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:
Analysis of 12 large-scale CRISPRi screens reveals shared global responses, function-specific transcriptional effects, and a common hierarchy of responsive genes.
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.