My research focuses on developing statistical theories, computational methods, and scalable algorithms for complex biomedical and massive data problems.
The major research themes include:
- Mediation Analysis
- Statistical Inference for Massive Data
- Statistical Theory for DNA Data Storage
Research Areas
1. Mediation Analysis
Overview
Mediation analysis aims to understand how an exposure affects an outcome through intermediate variables.
My research develops statistical methods for high-dimensional, longitudinal, and survival mediation problems, with applications in biomedical studies, omics data, and causal inference.
Main Topics
- High-dimensional mediation analysis
- Quantile mediation analysis
- Longitudinal mediation models
- Survival mediation analysis
- Microbiome mediation analysis
2. Statistical Inference for Massive Data
Overview
Modern scientific applications generate massive-scale datasets that require new statistical methodologies balancing statistical efficiency and computational scalability.
Main Topics
- Optimal subsampling
- Scalable statistical inference
- Distributed estimation
- Online inference
- Massive survival analysis
3. Statistical Theory for DNA Data Storage
Overview
DNA data storage is an emerging technology that stores digital information using synthetic DNA molecules.
My research develops stochastic models and statistical inference methods for reliability analysis and optimization of DNA storage systems.