Network forecasting
This thrust focuses on developing new methods for the analysis of publicly available data in order to anticipate and/or predict significant societal events, such as political instability, humanitarian crises, disease outbreaks, economic instability, and devastating effects of natural disasters. We aim to develop data assimilation algorithms, forecasting algorithms, and data collection platforms for studies on human behavior, with deep exploration into foundational issues of measurement, construct validity and reliability, and dependencies within data.
Featured publications
YJMob100K: City-scale and longitudinal dataset of anonymized human mobility trajectories
Recent publications
Decoding how higher-order network interactions shape complex contagion dynamics
Effects of AI Feedback on Learning, the Skill Gap, and Intellectual Diversity
YJMob100K: City-scale and longitudinal dataset of anonymized human mobility trajectories
Featured project
The Data Forecasting Project focuses on developing new methodologies for forecasting by curating massive data sets from social media and mobility patterns. Epidemiological models will be built from tracking Infuenza-Like Illness (ILI) to detect early cases of ILI in small geographic regions; and from voter registration data collected from 1.7M Twitter handles from 86 countries of more than 500 elections. We use these data to develop a forecasting approach that combines digital indicators and mechanistic models. General formalizations of these forecasting models are applied to a wide range of behaviors including social movements, media consumption, and epidemiological prediction.
Associated faculty
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