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
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.