The doctoral thesis is titled: 'False positive control for machine learning algorithms and applications', focusing on the use of multiple testing theory and its application to statistical learning methods, in connection with conformal inference. Research areas include simple and multiple test theories, conformal inference and its application in learning, and the study of empirical processes.
Education
Since October 2023, a doctoral student in statistics at LPSM under the supervision of Étienne Roquain and Stéphane Boucheron.
Background
Research interests include the theory of multiple testing and its application to statistical learning methods, especially in relation to conformal inference. Additionally, he is interested in stochastic processes (Brownian, Poisson, Hawkes...), stochastic differential equations, interacting particle systems, as well as harmonic analysis and the study of distributions (in the sense of Schwartz).