Core Faculty

Anderson, Matt

Gene families, Intraspecies variation, genome evolution, population dynamics, microbiomes

Developing statistical methods for analysis of complex traits

Advancing mass spectrometer technology by developing instrumentation, chemistry, & informatics

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

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

Elucidating systems-level relationships between genotype-phenotype-environment in eukaryotic stress responses

The Hittinger Lab uses yeast carbon metabolism as a model for basic bioenergy, biomedical, and evolutionary research

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

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

A bioengineering approach to understanding fundamental biological and medical questions in microbes, specifically model and pathogenic yeasts.