I’m a PhD student at Yale University in the Computer Science Department, advised by Alex Lew.

My interests are in designing tools and algorithms to improve, automate, and scale probabilistic reasoning. My research aims to develop ways to represent and compute with uncertainty in a principled way.

A non-exhaustive list of questions I’m thinking about:

• How can we detect ‘good’ and ‘bad’ MCMC chains?
• What’s the best way to combine discrete and continuous variables in Bayesian inference?
• How can we parallelize inherently sequential inference algorithms?
• What are composable and intuitive priors for 2D and 3D shapes?

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