Tom Rossa pursued a dual degree in France at HEC Paris and ENSAE, gaining a multidisciplinary education in statistics, machine learning, and economics. During a research internship at Imperial College London, he worked on computational statistics, focusing on Langevin diffusions and their applications to sampling methods (MCMC). He then joined the MVA research master at ENS Paris-Saclay, specializing in machine learning and statistics.
Tom is joining the StatML programme to deepen his knowledge, particularly in Bayesian statistics and methods, Bayesian experimental design, and causality, with applications to economics, and healthcare. He has a strong appetite for interdisciplinarity and a curiosity to learn across multiple fields. Outside of academia, he enjoys sports—especially basketball—astronomy, and urban and electronic music
