- Associate Professor of Electrical and Computer Engineering
- Associate Professor of Computer Science
- Asaro Biggar Family Fellow in Data Science
PhD, University of Minnesota, 2012
726 Computer Studies Building
Gonzalo Mateos joined the faculty of the Department of Electrical and Computer Engineering as an assistant professor in September 2014. He is also a member of the Institute for Data Science and has a secondary appointment in the Department of Computer Science.
Gonzalo Mateos was born in Montevideo, Uruguay, in 1982. He 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, Rochester, NY, in 2014, where he is currently an Associate Professor in the Department of Electrical and Computer Engineering, as well as a member of the Goergen Institute for Data Science and Computer Science. During the 2013 academic year, he was a visiting scholar with the Computer Science Department 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 Big Data, network science, decentralized optimization, and graph signal processing, with applications in dynamic network health monitoring, social, power grid, and Big Data analytics. He currently serves as Associate Editor for the IEEE Transactions on Signal Processing, the IEEE Transactions on Signal and Information Processing over Networks, and is a member of the IEEE SigPort Editorial Board. Dr. Mateos received the NSF CAREER Award in 2018, the 2017 IEEE Signal Processing Society Young Author Best Paper Award (as senior co-author), and the Best Paper Awards at ICASSP 2018, SSP Workshop 2016, and SPAWC 2012. His doctoral work has been recognized with the 2013 University of Minnesota's Best Dissertation Award (Honorable Mention) across all Physical Sciences and Engineering areas.
- Statistical Signal Processing
- Machine Learning
- Decentralized Optimization
- Graph Signal Processing