Research

Innovations in Computational
Approaches

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

Biostatistics & Medical Informatics

Developing statistical methods for analysis of complex traits

Mark Craven

Biostatistics & Medical Informatics, Computer Sciences

Creating machine-learning methods for inferring networks of interacting genes, proteins, environmental variables, and phenotypes

Colin Dewey

Biostatistics & Medical Informatics, Computer Sciences

Developing statistical and algorithmic approaches for the analysis of biological sequence data

Christina Kendziorski

Biostatistics & Medical Informatics

Innovating statistical methods for the analysis of high-throughput genomics data, including bulk and single-cell transcriptomic measurements

Sunduz Keles

Statistics, Biostatistics & Medical Informatics

Developing statistical genomic methods, especially with regard to noncoding regulatory sequences and effects of their variation

Michael A. Newton

Statistics, Biostatistics & Medical Informatics

Developing statistics and empirical Bayes methods for contemporary biomedical applications