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

Thesis title: Random matrix theory, a domain with various widesparead applications in mobile communications and lately in signal processing at large, is now in a state of maturity allowing one to handle more complex scenarios of deep importance to the BigData paradigm, especially those involving large dimensional graphs. In this thesis, we will study spectral clustering based methods for community detection in graphs as well as state changes in random graphs. The envisionned applications are numerous, so we will need to develop a whole framework of tools to handle these systematically. A second part of the thesis will deal with the study of echo-state neural networks, that are fundamentally based on the random nature of the underlying neural network. The outcome of the thesis is to allow to be a main driver of the random matrix research for large dimensional graphs and to be a leader in the related applications of these tools, thanks to the development of yet to be discovered original methods.

Hafiz TIOMOKO ALI was born in Cotonou, Benin. He received an engineering degree in Telecommunications from Institut des télécommunications d’Oran (Algérie) and a Research master in Advanced Wireless Systems from Ecole Normale Supérieure de Cachan (France) in 2015. He worked during his master thesis on "The performance analysis of spectral community detection algorithms on large random graphs" with Romain COUILLET in the chair LANEAS. He is working since October 2015 on his PHD topic "Random Matrix Theory and Large Dimensional graphs" under the supervision of Romain COUILLET. His research interests are related to signal processing, machine learning, graph theory and random matrix theory.