Our lab develops computational methods for microbiome data analysis, with a focus on extracting meaningful biological signals from noisy, sparse, and compositional measurements. Microbiome data present unique statistical challenges due to high dimensionality, complex dependence structures, and sensitivity to technical variation. We design and apply advanced computational approaches to address these challenges and improve robustness, interpretability, and reproducibility.

Our goal is to translate microbiome data into actionable biological insight. By building methods that better capture microbial community structure and dynamics, our research supports applications in disease diagnosis, therapeutic development, and personalized patient care, helping bridge the gap between microbiome measurement and clinical impact.