Biography
I am a Research Scientist at Meta in the team of Alex Gramfort. I was a Postdoc Researcher supervised by Gabriel Peyré at ENS Ulm (CNRS). I obtained my PhD in deep learning for MRI reconstruction under the supervision of Philippe Ciuciu and Jean-Luc Starck at Parietal (Inria), NeuroSpin and Cosmostat (CEA).
Download my résumé.
Download my PhD thesis manuscript.
Interests
- Artificial Intelligence
- Computer Vision
- Deep Learning
- AI for Healthcare
- Open Source
Education
PhD in Artificial Intelligence, 2022
CEA & Inria
MSc in Mathematics, Vision and Machine Learning (MVA), 2017
Ecole Normale Superieure Paris-Saclay
Engineering Diploma, 2017
Telecom ParisTech
Experience
AI Research Scientist: Neural interfaces
Meta
- Understanding the EMG
Postdoc Researcher in Deep Learning
CNRS & ENS Ulm
- Worked on the understanding and improvement of Deep Equilibrium Models
- Worked on Benchopt, a benchmarking framework for optimization algorithms
PhD Student
CEA & Inria
- Designed and implemented new models for MRI reconstruction which allowed to me to secure the 2nd spot in the fastMRI 2020 reconstruction challenge organized by Facebook and NYU. This was featured in 2 articles in the specialized press (CEA mag' and Contact).
- Used the public HPC Jean Zay to train neural networks in a distributed fashion with up to 8 nodes totalling 32 GPUs while working on 1Tb of MRI data. Co-created a user’s collaborative documentation.
- Co-founded a lecture group focused on Deep Learning for students at NeuroSpin.
- Analyzed and benchmarked the state-of-the-art in Deep Learning for MRI reconstruction.
- Published 18 papers/abstracts in international peer-reviewed conferences/workshops/journals, among which IEEE TMI and ICLR (spotlight).
- Co-supervised 2 interns: Sophie Starck on the topic of GANs for reconstruction, and Kevin Michalewicz on the follow-up of my work on Learnlets.
- Peer-reviewed 19 submissions for scientific conferences and journals.
Data Scientist
- Built data pipelines for smartphone and wearable data.
- Designed, implemented, deployed and maintained ad-hoc machine learning models for human activity detection.
- Presented research results to technical and non-technical teams.
Research Intern
- Benchmarked and implemented deep learning algorithms for epilepsy detection in EEG signals.
Data engineer and Data science Intern
- Implemented and maintained data pipelines for genomic literature meta-analysis.
- Developed a blood hormone level model.
Data science Intern
- Developed the full stack of web applications.
- Performed data analysis on the use of these web applications, as well as on social media data.
Projects
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Recent Publications
Courses
I gave the following courses:
- TA at IUT Orsay for the Python Intro course
- Lecturer for the PSL intensive ML week
Reviews
I have peer-reviewed for the following conferences:
- Eusipco 2019
- ISBI 2020
- Eusipco 2020
- Eusipco 2021
- NeurIPS 2022
- ICML 2023
- NeurIPS 2023
I have peer-reviewed for the following journals: