معرفی
Guillermo Durand is an Associate Professor at Université Paris-Saclay, based at the Laboratoire de Mathématiques d'Orsay. His research focuses on multiple testing methodologies, particularly in controlling false discovery rates (FDR) and their applications to biological data. He also explores conformal prediction and broader areas of mathematical statistics.
Education: PhD in Applied Mathematics from Sorbonne University (2015-2018), Master 2 in Mathematics for Life Sciences from Université Paris-Sud, and a diploma from École Polytechnique (X). Professional experience includes a data scientist role at Intelligent Locations (2018-2021) and research internships at Sanofi and the University of Montreal.
Key contributions include the R packages sansSouci and DiscreteFDR, which address multiple testing challenges. He frequently presents on topics like FDR control and post hoc inference at academic seminars and conferences.
Teaching materials are detailed in his CV (in French), covering mathematical statistics and measure theory. His work bridges theoretical statistics with practical applications in genomics and healthcare logistics.
