• publication
    Wednesday, February 17, 2016

    We present a computational technique that answers the question "Which organisms are present in a given sample of of DNA from a microbial community, and at what relative amount" while simultaneously predicting the relatedness of novel (never-before seen organisms) in relation to known organisms. This relies on a mathematical technique referred to as sparsity-promoting optimization and relies on a technique similar to the Jaccard index.

  • Event
    Friday, May 12, 2017

    The Oregon State University Microbiome Initiative (OMBI) is a microbiome research and education program that centers on addressing pertinent problems in metagenomics.

  • publication
    Saturday, October 1, 2016

    In a network of interacting quantities (such as a food web), we examine how qualitative and quantitative predictions change when a quantity (such as the abundance of an organism or a set of organisms) is increased. This is quantified in terms of which model parameters cause the largest change in predictions.

  • publication
    Sunday, January 1, 2017

    In a very reproducible fashion, we assess a wide variety of computational techniques in metagenomics, including assembly (putting together pieces of genomes, called contigs, from short reads), binning (figuring out where the contigs came from), and taxonomic profiling (determining which organisms are present in a sample and at what relative amount).

  • publication
    Sunday, January 1, 2017

    Rapidly answers “why are these data sets different” by leveraging hierarchical/relatedness information. In short, we develop an algorithm to quickly compute the Unifrac distance by leveraging the earth mover's distance, prove its correctness, and derive time and space complexity characterizations.

Improving Min Hash for Metagenomic Classification

A presentation about work with Hooman Zabeti that used probabilistic data analysis to analyze metagenomic communities.

MTH 321: Introductory applications of mathematical software

This is a course that I created back in 2014 (that continues to run, typically in the Fall and Spring) to introduce students to Mathematica, Matlab, and LaTeX. In the future, I will be incorporating modules on Python and/or Julia. This hands-on course has been attended by over 80 undergradutes, as well as a handfull of graduate students and faculty as well!

I wrote a (~200 page) textbook to accompany this course which can be found here.

MetaPalette Summary video

Very brief explanation of how MetaPalette works.