MINDS Symposium on the Foundations of Data Science
Title: Graph Neural Networks in Collaborative Intelligent Systems
Abstract: Graphs are generic models of signal structure that can help to learn in several practical problems. To learn from graph data, we need scalable architectures that can be trained on moderate dataset sizes and that can be implemented distributedly. In this talk, I will draw from graph signal processing to define graph convolutions, and use them to introduce graph neural networks (GNNs). I will prove that GNNs are permutation equivariant and stable to perturbations of the graph, properties that guarantee scalability and transferability. I will also use these results to explain the advantages of GNNs over linear graph filters. I will then discuss the problem of learning decentralized controllers, and how GNNs naturally leverage the partial information structure inherent to distributed systems. Using flocking as an illustrative example, I will show that GNNs, not only successfully learn distributed actions that coordinate the team but also transfer and scale to larger teams.
Bio: Fernando Gama received the electronic engineer degree from the University of Buenos Aires, Argentina, in 2013, and the M.A. degree in statistics from the Wharton School, University of Pennsylvania, Philadelphia, PA, USA, in 2017. He is currently working towards the Ph.D. degree with the Department of Electrical and Systems Engineering, at the University of Pennsylvania. He has been a visiting researcher at TU Delft, the Netherlands, in 2017 and a research intern at Facebook Artificial Intelligence Research, Montreal, Canada, in 2018. His research interests are in the field of information processing and machine learning over network data. He has been awarded a Fulbright scholarship for international students and he has received a best student paper award at EUSIPCO 2019.