Bayesian Spatial Modeling

My research in spatial modeling focuses on developing statistically rigorous and computationally efficient methods to analyze complex spatial and spatial-temporal data. Motivated by applications in environmental science and spatial transcriptomics, I work on scalable Bayesian and Gaussian process-based models that capture spatial structure and quantify uncertainty. This includes tools for handling large-scale satellite and sensor data with missingness, as well as statistical frameworks to guide experimental design in high-throughput spatial genomics. A key focus is enabling interpretable and robust inference across diverse spatial domains under real-world constraints.