I am an assistant professor of actuarial science in the Department of Mathematics and Statistics at Université de Montréal. Before that, I was a postdoctoral researcher at University of Oxford, working with Arnaud Doucet on non-reversible jump algorithms.
My experience and interest in actuarial science began with undergraduate studies in this field. While doing the bachelor’s degree, I also fulfilled all the requirements for obtaining the Associate designation from the Society of Actuaries. After that, I completed a master’s and Ph.D. in Statistics. During the graduate studies, I developed an interest for Bayesian robustness against outliers and Markov chain Monte Carlo methods. I more precisely introduced methodology for obtaining 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 sophisticated methods of interest for actuaries. 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 Project for more details).
Advertisement: I am looking for students with a strong background in either: theoretical statistics (mainly probability and analysis), applied statistics and actuarial science, and 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 (see, e.g., NSERC and FRQNT). It is even possible for non-Canadians to apply for FRQNT scholarships. Please do not hesitate to contact me if you are interested. Recruiting will be done according to Université de Montréal’s Equity, Diversity and Inclusion policy.