Yazid Janati is a third year PhD candidate in statistics at Télécom SudParis - Institut Polytechnique de Paris, advised by Sylvain Le Corff (LPSM, Sorbonne Université) and Yohan Petetin (CITI, Télécom SudParis).

His PhD work focuses on building new algorithms related to Monte Carlo methods and studying their theoretical properties. He is particularly interested in the interplay between MC and deep learning methods and is currently focused on importance sampling, trying to understand how to build efficient proposals through the minimization of divergence measures.

More information on his personal website.