My program of research focuses on advancing probabilistic inferences in geospatial data analysis. I am particularly interested in Bayesian spatial and spatiotemporal modeling and its applications in exploring inequities of urban environmental exposures and their associations with health. I also apply (Bayesian) machine learning as well as computational methods including Markov chain Monte Carlo (MCMC) and Integrated Nested Laplace Approximation (INLA) to efficiently analyze large volumes of heterogeneous geographical data.
GEOG 281 The World and Big Data
GEOG 4/594 Spatial Analysis
GEOG 490 GIS and public health
GEOG 607 GIS seminar: Topics in Spatial and Spatio-temporal Analyses