Developing statistics and empirical Bayes methods for contemporary biomedical applications
Concurrent with technological advances, the CGSI faculty develop new computational and statistical approaches to handle new data types, integrate with existing knowledge, and answer new questions in biology. Close collaboration among researchers spanning research focal areas is accelerating the pace of invention and discovery, including co-development of computational methods to partner with novel genomic techniques.
Innovating statistical methods for the analysis of high-throughput genomics data, including bulk and single-cell transcriptomic measurements
Developing statistical genomic methods, especially with regard to noncoding regulatory sequences and effects of their variation
Developing statistical and algorithmic approaches for the analysis of biological sequence data
Creating machine-learning methods for inferring networks of interacting genes, proteins, environmental variables, and phenotypes
Developing statistical methods for analysis of complex traits