Topic: Learning with Graphs

Next in our spring seminar series, we welcome Dr. Gonzalo Mateos, an Associate Professor from the University of Rochester.

Abstract:
This talk is broadly about learning from network data, which arises for instance 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. To provide additional motivation, I will start with 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, I 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.

You can see the presentation live at the UTSA Main Campus in the Student Union Mesquite room (2.01.24).

Those who cannot attend in-person are welcome to tune in virtually through Zoom: https://utsa.zoom.us/j/94807623288.