Model-based Trajectory Inference for Single-Cell RNA Sequencing Using Deep Learning with a Mixture Prior
Causal Inference for Heritable Phenotypic Risk Factors Using Heterogeneous Genetic Instruments
Data denoising with transfer learning in single-cell transcriptomics
Our main research interest is in developing statistical methods for cutting-edge bio-technologies and genetic problems. We currently work on problems in single-cell RNA sequencing, Mendelian Randomization and structural variation in the 3D genome. We also develop new statistical methods and theory for factor models and multiple hypotheses testing, with their applications in statistical genetics.