About me

I am a PostDoc researcher in the parietal team @ INRIA Saclay, since spring 2018, working on convolutional dictionary learning.

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.

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 am also involved in open-source projects such as joblib or loky, for parallel scientific computing.

Latest publication and projects

Sparsity-based blind deconvolution of neural activation signal in fMRI
Hamza Cherkaoui, Thomas Moreau, Abderrahim Halimi, Philippe Ciuciu, May 2019, In proceedings of IEEE International Conference on Acoustic Speech and Signal Processing
In this work, we formulate the joint estimation of the HRF and neural activation signal as a semi blind deconvolution problem.
The estimation of the hemodynamic response function (HRF) in functional magnetic resonance imaging (fMRI) is critical to deconvolve a time-resolved neural activity and get insights on the underlying cognitive processes. Existing methods pro-pose to estimate the HRF using the experimental paradigm(EP) in task fMRI as a surrogate of neural activity. These approaches induce a bias as they do not account for latencies in the cognitive responses compared to EP and cannot be applied to resting-state data as no EP is available. In this work, we formulate the joint estimation of the HRF and neural activation signal as a semi blind deconvolution problem. Its solution can be approximated using an efficient alternate minimization algorithm. The proposed approach is applied to task fMRI data for validation purpose and compared to a state-of-the-art HRF estimation technique. Numerical experiments suggest that our approach is competitive with others while not requiring EP information.
Best Practices & Pitfalls in Applying Machine Learning to Magnetic Resonance Imaging slides
Sat May 2019, At Invited talk @ ISMRM, Montreal, Canada
In this talk, I cover the concept of generalization for supervised learning, with a focus on model selection and the importance of sample size.
In this talk, I cover the concept of generalization for supervised learning, with a focus on model selection and the importance of sample size.
Loky Aug 2018
The aim of this project is to provide a robust, cross-platform and cross-version implementation of the ProcessPoolExecutor class of concurrent.futures.
The aim of this project is to provide a robust, cross-platform and cross-version implementation of the ProcessPoolExecutor class of concurrent.futures. It features:
  • Deadlock free implementation: one of the major concern in standard multiprocessing and concurrent.futures libraries is the ability of the Pool/Executor to handle crashes of worker processes. This library intends to fix those possible deadlocks and send back meaningful errors.

  • Consistent spawn behavior: All processes are started using fork/exec on POSIX systems. This ensures safer interactions with third party libraries.

  • Reusable executor: strategy to avoid respawning a complete executor every time. A singleton pool can be reused (and dynamically resized if necessary) across consecutive calls to limit spawning and shutdown overhead. The worker processes can be shutdown automatically after a configurable idling timeout to free system resources.


python, multiprocessing, parallel computing