Mining Connections

Reihaneh Rabbany - Postdoctoral Fellow, School of Computer Science, Carnegie Mellon University

March 14, 2018, 10 a.m. - March 14, 2018, 11 a.m.




Connections are ubiquitous in different domains, from information diffusion channels in social networks to infection propagation routes in hospitals. To understand the interconnected world around us, we need tools that integrate three principal elements: connection, content, and time. In this talk, I will discuss bringing together techniques from network science, machine learning, and data mining towards integrated tools for mining dynamic interconnected data. As a step in this direction, I will demonstrate my works on narrowing the gap between content-based clustering and connection-based community detection, and their extensions to quantify mixing patterns in social networks. Throughout the talk, I will give examples from the broader impacts of this research in computational social science, combating online human trafficking, and educational data mining.


Reihaneh Rabbany is a Postdoctoral fellow at the School of Computer Science, Carnegie Mellon University (CMU). She completed her Ph.D. in Computing Science Department at the University of Alberta, as a member of Alberta Ingenuity Center for Machine Learning. Her research is at the intersection of network science, data mining and machine learning, with a focus on analyzing interconnected data. She has contributed to more than 20 peer-reviewed research papers. She has been selected as a top female graduate in the fields of electrical engineering and computer science in the 2016 Rising Stars program. As a postdoctoral fellow, she is working on real-world projects from different areas, including public health, fraud detection, intelligence and law enforcement.