Title: Data science with graphs: From social network analysis to autonomous driving
Abstract: A massive amount of data is being generated at an unprecedented level from a diversity of sources, including online social networks, cyber-physical systems, biological networks, urban traffic networks, 3D point clouds, and many others. These seemingly different types of data share a similar pattern: data are associated with complex and irregular structures. The necessity of analyzing such data has led to the birth of a unifying framework, data science with graphs. This framework offers a mathematically rigorous paradigm to analyze high-dimensional data associated with complex and irregular graph structures. It extends tools and concepts from classical signal processing and machine learning to the graph domain. In this talk, we will introduce this emerging framework and consider a series of graph-based theories and methodologies, including graph signal processing and graph neural networks. We will illustrate the power of the proposed methodologies with the applications to social network analysis and autonomous driving.
Time: June 17, 2020, 12:30pm – 1:00pm (eastern)