Welcome to my personal webpage! I'm a french PhD student working on Random Matrix Theory and Machine Learning at CEA (Alternative Energies and Atomic Energy Commission). I am also attached to the L2S (Laboratory of Signals and Systems) laboratory of Centrale-Supélec (part of the University of Paris-Saclay). I work under the supervision of Mohamed Tamaazousti (CEA) and Romain Couillet (Centrale-Supélec).
The bigdata era has driven the recent development of new algorithms and methods, often based on elementary principles allowing to handle large amounts of data. However, these large dimensional data impair the behavior of traditional methods that deserve to be revisited under the eye of more elaborate tools and methods. A better understanding of these methods in the bigdata regime indeed induces possibilities of improvements, thereby leading to the development of more efficient algorithms. The Random Matrix framework provides a powerful tool to understand and analyse the behavior of simple data models (such as the mixture of Gaussians model) in the large dimensional setting, which is naturally the case in the BigData paradigm. My PhD thesis will aim at going beyond the simple models hypothesis, to develop new methods that are more appropriate to practical datasets (structured data, images, etc.). Keywords: Random-Matrix-Theory, Machine-Learning, Deep-Learning, Image-Analysis.
Signal Processing tutorials for first grader engineering students. Temporal Representation, Frequency Representation, Fourrier Transform, Signal Filtering, Sampling, Audio and Image Signals Analysis etc. [Matlab]Centrale-Supélec - 2018
IEEE Computer Vision and Pattern Recognition 2018: Two papers.
International Conference on Learning Representations 2018: Two papers.
Advances in Neural Information Processing Systems 2017: Two papers.
Bibliography on Monte Carlo methods, variance reduction using functional control variables and reduction of dimension for the multi-dimensional case. [Linear Algebra / Probability and Statistics / Matlab]Scholar Project 2017
Application of machine learning methods (in particular, kernel methods) to the problem of physical humain activities classification, using heterogeneous data from position sensors. [Python / Scikit-Learn]Scholar Project 2017
Bibliography and implementation of a multi-target tracking algorithm, silhouette detection, SVM classification and identification of targets. [Python / Matlab]Scholar Project 2016
Implementation of a document prediction and classification algorithm based on neural networks. [Python]Scholar Project 2016