Mauro Maggioni works at the intersection of harmonic analysis, approximation theory, probability, machine learning, spectral graph theory and statistical signal processing. He received his B.S. in mathematics from the Universitá degli Studi in Milan, Italy, and his Ph.D. in mathematics from Washington University in St. Louis. He was a Gibbs Assistant Professor of Mathematics at Yale University, was professor of mathematics, electrical and computer engineering, and computer science at Duke University, and in 2016 became a Bloomberg Distinguished Professor in the Departments of Mathematics and Applied Mathematics and Statistics, Johns Hopkins University. He received the Popov Prize in Approximation Theory in 2007, a National Science Foundation CAREER Award and Sloan Fellowship in 2008, was nominated Fellow of the American Mathematical Society in 2013, and is a member of the American Mathematical Society and the Society for Industrial and Applied Mathematics.