Ph.D. student under the direction of R. COUILLET

Thesis title: RMT4ML -- Random Matrix Methods for Machine Learning
Thesis abstract: The BigData challenge induces a need for classical machine learning algorithms to evolve towards large dimensional and more versatile learning engines. Recently, a new direction of research emerging from the field of random matrix theory (so far never used in this context) has opened a path into the understanding and improvement of several families of such techniques for large dimensional datasets (kernel spectral clustering, community detection on graphs, echo-state networks, etc.). The objective of the PhD is to extend those results to the even more fundamental machine learning tools that are support vector machines and semi-supervised learning methods. These challenging questions rely on technical aspects at the crossroads between random matrix theory and quadratic optimization, which are the main expertise of the co-advisors. It is expected that the output of the thesis will foster the new promising area of theoretical large dimensional machine learning.