Yu-Ru Lin

Time 2020-10-06 20:05:50

Web Name: Yu-Ru Lin

WebSite: http://www.yurulin.com

ID:72751

Keywords:

Yu,Ru,Lin,

Description:

Associate Professor School of Computing and Information Secondary appointments: Intelligent Systems Program (ISP) & Political Science Department University of Pittsburgh Office: 710 IS Bldg, University of Pittsburgh 135 North Bellefield Ave., Pittsburgh, PA 15260 Phone: 412-624-9470 Fax: 412-624-5231 Email: yurulin (at) pitt.edu Recruiting: I am looking for highly motivated students who have strong interest in pursuing research careers in the areas of Data Science, Network Science, and Computational Social Science. See more information at PICSO LAB. I am an associate professor at School of Computing and Information, University of Pittsburgh. 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, high-dimensional (rich-context) social information summarization and representation. I have been using massive social media data and anonymized cellphone records to understand the collective responses with respect to political events and under exogenous shocks such as emergencies. I lead the PITT Computational Social Dynamics Lab (PICSO LAB). I am a computer scientist by training, and a computational social scientist working on 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. I was a postdoctoral research fellow at the Institute for Quantitative Social Science, Harvard University and College of Computer and Information Science, Northeastern University. 06/2020 Paper accepted to appear in ACM Transactions on Interactive Intelligent Systems (TiiS): "PolicyFlow: Interpreting Policy Diffusion in Context." This new visualization combines text and network analyses to help understand the diffusion of laws and regulations across political boundaries in the US. 03/2020 I am co-chairing the KDD Humanitarian Mapping Workshop, to be held on August 24, 2020. We look forward to your participation! 01/2020 I'm honored to serve on the Pittsburgh Task Force on Public Algorithms. The task force, chaired by former U.S. Attorney David Hickton, will examine potential bias in algorithms and automated systems used by governments. Check out the recent news media coverage on this effort. 08/2019 Paper accepted to appear in Applied Network Science: "Learning Embeddings for Multiplex Networks Using Triplet Loss."

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