I am a PhD student in computer science at Princeton, where I work on methods for machine learning. I am particularly interested in flexible and tractable Bayesian modeling. I am supervised by Barbara Engelhardt and also work with Ryan Adams.
I value clear scientific communication and maintain a blog on topics in and around machine learning. In a previous life, I was an architect. Changing careers, I worked as a technician in a biophysics lab while teaching myself programming. 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 for Tudor in London.