Multimodal AI for Public Health
We develop machine-learning methods that integrate heterogeneous sources of health, behavioral, mobility, environmental, and digital data. Our goal is to uncover population-level signals that improve public health surveillance, prediction, and understanding.
Featured Projects
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PandemicLLM
PandemicLLM integrates epidemiological time series, public health policies, and genomic surveillance data within a large language model for population-level disease forecasting.