Statistical Genetics · Computational Biology · General Statistical Methodology
We develop statistical methods and machine learning tools for modern biotechnologies and genetic data. Much of our work is motivated by problems in single-cell genomics and complex traits.
Current research directions include:
Our proposal on causal inference methods for genetics and genomics has been awarded an NIH R35 grant from the National Institute of General Medical Sciences.
Our paper on estimating cell-type proportions from bulk RNA-seq data using single-cell references has been accepted for publication in the Journal of the Royal Statistical Society Series B.
A new calibration method to boost efficiency and power in family-based GWAS using external summary statistics.
Our team achieved 3rd place in the 2025 Virtual Cell Challenge.