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Email: yurulin (at) pitt.edu
I am an assistant professor at School of Information Sciences, University of Pittsburgh. In the past, I have worked as an assistant research professor at the College of Social Sciences and Humanities, Northeastern University. I have also been a postdoctoral research fellow at the Institute for Quantitative Social Science, Harvard University and College of Computer and Information Science, Northeastern University.
I am interested in studying social and political networks, as well as computational and visualization methods for understanding network data. My work has focused on large-scale community dynamics, rich-context (high-dimensional) social information summarization and representation. I have been using massive social media data and anonymized cellphone records (CDRs) to understand the collective responses with respect to political events and under exogenous shocks such as emergencies.
I am a computer scientist by training, and I identify myself as a computational social scientist as I concern more with questions like: "how would a society be informed?" "how do people share information, ideas and opinions in various contexts?" These questions have led me to explore analytical and computational techniques for mining heterogeneous, multi-relational, and semistructured data that can advance our understanding about structures in networked societies.
During my postdoctoral training, I have worked with colleagues from the disciplines of Physics and Social Sciences in the BarabasiLab and the LazerLab, where I worked with Dr. David Lazer. I received my Ph.D. in Computer Science from Arizona State University, with an interdisciplinary concentration in Arts Media and Engineering, where I worked with Dr. Hari Sundaram and Dr. Aisling Kelliher. My Ph.D. work focused on extracting human communities that collaborate around certain topics or shared media artifacts. I have proposed matrix and tensor based techniques for analyzing community structures and evolutions in time-varying heterogeneous social networks, and developed visualizations to support community discovery in the context of everyday social media use.here.