Congratulations to our new accredited statisticians!
A.Stat. Accreditation
Pan, Yuewen #183
Yuewen has a Master of Science in mathematics and statistics (concentration in statistics) and an Honours Bachelor of Science in statistics (co-op) with a minor in computer science for scientists from the University of Ottawa. Her academic training provided a strong foundation in statistical methodology, data analysis, and computational techniques. She completed multiple cooperative education work terms as a research assistant at the Bruyère Research Institute, where she contributed to healthcare research through data extraction, statistical analysis, and evidence synthesis. She coauthored peer-reviewed publications in the Journal of Clinical Epidemiology and Campbell Systematic Reviews and supported projects focused on health equity and social prescribing. Her work included managing large-scale healthcare datasets, conducting quantitative and qualitative analyses, and developing research summaries and reports.
Her master’s research focused on multiple imputation and predictive modelling using clinical data, applying techniques such as LASSO logistic regression and Cox proportional hazards models.
Her professional interests include biostatistics, data analytics, and applying statistical methods to healthcare research and evidence-based decision-making. She is committed to maintaining professional standards and continuing professional development in statistics.
Sadeem, Thierry Fotchou #184
Thierry is a statistician with a master's degree in statistics from Laval University and a bachelor's degree in mathematics from the University of Yaoundé. His training included statistical modelling, data analysis, and advanced quantitative methods. He has 4 years of professional experience as a statistician and 5 years of teaching experience. At Laval University, he worked as a research student and teaching assistant at the graduate level. His research focused on predictive modelling in pharmaceutical sciences, machine learning, and model performance evaluation. He contributed to teaching statistical programming with R, modelling with SAS, and statistical methodology. Before coming to Canada, he worked as a statistician in Cameroon, assisting researchers and organizations with study planning, the selection of analytical methods, data processing, and the writing of scientific reports.
His professional interests include applied statistics, data analysis, and college or university teaching.
Eckel, Graham #185
Graham is a machine learning engineer and data scientist with an academic foundation in mathematics and statistics and over a decade of professional experience applying quantitative methods to business problems for organizations in healthcare, finance, manufacturing, retail, and research.
He has an MSc in mathematics and statistics with a collaborative specialization in artificial intelligence from the University of Guelph. His graduate research focused on applying deep learning and natural language processing techniques to large-scale semantic matching and information retrieval problems.
Professionally, he has worked in a range of data roles, including data analyst, data engineer, data scientist, and machine learning engineer. Across these roles, he developed and deployed statistical models, dashboards, data warehouses, and data pipelines. More recently, his work has focused on designing and deploying enterprise machine learning and generative AI systems, including Agentic AI and retrieval-augmented generation architectures, to support recommendation and automation use cases.
Outside of his professional work, he spends time travelling, snowboarding, or scuba diving.
Ouimet, Frederic #186
Frederic completed a PhD in mathematics at the University of Montreal in 2019, under the supervision of Louis-Pierre Arguin and Alexander Fribergh. From 2019 to 2021, he was a postdoctoral researcher in probability and statistics at the California Institute of Technology, under the supervision of Maksym Radziwill. From 2021 to 2024, he pursued a postdoctoral fellowship in statistics at the University of Montreal and McGill University, under the supervision of Christian Genest. From 2024 to 2025, he was Christian Genest's research assistant at McGill University and a postdoctoral researcher at the University of Sherbrooke, under the supervision of Anne MacKay. Since June 2025, he has been a professor of statistics at the University of Quebec at Trois-Rivières. He has also been teaching at the University of Quebec at Montréal since 2018.
His main interests are in statistics, in modelling, nonparametric estimation, multivariate analysis, matrix analysis, and asymptotic theory, as well as in probability, in branching processes, log-correlated fields, disordered systems and stochastic orders. He is also interested in applications in medical imaging and finance.
P.Stat. Accreditation
Davis, Michael John #220
Jack is an assistant professor in the teaching stream in the Department of Statistics and Actuarial Science at the University of Waterloo. He has a PhD in statistics from Simon Fraser University. He has lectured at Simon Fraser University, the University of British Columbia, and the University of Waterloo. He has industry experience at Sportlogiq, Big River Analytics, and Wiley Interactive. He copyedits for the Canadian Journal of Statistics.
His professional interests include the analysis of games and sports, ranging from chess and poker to cricket and ice hockey. He has written dozens of sports analyses and looks forward to finishing a textbook on the topic.
His other professional and research interests include the analysis of legal citation networks, computational methods using fractions, analysis of electroencephalogram data, statistical education and course development, binned data, game design, polling methods and questionnaire design, academic copyediting, advancements in data visualization, and data ethics.