My lab and I develop statistical methods, theory, and algorithms for high-dimensional data analysis problems. From a variety of directions, we are currently working on a comprehensive framework to carry out statistical inference on high-dimensional data consisting of binary, count, or continuous measurements that are influenced by both known and unknown (latent) sources of variation. Most of our statistics research is directly motivated by and applied to problems in genomics and other areas of modern high-throughput quantitative biology. Examples include studies involving genome sequences of individuals from structured populations, genome-wide gene expression profiling measurements from next generation sequencing, and complex clinical genomics studies.
Specific projects in the lab include:
Please see some of our recent publications to learn more about our research.