Statistical tools for exploring latent variation in microbiological and earth systems data
Education
- Postdoc, Computer Science, Mila / Université de Montréal
- PhD, Statistics, Stanford University
- MS, Statistics, Stanford University
- BS, Mathematics, Stanford University
Research Description
Modern sequencing, imaging, and spectroscopy technologies give a window into complex biological and ecological environments. However, exploring these data without getting lost remains a challenge — statistical methods can help chart out the territory. Our research group develops models and interfaces that help researchers summarize general patterns, spot interesting relationships, and articulate uncertainty in multi-omics contexts. We have worked with generative and latent variable models to better incorporate contextual knowledge and integrate complementary data sources. We regularly implement software packages that translate statistical and visualization theory into flexible and accessible tools for the research community.
Honors
- Jerome H. Friedman Applied Statistics Dissertation Award, 2018
- Ric Weiland Graduate Fellowship, 2016 - 2018