I'm research scientist at Inria, in Sophia Antipolis, near Nice. I'm part of the Maasai (Models and Algorithms for Artificial Intelligence) team. I'm also affiliated with the J.A. Dieudonné lab, which is the mathematics research department of Université Côte d'Azur.

I received in 2017 my Ph.D. in applied mathematics from Université Paris Descartes (now called Université de Paris). I worked at the MAP5 lab, where I had the chance to be advised by Charles Bouveyron and Pierre Latouche. After my Ph.D., I did a postdoc at the IT University of Copenhagen, where I mainly worked with Jes Frellsen.

My field of research is statistical machine learning, with a particular emphasis on hidden variables and model uncertainty. During my Ph.D., I mainly developed new Bayesian model selection methods for high-dimensional data. I also currently work on deep generative models and their applications.


High-dimensional statistics
MSc Data science, Université Côte d'Azur, 2019/2020
Deep learning course
MSc Data science, Université Côte d'Azur, 2019/2020
Deep latent variable models and missing data imputation
Course at the ProbAI summer school, in Trondheim, Norway, 2019

Preprints & Working papers

A Parsimonious Tour of Bayesian Model Uncertainty
Preprint arXiv:1902.05539, 2019
Refit your Encoder when New Data Comes by
(with Jes Frellsen)
Short version presented at the 3rd NeurIPS workshop on Bayesian Deep Learning, 2018
Wasserstein Adversarial Mixture Clustering
(with Warith Harchaoui, Charles Bouveyron, and Andrés Almansa)
Preprint HAL-01827775, Université Paris Descartes, 2018



Exact Dimensionality Selection for Bayesian PCA
(with Charles Bouveyron and Pierre Latouche)
Scandinavian Journal of Statistics, vol. 47 (1), pp. 196-211, 2020


MIWAE: Deep Generative Modelling and Imputation of Incomplete Data Sets
(with Jes Frellsen)
Proceedings of the 36th International Conference on Machine Learning, pp. 4413-4423, 2019
Partially Exchangeable Networks and Architectures for Learning Summary Statistics in Approximate Bayesian Computation
(with Samuel Wiqvist, Umberto Picchini, and Jes Frellsen)
Proceedings of the 36th International Conference on Machine Learning, pp. 6798-6807, 2019
Class-specific Variable Selection in High-Dimensional Discriminant Analysis through Bayesian Sparsity
(with Fanny Orlhac, Charles Bouveyron, and Nicholas Ayache)
Journal of Chemometrics, vol. 33 (2), e3097, 2019


Leveraging the Exact Likelihood of Deep Latent Variable Models
(with Jes Frellsen)
Advances in Neural Information Processing Signals 31, pp. 3859-3870, 2018
Bayesian Variable Selection for Globally Sparse Probabilistic PCA
(with Charles Bouveyron and Pierre Latouche)
Electronic Journal of Statistics, vol. 12 (2), pp. 3036-3070, 2018


Model Selection for Sparse High-Dimensional Learning
Ph.D. Thesis, Université Paris Descartes, 2017
Multiplying a Gaussian Matrix by a Gaussian Vector
Statistics & Probability Letters, vol. 128, pp. 67–70, 2017
Discussion on the Paper "A Bayesian Information Criterion for Singular Models" by Drton and Plummer
Journal of the Royal Statistical Society: Series B, vol. 79, pp. 370–371, 2017


Combining a Relaxed EM Algorithm with Occam's Razor for Bayesian Variable Selection in High-Dimensional Regression
(with Pierre Latouche, Charles Bouveyron, and Julien Chiquet)
Journal of Multivariate Analysis, vol. 146, pp. 177–190, 2016