publications — 87th percentile

87% of scientists in this discipline score the same or lower.

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

Francis Bach 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 Francis Bach 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: 112 D-Index — 99th percentile

99% of scientists in this discipline score the same or lower.

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

Research.com Recognitions

  • 2026 - Research.com Computer Science in France Leader Award
  • 2025 - Research.com Computer Science in France Leader Award
  • 2023 - Research.com Computer Science in France Leader Award
  • 2022 - Research.com Computer Science in France Leader Award

Overview

Francis Bach is affiliated with École Normale Supérieure in France. Their research spans multiple fields of study, principally intersecting Engineering and Computer Science. The scientist's work focuses on areas such as Computational Mechanics, Artificial Intelligence, and Computer Vision and Pattern Recognition, with additional contributions to Statistics and Probability as well as Numerical Analysis.

The research topics Francis Bach has contributed to include:

  • Sparse and Compressive Sensing Techniques
  • Face and Expression Recognition
  • Advanced Optimization Algorithms Research
  • Groundwater Flow and Contamination Studies
  • Markov Chains and Monte Carlo Methods
  • Machine Learning and Data Classification
  • Domain Adaptation and Few-Shot Learning

The scientist has published extensively, with notable frequent appearances in the following venues:

  • arXiv (Cornell University)
  • SIAM Journal on Mathematics of Data Science

Recent papers authored or co-authored by Francis Bach include:

  • Non-parametric Models for Non-negative Functions, 2020, arXiv (Cornell University)
  • Batch Normalization Provably Avoids Rank Collapse for Randomly Initialised Deep Networks, 2020, arXiv (Cornell University)
  • Optimal Estimation of Smooth Transport Maps with Kernel SoS, 2024, SIAM Journal on Mathematics of Data Science
  • Overcoming the Curse of Dimensionality with Laplacian Regularization in Semi-supervised Learning, 2020, arXiv (Cornell University)
  • Kernelized Diffusion Maps, 2023, arXiv (Cornell University)

Collaboration has been a notable aspect of their work, frequently co-authoring with:

  • Alessandro Rudi
  • Boris Muzellec
  • Loucas Pillaud-Vivien
  • Nathan Doumèche
  • Gérard Biau

Best Publications

  • Online Learning for Matrix Factorization and Sparse Coding

    Julien Mairal;Francis Bach;Jean Ponce;Guillermo Sapiro

  • Online dictionary learning for sparse coding

    Julien Mairal;Francis Bach;Jean Ponce;Guillermo Sapiro

  • Kernel independent component analysis

    Francis R. Bach;Michael I. Jordan

  • Non-local sparse models for image restoration

    Julien Mairal;Francis Bach;Jean Ponce;Guillermo Sapiro

  • Online Learning for Latent Dirichlet Allocation

    Matthew Hoffman;Francis R. Bach;David M. Blei

  • Multiple kernel learning, conic duality, and the SMO algorithm

    Francis R. Bach;Gert R. G. Lanckriet;Michael I. Jordan

  • SAGA: A Fast Incremental Gradient Method With Support for Non-Strongly Convex Composite Objectives

    Aaron Defazio;Francis Bach;Simon Lacoste-Julien

  • Learning mid-level features for recognition

    Y-Lan Boureau;Francis Bach;Yann LeCun;Jean Ponce

  • Supervised Dictionary Learning

    Julien Mairal;Jean Ponce;Guillermo Sapiro;Andrew Zisserman

  • Minimizing finite sums with the stochastic average gradient

    Mark Schmidt;Nicolas Le Roux;Francis Bach

  • Optimization with Sparsity-Inducing Penalties

    Francis Bach;Rodolphe Jenatton;Julien Mairal;Guillaume Obozinski

  • Task-Driven Dictionary Learning

    J. Mairal;F. Bach;J. Ponce

  • Discriminative learned dictionaries for local image analysis

    J. Mairal;F. Bach;J. Ponce;G. Sapiro

  • Consistency of the Group Lasso and Multiple Kernel Learning

    Francis R. Bach

  • A Stochastic Gradient Method with an Exponential Convergence _Rate for Finite Training Sets

    Nicolas L. Roux;Mark Schmidt;Francis R. Bach

  • Dimensionality Reduction for Supervised Learning with Reproducing Kernel Hilbert Spaces

    Kenji Fukumizu;Francis R. Bach;Michael I. Jordan

  • Supervised Dictionary Learning

    Julien Mairal;Francis Bach;Jean Ponce;Guillermo Sapiro

  • Non-Asymptotic Analysis of Stochastic Approximation Algorithms for Machine Learning

    Eric Moulines;Francis R. Bach

  • A Tensor-Based Algorithm for High-Order Graph Matching

    O. Duchenne;F. Bach;In-So Kweon;Jean Ponce

  • Structured Variable Selection with Sparsity-Inducing Norms

    Rodolphe Jenatton;Jean-Yves Audibert;Francis Bach

  • Full regularization path for sparse principal component analysis

    Alexandre d'Aspremont;Francis R. Bach;Laurent El Ghaoui

Frequent Co-Authors

Jean Ponce
Jean Ponce École Normale Supérieure
Julien Mairal
Julien Mairal French Institute for Research in Computer Science and Automation - INRIA
Michael I. Jordan
Michael I. Jordan University of California, Berkeley
Jean-Philippe Vert
Jean-Philippe Vert Google (United States)
Simon Lacoste-Julien
Simon Lacoste-Julien University of Montreal
Mark Schmidt
Mark Schmidt University of British Columbia
Laurent Massoulié
Laurent Massoulié French Institute for Research in Computer Science and Automation - INRIA
Bernhard Pfahringer
Bernhard Pfahringer University of Waikato
Ricard Gavaldà
Ricard Gavaldà Universitat Politècnica de Catalunya

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

Studying Computer Science in the USA opens a pathway to numerous related online degrees and diverse career opportunities. Many students seeking flexibility choose the fastest computer science degree route, allowing them to complete their studies more quickly and enter the workforce sooner.

Those interested in the intersection of technology and the environment may explore environmental engineering degree online programs. Graduates in this field can pursue impactful careers in sustainable technology or conservation. Additionally, applying computational skills in traditional STEM fields can lead to engaging job prospects. For instance, earning an online degree for mechanical engineering provides opportunities to work in robotics, manufacturing, or aerospace.

For career changers or those interested in adjacent fields, there are also high-paying jobs with environmental science degree credentials. This makes studying Computer Science just one avenue among a broader range of rewarding, online-accessible technical careers. Consider the options that best fit your skills and long-term goals.

Best Scientists Citing Francis Bach

Trending Scientists

Recently Published Articles