PhD, Statistics, Stanford University (advisor: Emmanuel Candès); MS, Physics of Complex Systems, Politecnico di Torino (Italy); BS, Engineering Physics, Politecnico di Torino.
Matteo Sesia is an assistant professor in the department of Data Sciences and Operation, at the USC Marshall School of Business. His research is focused on developing data science methods combining the power of machine learning algorithms with the reliability of rigorous statistical guarantees. While pursuing this goal, he enjoys dividing his time between theoretical, methodological, computational, and applied work. His doctoral research earned the Jerome H. Friedman Applied Statistics Dissertation Award from the Stanford Statistics Department in 2020.