Portrait of Mehdi Dagdoug

Mehdi Dagdoug

Assistant Professor of Statistics

Department of Mathematics and Statistics
McGill University

  • Survey sampling
  • Missing data
  • Statistical learning
  • High-dimensional inference

I am interested in statistical inference from survey data, both for finite population quantities and for superpopulation quantities, particularly in difficult settings: a large number of auxiliary variables, domains containing few sampled units, or nonresponse. Survey data are often neither independent nor identically distributed. I aim to understand how classical methods behave in this setting and how new tools, including machine learning, can improve them, by studying their asymptotic properties, how their uncertainty can be quantified, and when they are optimal.

This leads me to work on machine learning for finite population inference, statistical learning from survey data, missing data and high-dimensional inference.

Burnside Hall, room 1230

Contact Research Publications CV (PDF)

Teaching this year

  • MATH 598 · Asymptotic Statistics (Fall 2026)
  • MATH 324 · Statistics (Winter 2027)
Teaching →

Office & contact

mehdi.dagdoug@mcgill.ca

Burnside Hall, room 1230
805 rue Sherbrooke Ouest, Montréal, QC H3A 0B9

About

I am an Assistant Professor in the Department of Mathematics and Statistics at McGill University. Previously, I was a Postdoctoral Fellow at the University of Ottawa, working with David Haziza.

I completed my Ph.D. in Mathematics at the Laboratoire de Mathématiques de Besançon (Université de Bourgogne Franche-Comté), supervised by Camelia Goga and David Haziza. I defended my thesis, Statistical learning for high-dimensional sampling, on July 12, 2022. My doctoral work was funded by the Région Franche-Comté and Médiamétrie.

Recent papers

  1. Agnostic model-assisted estimation with machine learning for survey data with Z. An, D. Haziza and Y. Tillé · Submitted, 2026.
  2. High-dimensional variance estimation for the generalized regression estimator with K. Bouhadra · To appear in The Survey Statistician, 2026.
  3. Machine learning methods for finite population parameter estimation in survey sampling with D. Haziza · Submitted, 2026.
  4. Variable selection for linear regression imputation in surveys with Z. An and D. Haziza · Submitted, 2026.
All publications →

Recent talks

  1. Aug 2026 Agnostic model-assisted estimation in finite population EcoSta 2026, Kyoto, Japan.
  2. Aug 2026 Finite-population inference with ML-based predictions Short course, 2026 Joint Statistical Meetings, Boston, USA.
  3. Jun 2026 Machine learning for the treatment of nonresponse in surveys IVADO 2nd Workshop: Uncertainty in AI, Montréal, Canada.
  4. May 2026 Finite-population inference with ML-based predictions Short course, 2026 SSC Annual Meeting, Hamilton, Canada.
All talks →