Hugo RAGUET did his thesis in Paris in signal processing and convex optimization, with applications in neurobiological imaging. One of the projects was to provide software to separate the neural signal from interference, in “potentiometric optical imaging” recordings.
He then worked in Marseilles on convex optimization for statistical learning, in particular on problems structured on graphs. Applications include “identification of brain sources” from electroencephalograms, as well as “semantic classification of clouds of 3D points recorded with lidar”.
You can find on this Github directory some corresponding illustrations:
He then worked at the Atomic Energy Commission of Cadarache, where he developed statistical tools for sensitivity analysis, with applications in industrial safety.
He now teaches at the INSA Centre Val-de-Loire, algorithmics, programming, data analysis, image processing and statistical learning.
At LIFAT, RAGUET continues to work on image processing in general, with applications in medical imaging in particular, and on convex optimization, but also combinatorial optimization.
His Github account where you can find some of his contributions: https://github.com/1a7r0ch3/