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.
Publications and preprints
Leveraging the Exact Likelihood of Deep Latent Variable Models
Preprint arXiv:1802.04826 (2018)
Model Selection for Sparse High-Dimensional Learning
Ph.D. Thesis, Université Paris Descartes (2017)
Exact Dimensionality Selection for Bayesian PCA
Preprint HAL-01484099, 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)
Globally Sparse Probabilistic PCA
Proceedings of the 19th International Conference on Artificial Intelligence and Statistics
Proceedings of Machine Learning Research, vol. 51, pp. 976–984 (2016)
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)