Assistant Professor of Statistical Science
My research develops statistical and machine learning methods for identifying structure and mechanisms in high-dimensional and complex data, with a focus on variable selection, graphical modeling, and nonparametric inference. Much of my work centers on robust, stability-based approaches that recover relevant variables and data structures under minimal assumptions, providing theoretical guarantees while remaining computationally efficient. Although the methods I develop are broadly applicable, my primary applied focus is on biomedical and public health studies, where the goal is to uncover meaningful connections among molecular, clinical, and environmental factors that shape human health.