I am currently a Bioinformatics and Data Scientist at GRAIL where I work on statistical methods and analyze large-scale cell-free methylation data for early detection of cancer. I recieved my PhD from the
Department of Human Genetics
at the University of Chicago advised by
John Novembre.
I also worked on collaborative projects
advised by Rina Foygel Barber
and Matthew Stephens.
My PhD was supported by the National Institutes of Health Genetics and Regulation Training Grant
and the National Science Foundation Graduate Research Fellowship
I'm interested in using statistics and machine-learning to solve problems
in large-scale genomic data. I develop statistical methods that are grounded in
real-world data analysis. In my work, I value domain expertise,
exploratory data analysis, data visualization, modeling uncertainty, and balancing
computational practicality and modularity with statistical rigor. My PhD
research broadly focused on learning about human history from patterns of genetic
variation. I developed statistical methods to help visualize and interpret population
structure in population genomic datasets. I have also contributed
to studies which aim to understand the genetic basis and evolutionary history
of human traits and diseases such as height and multiple sclerosis. I have experience working in large international
collaborations as well as small teams in both academic and industry settings.
Previously, I was an intern on the Computational Biology team at
Adaptive Biotechnologies
where I developed new statistical tools for analyzing immunosequencing
data from T-Cell Receptor (TCR) sequences. I received my Bachelor of Science
(with honors) in Biology from the University of Washington, where I was first
introduced to computational approaches to studying evolution and population
genetics through undergraduate research
in the Kerr Lab supported
by the Mary Gates Research Scholarship.