After Andrew Hoegh graduated from Virginia Tech with a Ph.D. in statistics in 2016, he headed northwest to Bozeman, Montana, to join Montana State University as assistant professor of statistics. That same year, there was more good news for Hoegh. “Bayesian Model Fusion for Forecasting Civil Unrest,” which he co-authored with his DAC advisor Scotland […]
As a National Science Foundation trainee in the Urban Computing certificate program, Stacey Clifton, a Ph.D. student and sociology major, had the opportunity to attend the American Society of Evidence-Based Policing Conference last month. The conference, held in Philadelphia, Pennsylvania, provided valuable information and insights related to her research on police socialization and subculture, and […]
The long-term goal of this research is to understand, manage efficiently, and utilize dynamical mechanisms like propagation on large networks, occurring across natural, social, and technological systems. Understanding such processes enables us to manipulate them for our benefit. Propagation and networks have numerous applications in areas as diverse as public health and epidemiology, systems biology, […]
Jia-Bin Huang, an assistant professor of electrical and computer engineering and a DAC faculty member, has received a grant from the National Science Foundation’s Division of Information and Intelligent Systems to develop algorithms to capitalize on the massive amount of free unlabeled images and videos readily available on the internet for representation learning and adaptation. […]
While online communities play a crucial role in spreading conspiracy theories after catastrophic events like mass shootings or a terrorist attack, not much is known about who participates in these event-specific conspiratorial discussions or how they evolve over time. A new study by Tanushree Mitra, assistant professor of computer science and a faculty member at […]
Recent success in visual recognition relies on training deep neural networks (DNNs) on a large-scale annotated image classification dataset in a fully supervised fashion. The learned representation encoded in the parameters of DNNs have shown remarkable transferability to a wide range of tasks. However, the dependency on supervised learning substantially limits the scalability to new […]
This research employs social computing and human-centered approaches to understand the relationship between people and technology in the context of online news. It specifically investigates online news sources that propagate fabricated stories, explores how users engage with these sources on social media, and examines ways to nudge users to be more conscious consumers of online […]