Education
- Postdoctoral fellow in Computer science, 2020-2022, Vanderbilt University
- PhD in Computer science, 2016-2020, Université de Sherbrooke
- MSc in Computer science, 2015-2016, Université de Sherbrooke
- BSc in Imaging science and digital medias, 2011-2015, Université de Sherbrooke
Bio
My research area focuses on developing algorithms, pipelines, and tools for medical image analysis. Specifically, my research interests are related to multimodal neuroimaging (including processing, segmentation, statistical analysis, and scientific visualization). I have developed my expertise with the goal of better understanding neuroanatomy (primarily through brain connectivity), brain development, and pathologies relevant to clinical applications. During my graduate studies, I developed close collaborations with clinicians and neuroscientists and targeted my efforts to help them improve their technical skills and develop tools tailored to their needs.
I am particularly interested in assessing algorithmic reproducibility, research replicability, and open-source initiatives.
My current projects involve generating white matter parcellations based on cortical connectivity and diffusion imaging signal (using machine learning), developing neural network architectures tailored to structural connectivity matrices, and creating a new framework for longitudinal analyses of white matter bundles. I am currently leading an initiative to create a new (open, accepted, and community-developed) file format for tractography (.trx). I also contribute to various open-source software (Dipy, Nibabel, Scilpy).
