[gov3009-l] 11/1 Applied Stats; Michael Bronstein

Hi everyone! This week at the Applied Statistics Workshop we will be welcoming *Michael Bronstein*, Radcliffe Fellow at the Harvard Institute for Advanced Study. He will be presenting work entitled *Deep Learning on Graphs: Going Beyond Euclidean Data*. Please find the abstract below and on the Applied Stats website here <https://projects.iq.harvard.edu/applied.stats.workshop-gov3009>. As usual, we will meet at noon in CGIS Knafel Room 354 and lunch will be provided. See you all there! -- Dana Higgins *Title:* *Deep Learning on Graphs: Going Beyond Euclidean Data* *Abstract:* In the past decade, deep learning methods have achieved unprecedented performance on a broad range of problems in various fields from computer vision to speech recognition. So far research has mainly focused on developing deep learning methods for Euclidean-structured data. However, many important applications have to deal with non-Euclidean structured data, such as graphs and networks. Such data are becoming increasingly important in computer graphics and 3D vision, sensor networks, drug design, biomedicine, high energy physics, recommendation systems, and web applications. The adoption of deep learning in these fields has been lagging behind until recently, primarily since the non-Euclidean nature of objects dealt with makes the very definition of basic operations used in deep networks rather elusive. In this talk, I will introduce the emerging field of geometric deep learning on graphs, overview existing solutions and applications for this class of problems, and outline the key difficulties and future research directions.