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
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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
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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,
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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
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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
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Codes of the paper available in https://github.com/maliktiomoko/
Guitar
Dance
Football/Squash/Tennis
Cooking
Reading