I am a PhD candidate in computer science at Princeton University where I am advised by and work with Barbara Engelhardt in the Engelhardt Group. I also collaborate with Ryan Adams in the Laboratory for Intelligent Probabilistic Systems. My interests are in statistical machine learning and scalable Bayesian inference. My current projects include randomized methods for latent variable modeling and memory-efficient online Bayesian inference for changepoint detection problems.
In a previous life, I studied architecture, first in Oklahoma and then at Yale. Bootstrapping a career in science and tech, I worked as a technician in a biophysics lab while teaching myself programming on nights and weekends. I then worked as a software engineer in New York City before pursuing a PhD. In the summer of 2019, I interned as quantitative researcher building fast online algorithms for Tudor in London.
I value clear scientific communication. I maintain a research blog on topics in and around statistical machine learning. As a volunteer, I have taught software development for Pursuit and helped write a computer science course with the Prison Teaching Initiative.
See my Google Scholar for a full list.