His research interests lies in the fields of nonparametric and robust statistics, machine learning, and imputation of missing data. In the center of investigation is the statistical data depth function, an evolving machinery able to describe multivariate and functional data in a distribution-free way. He is mainly working on depth-based classification and depth-computing algorithms allowing for application of the concept of data depth by practitioners.
During the postdoc, still keeping working on depth-based supervised classification (jointly with Tatjana Lange, Karl Mosler, and Oleksii Pokotylo) and exploring the computational aspects of data depth (jointly with Rainer Dyckerhoff), he started to extend the area of research to high-dimensional data (jointly with Karl Mosler), imputation of missing values (jointly with Julie Josse and François Husson), and econometric large-scale applications: composite likelihood estimation (jointly with Jan Vogler) and nonparametric data envelopment analysis (jointly with Oleg Badunenko, see also R-package npsf).
He is now Assistant Professor in Statistics at ENSAI