A core principle in population genetics is that genetic variance is partially responsible for variance in cellular to organism-level traits. In natural and agricultural systems, this variation permits flexibility and can be probed to improve resilience in the face of a changing environment. In medical research, genetic variation is often studied to root out the basis of disease. In all cases, studying the link between genetic variantion and trait variation is complicated by interactions between genetic, epigenetic, and environmental components. To make an impact in this challenging and fast pace field, we need collaborative and interdisciplinary teams that can generate cleaver and creative solutions.

I am a computational biologist with 8 years of experience developing reproducible bioinformatic and machine learning workflows and software to model the relationship between genetics and traits. As a postdoctoral researcher at St. Vincent’s Institute of Medical Research in Melbourne, Australia in Dr. Davis McCarthy’s lab I am working on functional genomics studies in single-cells. I completed my Ph.D. in Plant Biology in 2019 at Michigan State University in the Shiu Lab. My dissertation was on training and interpreting machine learning models for genomic prediction (a.k.a. predicting traits like yield and flowering time from genetic information) and for improving our understanding of the cis-regulatory code regulating how plants respond to combined heat and drought stress at the gene expression level. I earned my B.A. in Molecular Biology and Biochemistry with a minor in Environmental Studies from Middlebury College in 2012.