Dr. Julia Fukuyama, Fred Hutchinson Cancer Research Institute: Using Phylogenetic Information to Understand the Microbiome

Seminar | March 1 | 3-4 p.m. | 125 Li Ka Shing Center

 Center for Computational Biology, Electrical Engineering and Computer Sciences (EECS)

Studies of the microbiome, the complex communities of bacteria that live in and around us, present interesting statistical problems. In particular, bacteria are best understood as the result of a continuous evolutionary process and methods to analyze data from microbiome studies should use the evolutionary history. Motivated by this example, I describe adaptive gPCA, a method for dimensionality reduction that uses the evolutionary structure as a regularizer and to improve interpretability of the low-dimensional space. I also discuss implications for interpretable supervised learning incorporating both the phylogeny and variable selection.

Julia Fukuyama is currently a postdoctoral research fellow in Computational Biology at the Fred Hutchinson Cancer Research Center. She obtained her PhD in Statistics at Stanford University, where she developed a set of multivariate methods for integrative analysis of abundance and phylogenetic data for the microbiome. Her postdoctoral work has been in computational immunology, focusing in particular on B cell repertoire sequencing. She also holds a BS in Biology from Yale University, which informs her interest in methods that help us make sense of complex, high-dimensional biological data.

Website: jfukuyama.github.io