معرفی
Dr. Gonzalo Mateos is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Rochester and a member of the Goergen Institute for Data Science. His research focuses on graph signal processing, statistical learning from big data, network science, decentralized optimization, and their applications in social, power grid, and health analytics. Prior to joining Rochester, he was a visiting scholar in Carnegie Mellon University's Computer Science Department.
Mateos has developed innovative approaches for constructing graph Fourier transforms for directed networks and solving inverse problems in network analysis. His work on digraph signal processing includes methods for designing orthonormal transforms that capture spectral characteristics of directed networks. Additional research addresses source localization problems and topology inference from nodal observations generated by diffusion dynamics.
