Deep learning for MR image reconstruction
My PhD will be focused on designing the best neural network architecture for MR image reconstruction from undersampled data. MR image reconstruction basically consists in doing an inverse Fourier transform from undersampled (in the sense of Nyquist-Shannon) Fourier data. The theory of compressed sensing tells us it's possible if the original data has some sparsity (or at least compressibility) in a particular domain, be it the wavelet decomposition or the TV.
Convex Optimisation for non smooth inverse problems
In order to understand the current state of the art of MR image reconstruction, I need to understand how the current compressed sensing strategies work. It involves solving an inverse problem via convex non smooth optimisation.
- SPARS, Toulouse, France, July 2019 (poster, code).
- ISBI, Iowa City, IO, USA, April 2020 (poster, code) - held virtually.
- Teaching assistant in charge of Python practical sessions, IUT Orsay (2019).
I reviewed submissions for the following conferences:
- Eusipco 2019 (4 5-Page Papers)
- ISBI 2020 (3 4-Page Papers)
And for the following journals:
- Medical Image Analysis (1 Research Paper)
Other institutes I work at
In addition to Jean-Luc Starck, I am also supervised by Philippe Ciuciu who works at Neurospin (CEA) and is part of the Parietal team (Inria). Therefore I spend 4 days a week at Neurospin (1 at Cosmostat) and sometimes pass by the Inria building in Saclay.
Previously to my PhD
I graduated from Telecom Paris and received an MSc in machine learning from ENS Paris-Saclay (MVA). I also did internships in various companies during my studies, and worked 1 year at xbird before starting my PhD.