Bayesian statistics
Computational statistics


I am interested in general applications of statistics in epidemiology. These past few years I have specialized in causal inference, the study of causality relationships between specific exposure and response variables. An example of the type of application on which I am currently working is the determination of the effects of inhaled corticosteroids used in the treatment of asthma on pregnant women and their babies.

My research themes in causal inference are:

  • Mediation 
  • Model selection
  • Graphical approaches
  • Generalized propensity scores
  • Modelling and miscellaneous applications

I have used the Bayesian paradigm for most of my previous work. I have notably worked on path sampling, an advanced integration technique that aims to estimate the marginal likelihood, an essential quantity for model selection in this context. Through this research I have developed an expertise in Markov Chain Monte Carlo (MCMC) techniques and in Bayesian modelling more generally 

My list of publications is here.



  • Atlantic Causal Inference Conference (McGill, 22-24 mai 2019) - les résultats du "Data Challenge" sont maintenant disponibles !!
  • Nouveau centre de recherche facultaire en statistique et sciences des données STATQAM ! 
  • Ouverture pour un projet de mémoire collaboratif en biostatistique : contactez-moi pour détails !





I am recruiting qualified and motivated MSc/PhD students (towards a degree in mathematics concentration statistics at UQAM) or post-doctoral fellows. Potential trainees should have a good background in statistics, very good computer skills and interest in data analysis in epidemiology.