My research focuses on developing statistical methods for modern data settings where complexity can obscure structure and traditional tools become unreliable. I am particularly interested in problems involving high dimensionality, complex dependence, and non-standard data representations.
Methodological themes
- Nonparametric and graph-based methods for high-dimensional data
- Change-point analysis, anomaly detection, and monitoring
- Permutation-based inference
- Design-stage methods for covariate balance and comparability
- Network-structured and non-Euclidean data analysis
Applied motivation
- Public health and disease surveillance
- Genetics and other data-rich scientific domains
Across these areas, I am interested in developing statistical tools that remain reliable as data become larger and more complex. I am always happy to discuss potential collaborations or student projects.