Positions
- Professor in statistics and machine learning MIA AgroParisTech, INRAE, Université Paris-Saclay
Lecturer Ecole polytechnique
Research interests
- Network analysis and random graph models
- Statistical inference algorithms
- Uncertainty quantification
- Deep learning
- Applications in various domains including ecology, biology, chemistry, industrial production processes
Activities
- Formation continue CNRS Formation Entreprises Introduction au Machine Learning et au Deep Learning avec Python. Prochaine édition du 10 au 12 juin 2026.
- Vidéo de mon exposé à Mathematic Park intitulé Tous connectés sur les réseaux sociaux. Quels outils mathématiques pour les analyser ?
PhD students
- Ariane Marandon, co-supervised with Etienne Roquain and Nataliya Sokolovska, 2020-2023.
- Sara Rejeb, thèse CIFRE avec Safran Aircraft Engines, co-supervised with Catherine Duveau, 2020-2023.
- Arsen Sultanov, co-supervised with Jean-Claude Crivello (ICMPE) and Nataliya Sokolovska, 2021-2024.
- Roland Sogan, co-supervised with Fanny Villers, since 2023.
- Maud Hassenfratz, co-supervised with Alexandre Wadoux, since 2025.
Contact
tabea DOT rebafka AT agroparistech DOT fr
Campus AgroParisTech, Bureau E4.2.16
Développement de code
- R package graphclust released on CRAN, 2023. Hierarchical graph clustering for a collection of networks.
- R package missSOM released on CRAN. With Sara Rejeb and Catherine Duveau, 2021. Self-organizing maps with built-in missing-data imputation.
- R package noisySBM released on CRAN. With Fanny Villers and Etienne Roquain, 2020. Implementation of the methods presented in the article Graph inference with clustering and false discovery rate control.
- R package ppsbm released on CRAN. With Daphné Giorgi, Catherine Matias and Fanny Villers, 2018. This package contains the optimized R code for PPSBM.