I am currently a postdoctoral researcher at the Laboratory of Applied and Theoretical NeuroAnatomy (NAAT, Roberto Toro) at Institut Pasteur as part of the Unfold project.
I completed a PhD in Signal, Image, Vision in the Empenn lab under the supervision of Olivier Commowick, François Rousseau and Christian Barillot before joining the Computer Imaging Group (CIG, Gary Zhang) at the UCL Hawkes Institute (formerly CMIC) as a postdoctoral researcher.
My research focuses on developing mathematical and computational methods for neuroimaging, with a core expertise in machine learning, geometric modelling, and physics-informed numerical methods.
How to extract reliable geometric and quantitative descriptions of the brain from images that are distorted, variable across acquisition conditions, and acquired at different scales?
I address this question through geometric and quantitative inverse problems, including registration, atlasing, distortion correction, and parameter estimation in quantitative MRI, using a combination of classical optimisation and deep learning.
More information in the Projects page.
I release my work as open-source software.