My research interests are Bayesian robustness against outliers and Markov chain Monte Carlo methods. Recently, I introduced methodology for robust model/variable selection and parameter estimation in linear regression, along with efficient algorithms for achieving these tasks. My research has so far been more on the theoretical/methodological side. My objective for the next years is to apply the tools I developed in actuarial science, and to introduce new ones of interest for actuaries, such as Bayesian robust generalized linear models. In particular, I want to develop an automatic data analysis procedure in which the models are robust and trained through a full and exact Bayesian analysis. See my personal web page for more details and a list of my publications.

 

Advertisement: I am looking for students with a strong background in either: theoretical statistics, applied statistics and actuarial science, or computer science (the plan is also to develop R packages for a user-friendly and efficient implementation of the methods).  There exist multiple funding opportunities. In particular, I recently obtained grants that are dedicated to the funding of world-class M.Sc. and Ph.D. students, and postdoctoral researchers (see my personal web page for more details). Also, prospective students and postdocs can apply for scholarships such as those of NSERC and FRQNT. Please do not hesitate to contact me if you are interested. I aim to provide an environment to my students in actuarial science and statistics that is equitable, inclusive and diversified. In particular, recruiting will be done according to Université de Montréal's Equity, Diversity and Inclusion policy.