HELLO, I'M
Malik TIOMOKO
PhD Student
About
MY BACKGROUND
Coming from a military training for officers coupled with a diploma in engineering science specialized in telecommunication, i switch recently to machine learning. I an actually doing a PhD in statistics in high dimensions especially the application of Random Matrix Theory to machine learning.
Education
WHAT I’VE LEARNED
Experience & Teaching
WHERE I’VE WORKED
2012-2017
Royal Military Academy– Brussels, Belgium
Bachelor & Master of Engineering Science, Telecommunication Options, Military officer training
2017-2018
Ecole Normale Supérieure- Cachan, France
Master in Machine learning (Mathématique , Vision, Apprentissage)
September 2018-January 2019
Introduction to Python programming, Bachelor 1, Université de Grenoble
January 2018-March 2018
Codalab challenge: 3rd place on AutoML Challenge
2018-2021
Université Paris-Sud, Paris/ Gipsa Lab, Grenoble
PhD: Transfer learning and Semi supervised learning in high dimensions using Random Matrix Theory
Skills & Languages
Random Matrix Theory
Machine learning
Matlab & Python
Management & Leadership
French
English
Publications
Interests
OUT OF OFFICE
R. Couillet, M. Tiomoko, S. Zozor, E. Moisan, "Random matrix-improved estimation of covariance matrix distances", (submitted to) Journal of Multivariate Analysis, 2018
M. Tiomoko, R. Couillet, S. Zozor, E. Moisan, "Improved Estimation of the Distance between Covariance Matrices", IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP'19), Brighton, UK, 2019
M. Tiomoko, F. Bouchard, G. Ginholac, R. Couillet, "Random Matrix Improved Covariance Estimation for a Large Class of Metrics", (submitted to) International Conference on Machine Learning, Long Beach, USA, 2019
M. Tiomoko, R. Couillet, "Random Matrix-Improved Estimation of the Wasserstein Distance between two Centered Gaussian Distributions", European Signal Processing Conference (EUSIPCO'19), A Coruna, Spain, 2019,
M. Tiomoko, R. Couillet, "Estimation of Covariance Matrix Distances in the High Dimension Low Sample Size Regime", IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP'19), Guadeloupe, France, 2019
M. Tiomoko, C. Louart, R. Couillet, "Large Dimensional Asymptotics of Multi-Task Learning", IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP'20), Barcelona, Spain, 2020
Codes of the paper available in https://github.com/maliktiomoko/
Guitar
Dance
Football/Squash/Tennis
Cooking
Reading