I am a Senior Researcher in the Nature + Computing group at Microsoft Research in Redmond. Previously, I was with Microsoft Research in Los Angeles, where I joined after completing my Ph.D. in the department of Computer Science and Engineering and the University of Washington.
My area of expertise is machine learning and applied statistics for computation biology, with a specific emphasis on viruses and other microbes. Much of my research has been focused on HIV evolution, though I'm also interested in microbial metagenomics as well.
If you're interested in the tools I've developed for HIV, please see our PhyloD web app.
For information on project PREMONITION, see the project page
Impact of pre-adapted HIV transmission May 2016
HIV adapts to our immune response. So what? That's been a surprisingly difficult question to answer, beyond very focused questions about very special epitopes. So we teamed up with Paul Goepfert of UAB, Eric Hunter of Emory, and a several other labs to answer the question. First, we built a model of HIV adaptation and trained it on 4000 people. Armed with this model, we looked at a number of data sets to see how adaptation predicts disease progression, then Paul's designed a series of functional studies to validate the results. Not only does HIV adaptation within a patient predict rapid disease progression, but infection by a pre-adapted virus–a virus that already carries mutations specific to the new host–results in dysfunctional immune responses and rapid progression. Thus suggests HIV is finding universal holes in our immune response, and bolsters claims that we should be pursuing vaccines that target regions of the virus that are relatively conserved. Moreover, these results highlight the interactions between host and virus genetics, explaining many of the "protective" effects commonly attributed to HLA alleles, and confounding estimates such as the "heritability" of viral load that ignore such interactions. Read more… or Watch the video…