Philippe GAGNON

Philippe GAGNON is an Associate Professor in the Department of Mathematics and Statistics at Université de Montréal, affiliated with the Centre de recherches mathématiques (CRM). He specializes in robust statistical methods, Bayesian inference, and actuarial science applications. His research focuses on developing robust models and efficient algorithms for data analysis, particularly in high-dimensional and outlier-prone contexts. He teaches courses such as ACT-2284 (Mathematics of Property and Casualty Insurance) and ACT-3261 (Predictive Modeling).

Education:

• PhD in Statistics
• Master’s in Statistics
• Bachelor’s in Actuarial Science (fulfilled requirements for Society of Actuaries’ Associate designation)
• Postdoctoral Research, University of Oxford (with Arnaud Doucet)

Research Interests:

GAGNON’s work integrates robust Bayesian methods with advanced computational techniques like Markov Chain Monte Carlo (MCMC). Key themes include:

• Robust generalized linear models for actuarial applications
• Non-reversible jump algorithms for efficient model selection
• Automated statistical learning procedures
• Outlier-resistant parameter estimation

Funding and Grants:

• NSERC Discovery Grant (2020–2027)
• FRQNT Emerging Researcher Grant (2020–2026)
• MITACS Accelerate Projects (Synthetic Data in Insurance, Spatial Regression Models)

Advising: Current/previous advisees include Yuxi Wang (MSc 2022) and Arghya Datta (PhD in progress). He actively recruits students with backgrounds in theoretical statistics, actuarial science, or computer science.

Labs/Teams: Collaborates within Université de Montréal’s research groups on computational statistics and actuarial science applications.