Leonardo Santoro

Leonardo Santoro is a Postdoctoral Researcher at EPFL's Chair of Mathematical Statistics within the School of Basic Sciences. His academic appointments include current research roles at EPFL (2024–present) and a research stay at UC Berkeley (2023).

His research focuses on theoretical intersections of statistics and machine learning. Key interests include kernel methods for machine learning, nonparametric inference for high-dimensional and functional data, and optimal transport theory. His work addresses inverse problems through mathematical frameworks like covariance operators, transport flows, and diffusions.

His publication trends reveal specialization in statistical methodology development, particularly for complex data structures. Research outputs emphasize mathematical rigor in machine learning applications, with emphasis on geometric statistics and probabilistic modeling.

Professional trajectory includes doctoral training under Victor Panaretos at EPFL, preceded by masters studies at ETH Zurich and undergraduate education at Università di Trento. His technical profile demonstrates applied experience through an industry internship at Teralytics AG developing mobility models from telco data.