Computational Analysis, Theory, and Statistics

CATS group photo

New insights into microbiome research can be realized by viewing old data with a new lens.  Several of our core Center members are developing novel computation and statistical techniques and tools to examine big data sets. 

To see an example of how our Center provides workshops on computational and statistical analyses of microbiome data sets, check out our GitHub page on previous and upcoming workshops, including our annual Kick-Start Bioinformatics workshop for incoming graduate students.

Faculty Working in Computational Analysis, Theory, and Statistics

Jordan Bisanz

Assistant Professor of Biochemistry and Molecular Biology

The interface of microbiology and bioinformatics, with approaches including genomics and metabolomics to investigate the interplay of diet, drugs, and the gut microbiome.

David Koslicki

Associate Professor of Computer Science and Engineering and Biology

Developing efficient algorithms to extract insight from high-throughput sequencing data.

Justin Silverman

Assistant Professor of Information Sciences and Technology

Statistical methods for the analysis of biomedical data (or any other interesting data/questions)

Guy Townsend

Assistant Professor of Biochemistry and Molecular Biology