Wenwen Zhang

Wenwen Zhang is an assistant professor of Public Informatics at the Bloustein School. Her research centers on understanding the social and policy impacts of emerging transportation technologies, such as autonomous vehicles, e-scooters, and ride-hailing.

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Visualizing the Spread of COVID-19 in the United States

The New York Times created a public dataset tracking the spread of COVID-19 by county since the beginning of the year. This national, county-level dataset is a valuable resource for researchers and is a result of the collaborative effort journalists working across the country compiling information from public health authorities and other sources. As noted […]

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COVID-19 and Movement Patterns in New Jersey

Do you feel as if you have been at home for a long time during the COVID-19 pandemic? If yes, you are not alone. New Jersey instituted a stay-at-home policy on Saturday, March 21. As a part of a multiphase reopening process, NJ ended maximum restrictions and moved to Stage One Reopening on May 18, […]

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CES-COVID19

The RUCI Lab’s project “CES-COVID19” is a cyberinfrastructure that captures real-time or close-to real-time data on social and economic effects of COVID-19.

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Gavin Rozzi

Gavin Rozzi joined the RUCI Lab in March 2020. He has conducted data analysis for polls and co-authored a research report on the underground construction on New Jersey’s economy.

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Medical and Health/Upstream Determinants of Health Informatics

The field of healthcare administration is concerned with better patient care and healthcare delivery. The RUCI Lab is interested in using emerging technologies and novel sources of data for analytics to improve upstream determinants of health such as transportation and air quality, as well as quality of care. Some of the work relates to using […]

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Social media analytics and event detection in transportation and cities

Social media data offers great potential to understand trends and patterns, real-time monitoring of disruptions and events, as well as sentiments, perceptions and beliefs. Yet, almost all social media data, whether it is microblogging data, or website data, are noisy and a mixture of text and images, and with provider restrictions and sampling restrictions to access.

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