My research interests focus on developing efficient statistical learning methods to address a variety of biological problems and computational challenges in genomics and genetics. These challenges typically arise with the high-dimensional data generated by rapidly evolving sequencing technologies, e.g., single-cell RNA-seq (scRNA-seq), and spatially resolved transcriptomics (SRT). With the emergence of these large-scale data, I have been continually motivated to develop tailored statistical models to advance our understanding in cellular heterogeneity, tissue organization, and the underlying mechanisms of various types of cancers. In particular, my work centers on these important areas, (1) Powerful integrative statistical tests of association between gene sets and a phenotype; (2) Scalable Bayesian models and computational tools for high-throughput genomics data; (3) Effective and efficient optimization algorithms for multi-omics integrative analysis; (4) Genetic risk prediction analysis in large-scale bio-banks.
 

key words

1. Statistical genetics and genomics
2. Single-cell RNA-seq
3. Spatial Transcriptomics
4. Genetic risk analysis, Polygenic risk score
5. High dimensional data
6. Machine learning
7. Data integration

 

Biostatistics
Center for Computational Molecular Biology