Julien Chiquet

UMR MIA Paris-Saclay

Campus Agro Paris-Saclay

22 place de l'agronomie

91120 Palaiseau

+33(0)1.89.10.09.37

julien.chiquet[at]inrae[dot]fr

Habilitation (12/2015)

Academic CV (05/2025)

head of UMR MIA Paris-Saclay, Team SOLsTIS
chief editor of Computo
part-time lecturer at École Polytechnique (2020-2024)

research interests

statistics and machine learning
- sparse methods and regularization
- multivariate analysis
- latent variable models
- optimization and algorithms
applications to life sciences
- ecology, environment
- metagenomics, metabolomics, genomics
reproducible research

news

Sep 10, 2024	Happy to share a quarto template for HCERES report, based on a home-made LaTeX sheet and Pandoc, which allows you to smoothly include markdown, code, equations, plots on the fly and manage bibliographic elements extracted from HAL with BibLaTeX. Easy to install with the quarto installer. The rendered document is here , rendered by CI with gh-actions, if you want a quick look. This was clearly the most exciting part of writing the report for my laboratory. I hope this helps some colleagues, so please feel free to use and contribute.
Jun 1, 2023	I gave two talks last week on probabilistic dimension reduction, one at the Séminaire nantais inter-établissements en Science des Données and another during the first animation of the working group LEGO on machine-learning for genomics, which are two very nice research intitiaves! Check the github repos of my slides and code.
Nov 13, 2022	I am glad to give two talks this week, one at the Séminaire Parisien de Statistique and another at ML4 Life Science in Montpellier, both based on this material about recent advances in the Poisson lognormal model
May 16, 2022	The laboratory moved to Palaiseau with all of AgroParisTech to a brand new campus: a few shocks and adjustments on arrival, but all united!
Feb 24, 2022	I have presented Computo at the 2nd Toronto Workshop on reproducibility ! 🤩

selected publications

EJS

Importance sampling-based gradient method for dimension reduction in Poisson log-normal model

Bastien Batardière, Julien Chiquet, Joon Kwon, and Julien Stoehr

Electronic Journal of Statistics, 2025

arXiv DOI Bib

@article{batardiere2025importance,
  author = {Batardi\`ere, Bastien and Chiquet, Julien and Kwon, Joon and Stoehr, Julien},
  title = {Importance sampling-based gradient method for dimension reduction in Poisson log-normal model},
  journal = {Electronic Journal of Statistics},
  year = {2025},
  volume = {19},
  number = {1},
  doi = {10.1214/25-EJS2384},
}

JSS

Stability Selection and Consensus Clustering in R: The R Package sharp

Barbara Bodinier, Sabrina Rodrigues, Maryam Karimi, Sarah Filippi, Julien Chiquet, and Marc Chadeau-Hyam

Journal of Statistical Software, 2025

Abs DOI

The R package sharp (Stability-enHanced Approaches using Resampling Procedures) provides an integrated framework for stability-enhanced variable selection, graphical modeling and clustering. In stability selection, a feature selection algorithm is combined with a resampling technique to estimate feature selection probabilities. Features with selection proportions above a threshold are considered stably selected. Similarly, a clustering algorithm is applied on multiple subsamples of items to compute co-membership proportions in consensus clustering. The consensus clusters are obtained by clustering using comembership proportions as a measure of similarity. We calibrate the hyper-parameters of stability selection (or consensus clustering) jointly by maximizing a consensus score calculated under the null hypothesis of equiprobability of selection (or co-membership), which characterizes instability. The package offers flexibility in the modeling, includes diagnostic and visualization tools, and allows for parallelization.</p>

JOSS

pyPLNmodels: A Python package to analyze multivariate high-dimensional count data

Bastien Batardiere, Joon Kwon, and Julien Chiquet

Journal of Open Source Software, 2024

DOI Bib

@article{batardiere2024joss,
  doi = {10.21105/joss.06969},
  url = {https://doi.org/10.21105/joss.06969},
  year = {2024},
  publisher = {The Open Journal},
  volume = {9},
  number = {104},
  pages = {6969},
  author = {Batardiere, Bastien and Kwon, Joon and Chiquet, Julien},
  title = {pyPLNmodels: A Python package to analyze multivariate high-dimensional count data},
  journal = {Journal of Open Source Software},
}