My primary research interests are to develop new statistical models, develop efficient algorithms and robust software to fit those models, and to apply those models to identify and characterize the biological processes driving human development and disease..
Currently, I am working on methods to analyze single-cell genomic data and gain insight into the interplay between stochastic gene expression, cell type/state, and genetic variation at quantitative trait loci.
Previously, I developed methods to query how genetic variation which does not alter protein-coding sequences contributes to human disease. We integrate epigenomic information from the ENCODE and Roadmap Epigenomics consortia to identify cell-type–specific enhancers enriched for disease associated variants, and additional information about the transcriptional regulatory network to dissect their mechanistic role in psychiatric, metabolic, and autoimmune disorders. we then develop efficient Bayesian methods to understand the architecture of common diseases: how many causal genetic variants are there, where in the genome do they reside, and how should we design studies to more rapidly discover them in the population?
Separating measurement and expression models clarifies confusion in single cell RNA-seq analysis.bioRxiv, 2020.
A simple new approach to variable selection in regression, with application to genetic fine-mapping.Journal of the Royal Statistical Society: Series B (Statistical Methodology), 2020
Discovery and characterization of variance QTLs in human induced pluripotent stem cells.PLOS Genetics, 2019
Multi-tissue polygenic models for transcriptome-wide association studies.biorXiv, 2017 (in revision) *Equal contribution
Integrative analysis of 111 reference human epigenomes.Nature, 2015.