santiago segarra, assistant Professor
Understanding networks and networked behavior has emerged as one of the foremost intellectual challenges of the 21st century. While we obviously master the technology to engineer transformational networks — from communication infrastructure to online social networks — our theoretical understanding of fundamental phenomena that arise in networked systems remains limited. My goal is to combine network science and signal processing in order to leverage the structure of networks to better understand data defined on them. In this context, the term Data Science for Networks can be understood as a joint effort to understand both network structures and network data. I will introduce the fundamental building blocks of graph signal processing (GSP) as a toolbox to study network data, and showcase its broad applicability by delving deeper into how networks can help us to understand Shakespearean authorship.