Teaching philosophy
I approach mathematics, statistics, and data science teaching as an activity of understanding, practice, and communication. Courses are designed to help students produce reproducible analyses, justify their choices, and explain their results clearly.
Active learning
Classes emphasize hands-on work, applied situations, and projects where statistical concepts are used in concrete contexts.
Reproducibility
Activities use R, Quarto, GitHub, and explicit project structures so that reasoning, code, and results can be checked.
Support
Exercises, formative feedback, course assistants, and CDA resources support autonomy while preserving the central role of student understanding.
Teaching innovation and learning support
Explore the projects, resources and student support initiatives on the teaching innovation and CDA pages.
Teaching experience and languages
I teach at undergraduate and graduate levels at Université Laval and UQAC. My experience covers probability, applied statistics, data science, R programming, visualization and project management.
- GSO-6092, Project Management: graduate course taught in English in Winter 2026 and Fall 2026 (in progress).
- GSO-6082, Gestion de projets: partial assignment of 21 teaching hours in Fall 2026, replacing the regular instructor from August 31 to October 16.
- STT-1900: in-person and online teaching and coordination for approximately 350 students in 2025.
In assessments, I ask students to justify their choice of methods, verify computations and interpret uncertainty. When AI tools are permitted, their use must be disclosed and their output checked. My published discussion in the Journal of Data Science (2026) addresses AI-generated assignments.
Courses taught
Université Laval
Data science
STT-1100 - Introduction to Data Science
Introductory course centered on professional situations where students take on a data science role. The course covers the analysis cycle, from data import to communication of results.
Scientific programming
STT-4230 / STT-6230 - Programming for Data Science
Course focused on development practices: project structure, documentation, tests, collaboration, and publication with GitHub.
Applied modelling
MQT-2101 - Data Analysis and Modelling
Applied course using R and Quarto to study regression, forecasting and classification, with course notes, video capsules and analysis activities.
Applied statistics
STT-2200 - Data Analysis
Applied statistics course where students work from real datasets and produce reproducible reports.
STT-1900 and STT-2920
Statistics for Engineering and Probability for Engineering. Probability foundations, inference and modelling for engineering programs.
GSO-6092 and GSO-6082
Graduate project management in English and French: planning, PERT and CPM networks, resource allocation and project control. The scope of the 2026 assignments is described above.
Université du Québec à Chicoutimi
8INF404 - Introduction to Data Science and Business Intelligence
Interdisciplinary course presenting the full data science project cycle: preparation, exploration, predictive modeling, and communication.
8INF416 - Data Visualization and Interface Design
Course devoted to interactive visualization and scientific communication with R, Shiny, and ggplot2.
8STT108 - Statistical Analysis of Large Data Sets
Introduction to statistical methods for large datasets, with emphasis on model logic before automation.
8GEN444 - Engineering Statistics
Statistical methods applied to engineering problems.
Supervision
I have served as faculty consultant for three undergraduate projects on directional statistics (2024), random matrices (2025) and visual inference (2026). I have also supervised industry internships and undergraduate research projects at UQAC, and examined two master’s theses.
My approach to supervision emphasizes a focused question, explicit milestones, version control and reproducible analysis. My research themes provide training topics in simulation, diagnostics, visualization and software development.
Academic coordination and curriculum development
At the CDA, I coordinate and train approximately 30 teaching assistants supporting mathematics and statistics courses. I develop diagnostic assessments to identify learning needs early and guide student support.
I contribute to the review of the statistics degree and the development of Université Laval’s bachelor’s program in statistics and data science. I also contribute to the co-adaptation of Probabilités et statistique, Science et génie, fourth edition, forthcoming from TC Média Livres in April 2027.
GPT-CDA received two PAIP teaching innovation grants, in 2025 and 2026, of CAD 20,000 each. Open resources, including tutorizeR, datasets and tutorials, extend this training work.
Teaching recognition
I received Teaching Stars recognition from the Faculty of Science and Engineering for 2022-2023, 2023-2024, 2024-2025 and 2025-2026, as well as the Professor Star Award in 2014. In 2026, I also received student recognition in the teaching award category at the Gala du mérite.
Early R workshops (2017)
In June 2017, a CBS QCSC statistics workshop was accompanied by introductory R and RStudio materials. This activity is part of my early work on data analysis training resources.