I am a postdoctoral scholar working with Xin He and Matthew Stephens in the department of Human Genetics at the University of Chicago. I received my PhD in Bioinformatics and Computational Biology from UNC Chapel Hill. My experience is in statistical genetics, and my research includes developing statistical approaches for genetic association and analyzing gene expression in model organisms and humans. I have expertise in causal inference, Bayesian nonparametric approaches, Bayesian model selection, and generalized linear models, with implementation in the R and Stan statistical computing languages.
I am interested in developing and applying cutting-edge methods to analyze large and complex biomedical datasets. My current research involves developing and applying statistical methods for prioritizing causal genes and tissues in transcriptome-wide association studies.
You can see my full list of publications on Google Scholar. Here are some of the projects I've worked on recently:
bmediatR is a Bayesian model selection approach for mediation analysis. It explores different causal relationships between a dependent variable (Y), an independent variable (X), and a potential mediator variable (M). Unlike the Sobel test, this approach distinguishes between partial and complete mediation, and it accommodates grouped predictors in X. Grouped predictors are useful for haplotype-based analyses and other cases when the independent variable is multidimensional, such as modeling multiple variants or non-additive effects. The manuscript for this approach is currently under development.
TIMBR is a Bayesian nonparametric approach for haplotype-based genetic association. It partitions haplotypes into a potentially smaller number of functional alleles with shared trait effects. This improves haplotype effect estimation and provides useful information about the number of causal variants at a quantitative trait locus. TIMBR partitions haplotypes using a Chinese restaurant process (CRP) and, by leveraging its relationship to the coalescent, generalizes the CRP to allow for tree-structured haplotype relatedness. The manuscript for this approach was highlighted in the December 2020 issue of Genetics.
Here's my CV.
I'm happy to provide additional details about my experience. References are available upon request.