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.
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03/2021
Honor to receive the Best Paper Award for 2020 in ACM Transactions on Interactive Intelligent Systems for our paper our "Policy Flow: Interpreting Policy Diffusion in Context"!
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02/2021
Grateful to receive Pitt Cyber Accelerator Grant and Pitt Momentum Fund for our research "Co-building Human and Digital Infrastructures Against Systemic Oppression."
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10/2020
Paper accepted to appear in COLING 2020: "Inflating Topic Relevance with Ideology: A Case Study of Political Ideology Bias in Social Topic Detection Models."
This work highlights the susceptibility of large, complex models to propagating the biases from input, leading to a deterioration of retrieval accuracy, and the importance of controlling for these biases.
As a way to mitigate the bias, we propose to learn a text representation that is invariant to political ideology while still judging topic relevance.
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09/2020
Grateful to share my reflection on a new article by National Geographic titled "Why our minds can't make sense of COVID-19's enormous death toll."
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08/2020
Paper accepted to appear in Humanities and Social Sciences Communications: "The dynamics of Twitter users' gun narratives across major mass shooting events." This article reveals how cross-cutting political talks about guns and gun policies happened on Twitter in a time of mass shootings.
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08/2020
Paper accepted to appear in Big Data Research: "Data-Driven Computational Social Science: A Survey." This article presents a survey on data-driven computational social science, focusing on the state-of-the-art research on human dynamics from three aspects: individuals, relationships, and collectives.
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07/2020
Paper accepted to appear in IEEE Journal of Biomedical and Health Informatics (JBHI): "StoCast: Stochastic Disease Forecasting with Progression Uncertainty." Here we propose a deep generative learning framework to deal with the uncertainty scenarios often seen in medical and other time-evolving datasets.
The code is available at our github page.
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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.
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04/2020
Paper accepted to appear in ICWSM 2020: "MimicProp: Learning to Incorporate Lexicon Knowledge into Distributed Word Representation for Social Media Analysis."
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03/2020
I am co-chairing the KDD Humanitarian Mapping Workshop, to be held on August 24, 2020. We look forward to your participation!
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01/2020
Paper accepted to appear in The Web Conference 2020: "Examining Protest as An Intervention to Reduce Online Prejudice: A Case Study of Prejudice Against Immigrants." This is the first empirical study examining the effect of protests on reducing online prejudice.
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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.
» More on earlier updates.
