CICADA - Seminario con Gonzalo Mateos

El viernes 4 de agosto a las 10:30 en el salón 101 de la Facultad de Ingeniería

El seminario es organizado en conjunto entre CICADA y el Grupo de Probabilidad y Estadística de la Udelar.

La charla con Gonzalo será en español

Title: Fast Topology Identification from Smooth Graph Signals

Abstract: In this talk we consider network topology identification subject to a signal smoothness prior on the nodal observations. A fast dual-based proximal gradient algorithm is developed to efficiently tackle a strongly convex, smoothness-regularized network inverse problem known to yield high-quality graph solutions. Unlike existing solvers, the novel iterations come with global convergence rate guarantees and do not require additional step-size tuning. Reproducible simulated tests demonstrate the effectiveness of the proposed method in accurately recovering random and real-world graphs, markedly faster than state-of-the-art alternatives and without incurring an extra computational burden. 

Bio: Gonzalo Mateos earned the B.Sc. degree from Universidad de la Republica, Uruguay, in 2005, and the M.Sc. and Ph.D. degrees from the University of Minnesota, Twin Cities, in 2009 and 2011, all in electrical engineering. He joined the University of Rochester (UofR), Rochester, NY, in 2014, where he is currently an Associate Professor with the Dept. of Electrical and Computer Engineering, as well as an Asaro Biggar Family Fellow in Data Science. He is also the Associate Director for Research at the UofR’s Goergen Institute for Data Science.  During the 2013 academic year, he was a visiting scholar with the Computer Science Dept. at Carnegie Mellon University. From 2004 to 2006, he worked as a Systems Engineer at Asea Brown Boveri (ABB), Uruguay. His research interests lie in the areas of statistical learning from complex data, network science, decentralized optimization, and graph signal processing, with applications in brain connectivity, dynamic network health monitoring, social, power grid, and Big Data analytics.

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