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
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
Class-specific Variable Selection in High-Dimensional Discriminant Analysis through Bayesian Sparsity
Journal of Chemometrics, in press, 2018
Bayesian Variable Selection for Globally Sparse Probabilistic PCA
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
Journal of Multivariate Analysis, vol. 146, pp. 177–190, 2016