Data Science for Networks

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.

About Santiago:
Dr. Santiago Segarra joined the Department of Electrical and Computer Engineering at Rice University as an Assistant Professor in 2018. He completed his PhD at The University of Pennsylvania in 2016 in the Department of Electrical and Systems Engineering with Prof. Alejandro Ribeiro as his advisor. After that, he spent almost two years as a Postdoctoral Research Associate at the Institute for Data, Systems, and Society at MIT. Before moving to the U.S. for his graduate studies, he  lived in Argentina, where he received his Industrial Engineering Bachelor Degree from the Instituto Tecnológico de Buenos Aires (ITBA) in 2011. Santiago’s research interests include network theory, data science, machine learning, and graph signal processing. His research focuses in networks, ubiquitous data structures that relate to numerous fields, including engineering, economics, sociology, and medicine.