I’m a PhD student at the University of Washington’s Department of Biostatistics. Broadly, my research aims to integrate rigorous Bayesian statistical methods with anti-racist theoretic frameworks to study the health of nonwhite, transgender, and HIV+ populations in the United States. I received a BA in Statistics from Macalester College in May 2022.

Currently, I am researching and developing machine learning (ML) techniques that maximize model fairness and minimize differential misclassification by race in suicide risk prediction models under Dr. Yates Coley and in collaboration with the Kaiser Permanente Washington Health Research Institute’s Biostatistics Division. Thus far, my work has primarily centered on developing, applying, and testing gradient-boosted machine learning models with re-sampling procedures to address model limitations caused by outcome imbalance.

While at Macalester, I had the pleasure of working with the University of Minnesota’s fabulous Division of Epidemiology and Clinical Research on multiple projects concerning the epidemiology of pediatric cancer. Broadly, I worked on the application of admixture mapping, gene expression analysis, and survival modeling to study racial and sex-based disparities in pediatric cancer incidence and outcome.

Outside of research, I love making digital art, drinking monster, making spotify playlists, and reading critical theory. My greatest passion, however, is being silly and having fun :)