# About me

I am a researcher in the parietal team @ INRIA Saclay, since autumn 2019, working on unsupervised learning for time series and on deep learning methods applied to solving inverse problems.

My research interests touch several areas of Machine Learning, Signal Processing and High-Dimensional Statistics. In particular, I have been working on Convolutional Dictionary Learning, studying both their computational aspects and their possible application to pattern analysis. I am also interested in theoretical properties of learned optimization algorithms such as LISTA, in particular for the resolution of inverse problems.

I did my PhD under the supervision of Nicolas Vayatis and Laurent Oudre, in the CMLA @ ENS Paris-Saclay. My PhD focuses on convolutional representations and their applications to physiological signals. I continued working on unsupervised learning for time series with application to electrophysiological recordings during a 1,5 year in the Parietal Team. I am also involved in open-source projects such as joblib or loky, for parallel scientific computing.

### Latest publication and projects

Feb 2020,

*preprint /*

- transparent disk-caching of functions and lazy re-evaluation (memoize pattern)
- easy simple parallel computing

Starting 2017, I have been involved in Joblib development, first to integrate loky as a new multiprocessing backend and them as a regular developer for the library.

*python, multiprocessing, parallel computing*