About

I’m Vince Buffalo, a postdoc studying evolutionary genetics in the Kern and Ralph labs at the University of Oregon. I did my PhD with Graham Coop, in the Population Biology Graduate Group at UC Davis. Before graduate school, I worked as a bioinformatician, and I wrote the O’Reilly book Bioinformatics Data Skills teaching the skills I learned. I use statistics, probability, and computers to learn about evolution.

# Contact

You can contact me at the following email address with the poly-A tail removed (as an anti-spam measure): vsbuffaloAAAAA@gmail.com.

You can also find me on Twitter and Github.

Postdoctoral Scholar in Kern and Ralph Labs, University of Oregon, 2019-2020

Ph.D. in Population Genetics with Graham Coop, UC Davis, 2019

B.A. in Economics and Political Science, UC Davis, 2009
Minor in Statistics
Graduated with Highest Honors, Phi Beta Kappa, Dean’s List, Departmental Citation in Economics

Bioinformatics Data Skills: Reproducible and Robust Research with Open Source Tools, O’Reilly Media, July 2015

2014 - NSF Graduate Research Fellowship Program
2013 - NSF Graduate Research Fellowship Program, Honorable Mention
2013 - UC Davis Graduate Scholars Fellowship

Staff Computational Biologist, Jeffrey Ross-Ibarra Lab ─ 9/2013 to 9/2014

UC Davis Department of Plant Sciences Research projects: investigating genetic load in wild outcrossing teosinte populations with next-generation sequencing and GBS; development of imputation, phasing, and identification of rare alleles statistical genetics methods; short-read aligner assessment in highly paralogous genomes.

Bioinformatician, Jorge Dubcovsky Lab ─ 7/2013 to 9/2013

Research projects: developing novel software to extend single-individual haplotyping methods to phase homeologs in tetraploid wheat transcriptome data, development of a robust and sensitive RNA-seq bioinformatics protocol in tetraploid wheat.

Lead Statistical Programmer, Bioinformatics Core ─ 9/2009 to 7/2013

Research projects: developing next-generation sequencing quality pipeline, applying machine learning approaches (elastic net, lasso, sparse PCA) to RNA-seq expression data, RNA-seq statistical analysis, developed novel software to identify chromosomal translocations in post-chemotherapy patients using next-generation data.