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. Researchers with a primary focus in this area are listed below.
Karl Broman
Credentials: Biostatistics & Medical Informatics
Developing statistical methods for analysis of complex traits
Mark Craven
Credentials: Biostatistics & Medical Informatics, Computer Sciences
Creating machine-learning methods for inferring networks of interacting genes, proteins, environmental variables, and phenotypes
Colin Dewey
Credentials: Biostatistics & Medical Informatics, Computer Sciences
Developing statistical and algorithmic approaches for the analysis of biological sequence data
Sunduz Keles
Credentials: Statistics, Biostatistics & Medical Informatics
Developing statistical genomic methods, especially with regard to noncoding regulatory sequences and effects of their variation
Christina Kendziorski
Credentials: Biostatistics & Medical Informatics
Innovating statistical methods for the analysis of high-throughput genomics data, including bulk and single-cell transcriptomic measurements
Michael A. Newton
Credentials: Statistics, Biostatistics & Medical Informatics
Developing statistics and empirical Bayes methods for contemporary biomedical applications