Cédric Beaulac on Genetic Correlates of Alzheimer’s Disease Subtypes

Project Focus Areas

In order to better identify an individual’s predisposition to develop Alzheimer’s disease in the future, Cédric aims to combine existing statistical inference tools with deep learning. 

Cédric will create hybrid models and empirical-data-based statistical models (deep models). This is done to learn the individualized and complex interplay between genetic markers, neuroimaging Alzheimer’s disease phenotypes and clinical measures.

Getting to Know Cédric

After completing his Master’s Degree at the Université du Québec à Montréal, Cédric was awarded the Alexander Graham Bell Canada Graduate Scholarship. This allowed him to pursue a doctoral degree at the University of Toronto, where he worked under the supervision of Professor Jeffrey S. Rosenthal. 

Cédric’s research interests lie at the intersection of statistical sciences and machine learning. While working on his thesis, Cédric developed expertise in probabilistic graphical models such as variational autoencoders and state-space models. He studied the theoretical foundation of such models, developed new model architectures, and employed them to solve problems in different fields such as survival analysis and image analysis. 

As a recipient of the CANSSI Distinguished Postdoctoral Fellowship, Cédric will work under the supervision of Professor Mizra Faisal Beg at Simon Fraser University and Professor Farouk Nathoo at the University of Victoria. 

This project is the perfect opportunity to further develop a research program in image analysis using techniques emerging from both the statistical sciences community and the machine learning community. I’m looking forward to the start of the postdoctoral program.