Mohamed El Amine Seddik
Ph.D student in Random Matrix Theory and Machine Learning

About Me

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).

My Thesis

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.



A Kernel Random Matrix-Based Approach for Sparse PCA

Mohamed El Amine Seddik, Mohamed Tamaazousti, and Romain Couillet
ICLR 2019 (submitted)

PDF BibTex

Learning More Universal Representations for Transfer-Learning

Youssef Tamaazousti, Hervé Le Borgne, Céline Hudelot, Mohamed El Amine Seddik and Mohamed Tamaazousti
PAMI 2018 Journal of IEEE Transactions on Pattern Analysis and Machine Intelligence (submitted)
Journal - Impact Factor: 8.32
PDF BibTex Code (Github)

From Outage Probability to ALOHA MAC Layer Performance Analysis in Distributed WSNs

Mohamed El Amine Seddik, Viktor Toldov, Laurent Clavier, and Nathalie Mitton
WCNC 2018 International Conference on IEEE Wireless Communications and Networking, Barcelona, Spain, April 2018
PDF BibTex


Practical Signal-Processing at Centrale-Supélec

Signal Processing tutorials for first grader engineering students.
Temporal Representation, Frequency Representation, Fourrier Transform, Signal Filtering, Sampling, Audio and Image Signals Analysis etc.

With Elisabeth Lahalle
Centrale-Supélec - 2018

Reviewing Activities (Participation)

CVPR 2018

IEEE Computer Vision and Pattern Recognition 2018: Two papers.

ICLR 2018

International Conference on Learning Representations 2018: Two papers.

NIPS 2017

Advances in Neural Information Processing Systems 2017: Two papers.


Variance Reduction in Monte Carlo Methods

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]

Mohamed El Amine Seddik under the supervision of François Portier (Télécom ParisTech)
Scholar Project 2017

Human Activities Classifation using Kernel Methods

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]

Mohamed El Amine Seddik (Télécom ParisTech)
Scholar Project 2017

Multi-Target Tracking

Bibliography and implementation of a multi-target tracking algorithm, silhouette detection, SVM classification and identification of targets.
[Python / Matlab]

Mohamed El Amine Seddik under the supervision of Rita Noumeir (ETS Montréal)
Scholar Project 2016

Neural Networks

Implementation of a document prediction and classification algorithm based on neural networks.

Mohamed El Amine Seddik (ETS Montréal)
Scholar Project 2016