John Storey develops statistics and machine learning methods, theory, and algorithms for high-dimensional data analysis problems in genomics and other areas of biology. From a variety of directions, he is 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 his statistics and machine learning 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.
John D. Storey