Mehdi Dagdoug

Dr. Mehdi Dagdoug is an Assistant Professor in the Department of Mathematics and Statistics at McGill University, Montreal, Canada. His research focuses on the intersection of survey sampling, missing data treatment, and statistical learning. He holds a Ph.D. from the Université de Bourgogne Franche-Comté, supervised by Camelia Goga and David Haziza.

Education:

• Ph.D. in Mathematics, 2022, Université de Bourgogne Franche-Comté
• Postdoctoral Fellow, 2022-2023, University of Ottawa

Research Interests: Dr. Dagdoug develops rigorous inference methods for survey sampling, particularly addressing nonresponse challenges through statistical learning tools. His work emphasizes high-dimensional settings where auxiliary variables exceed sample sizes. Key areas include model-assisted estimation, variance estimation for imputed survey data, and random forest applications in finite population sampling.

Teaching: Currently teaches MATH 533 (Linear Regression & ANOVA) and MATH 525 (Sampling Theory) at McGill. Previously instructed courses in survey sampling, statistical learning, and programming at undergraduate and graduate levels.

Awards:

• 2022 Jean-Claude Deville Prize (French Statistical Society)

Grants: NSERC Discovery Grant, Mitacs Accelerate Proposal. Previously supported by Region Franche-Comté and Médiamétrie.

Administrative Roles:

• Committee Member, Student Travel Grants (Statistical Society of Canada)
• Organizer, McGill Statistics Seminar Series (2023-2025)
• Board Member, BFC-Maths Federation (2020-2022)

Popularization: Engages in science outreach through workshops and conferences for high school students, including a popular 'Titanic and Random Forests' workshop.