My research interests are at the interface of mathematical statistics and new technology-generated data. I employ a combination of statistical theory and computational tools that I have mastered during graduate and postdoctoral training with collaborators to knowledge discovery.

Applied statistical research: statistical methods for multiple measurements made at the genome-level at multiple gestational age from pregnant women.

Theoretical research: approximation theory for learning dependency in high-dimensional data.

Statistical Methods for Multi-domain Data

Multi-table statistical methods

Statistical Methods for Microbiome Data

Statistical Methods for 16S and shotgun metagenomics data.

Microbiome Studies

Analyzing microbiome genome-level data and reproducible research.

Approximation theory in statistics

Saddlepoint-based bootstrap method (SPBB), multivariate SPBB, extension of empirical saddlepoint approximation for right-censored data, bivariate saddlepoint approximations.