Mauro Maggioni is the Bloomberg Distinguished Professor of Data-Intensive Computation in the Whiting School of Engineering’s Department of Applied Mathematics and Statistics and the Krieger School of Arts and Science’s Department of Mathematics.
He is an expert in mathematical techniques for analyzing, modeling, and extracting information from large data sets in order to enable smarter machine learning algorithms and scientific discoveries. With a strong foundation in harmonic analysis and signal processing, Maggioni’s research is focused on the analysis of high-dimensional data, graphs, and networks. Specifically, he is developing algorithms that analyze and exploit the geometry of big data in order to train machines to learn and predict patterns in data. These hidden geometric structures in high-dimensional data are pervasive and appear in completely different data types, from images to text documents to trajectories of complex dynamical systems.
His work also has spanned the analysis of molecular dynamics data sets in order to find reduced representation for such high-dimensional stochastic systems, as well as for faster simulation and exploration of their configuration space. And, he developed algorithms in signal processing, in particular for the analysis of hyperspectral imaging, with applications ranging from digital pathology to target and anomaly detection. Subsequently, Maggioni’s findings have been published in top journals across the fields of pure and applied mathematics, machine learning, engineering, physical chemistry, engineering, and pathology.
Maggioni earned his bachelor of science and master of science degrees in mathematics from the Università degli Studi di Milano in 1999 and his doctorate in mathematics from Washington University in St. Louis in 2002. He was the Gibbs Assistant Professor of Mathematics at Yale University before joining Duke in 2006.
Maggioni was elected a fellow of the American Mathematical Society in 2013. He has received a Sloan Research Fellowship from the Alfred P. Sloan Foundation, a National Science Foundation CAREER Award, and the Vasil A. Popov Prize, which recognizes young mathematicians for distinguished research accomplishments in approximation theory and related areas of mathematics.