In this talk, we present our theoretical results recently developed around various types of applications. These results cover asymptotic and non-asymptotic analysis, functional limit theorems, concentration inequalities, and Malliavin calculus techniques elaborated for several classes of stochastic processes. We showcase through concrete examples the role of these results in taking up various challenges at the cutting-edge of modern applied probability.
Ahmed Kebaier is Associate Professor in Applied Mathematics at University Sorbonne Paris Nord. He is a member of the Mathematical laboratory LAGA and the CERMICS research center at Ecole des Ponts Paris Tech.
His main research interests are related to probability with applications to numerical approximations, statistics, quantitative finance, green energy production, stochastic optimization, and learning. His research works are supported by the french programs: Chaire Risques Financiers and the Laboratory of excellence LABEX MME-DII.