I am a postdoctoral researcher at the IT University of Copenhagen, where I mainly work with Jes Frellsen.
I received in 2017 my Ph.D. in applied mathematics from Université Paris Descartes. I worked at the MAP5 lab, where I had the chance to be advised by Charles Bouveyron
and Pierre Latouche.
My field of research is statistical machine learning, with a particular emphasis on model uncertainty and sparsity. During my Ph.D., I mainly developed new Bayesian model selection methods for high-dimensional data. I also currently work on Bayesian deep learning for bioinformatics.
Preprints & Working papers
Wasserstein Adversarial Mixture Clustering
Preprint HAL-01827775, Université Paris Descartes, 2018
Class-specific Variable Selection in High-Dimensional Discriminant Analysis through Bayesian Sparsity
Preprint HAL-01811514, Université Côte d'Azur, 2018
Exact Dimensionality Selection for Bayesian PCA
Preprint HAL-01484099, Université Paris Descartes, 2017
Leveraging the Exact Likelihood of Deep Latent Variable Models
Advances in Neural Information Processing Signals (NIPS), in press, 2018
Bayesian Variable Selection for Globally Sparse Probabilistic PCA
Electronic Journal of Statistics, in press, 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
Journal of Multivariate Analysis, vol. 146, pp. 177–190, 2016