My research broadly focuses on two areas:
Health Informatics. I combine disparate data sources to estimate the effects of environmental exposures on individual-level health, applying computational methods in statistical learning, causal inference, and spatio-temporal analysis to large-scale clinical, demographic, and geographic data.
Public Health. I combine large-scale field survey data, syndromic surveillance data, and novel mobility data streams for equitable humanitarian crisis response, in the context of the War in Syria, the 2018 Floods in India, the Rohingya Refugee Crisis in Bangladesh, and climate-driven displacement in North Carolina.
My dissertation develops methodological frameworks to combine disparate spatiotemporal data with individual-level health data, and uses these linked datasets to estimate the effects of environmental exposures on health. Specifically, I focus on quantifying the cumulative burden of climate extremes on population displacement, disease, and death. I apply quantitative methods in causal inference, statistical learning, geospatial analysis, and informatics to large-scale clinical, demographic, and geographic data.
As a Graduate Research Assistant under
Dr. Emily Pfaff, I leverage causal machine learning (ML) methods within a target trial framework to assess comparative effectiveness of interventions as part of the
National Clinical Cohort Collaborative (N3C) and the NIH RECOVER Initiative. This work also includes algorithmic approaches for automated cohort identification and computable phenotype generation using graph analysis, natural language processing (NLP), and dimensionality reduction.
With
Dr. Barbara Entwisle at the
Carolina Population Center, I apply methods in computational demography to examine how electronic health record data can inform research on population mobility dynamics.
