My research interests lie in Spatial Statistics, Bayesian Statistics, Causal Inference, and Machine Learning, with applications in Epidemiology and Biology.
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Epidemiology
My current research focuses on advancing spatial methods for applications in epidemiology. I recently developed a localized spatiotemporal random forest model to study COVID-19 dynamics across US counties, capturing how epidemiological, demographic, and environmental drivers shift across regions and periods. I also collaborated in applying a spatial Bayesian distributed lag non-linear model to quantify uncertainty and evaluate delayed climatic effects, such as temperature, on Salmonella risk across New South Wales local health districts.
Biology
Looking ahead, I aim to develop spatial and causal methods for applications in biology through two complementary directions. First, as an enthusiast in spatial omics, I want to develop spatially-aware ML models that encode tissue architecture using graph-based and Gaussian process approaches. These models will learn cell-cell interactions, microenvironmental niches, and multiscale patterns to improve tasks such as cell-state mapping, boundary detection, and pathway activity localization. Second, I also want to explore causal ML to better understand the causative role of genes in complex processes such as gene regulation, disease progression, and cellular development.
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