Learning with graphs
ECE 595 Seminar Series
November 26, 2024
11:00 AM - 12:00 PM
Learning with graphs
Speaker: Gonzalo Mateos, University of Rochester
Abstract: This talk is about learning from network data, which arises with applications involving online social media, recommendation systems, transportation, and network neuroscience. By fruitfully exploiting the inductive biases in relational data, graph neural networks (GNNs) have attained unprecedented performance in various machine learning tasks, including node/graph classification, link prediction, and graph generation. Mateos will present a user-friendly and didactic introduction to graph signal processing. The goal is to establish the foundations and basic concepts that will be useful to introduce graph GNNs in an intuitive way. After discussing architectures and key properties that make GNNs the model of choice when it comes to learning from relational data, Mateos will highlight several success stories of GNN-based learning for Amazon’s recommendation system, Google Maps navigation, antibiotic discovery, and our own work on explainable brain age prediction.
Speaker bio: Gonzalo Mateos earned his BS degree from Universidad de la Republica, Uruguay, in 2005, and his MSc and PhD degrees from the University of Minnesota, Twin Cities, in 2009 and 2011, respectively, all in electrical engineering. He joined the University of Rochester, Rochester, NY, in 2014, where he is currently an associate professor of electrical and computer engineering, and computer science (secondary appointment) at the University of Rochester's Goergen Institute for Data Science, where he also serves as the associate eirector for research . Mateos also was the Asaro Biggar Family Fellow in Data Science (2020-23). 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 complex data, network science, decentralized optimization, and graph signal processing, with applications in brain connectivity, causal discovery, wireless network monitoring, power grid analytics, and information diffusion.
Faculty host: Daniela Tuninetti, danielat@uic.edu
Date posted
Nov 25, 2024
Date updated
Nov 27, 2024