Core Faculty

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.

Developing statistics and empirical Bayes methods for contemporary biomedical applications

Elucidating bacterial genome evolution among plant and animal pathogens

Developing and applying genomic analysis methods to investigate the genetic complexity of adaptive trait evolution and reproductive isolation

Systems and synthetic biology approach to understanding and designing biology

Realizing fully integrated single-molecule systems for the understanding and fabrication of genomes

Innovating the development of powerful new technologies to drive biological research

Innovating massively parallel genomic technologies and applying to understand how the plasma membrane functions in all cells

Characterizing sex-differential risk mechanisms in autism and other neuropsychiatric disorders using functional genomics, human genetics and bioinformatics approaches