I’m a Research Fellow in Computational Mathematics at the Flatiron Institute, a part of the Simons Foundation. My primary interest lies in statistics and machine learning methods. Much of my research is motivated by Bayesian modeling problems in pharmacometrics, epidemiology, and more.

My work bridges statistical methods, computation, and application through the development of probabilistic programing languages. I’m a core developer of the Bayesian inference software Stan, the co-creator of its pharmacometrics extension Torsten, and I have an ongoing collaboration with the TensorFlow Probability team.

I earned a Ph.D. in Statistics from Columbia University in 2022 and a B.Sci. in Physics from Yale University in 2015. You can find out more by browsing this website or looking at my CV.


  • StanCon 2024 will be held at Oxford University in the UK, September 12th - 14th!
  • This June, I’ll be teaching a course on Monte Carlo methods, as part of the Nordic Probabilistic AI school in Copenhagen, Denmark.
  • I was the invited guess for the podcast learning Bayesian statistics, hosted by Alex Andorra. Our episode: “Demystifying MCMC and Variational Inference”.
  • My interview for the Simons Foundation with science writer Mara Johnson-Groh is out. It provides an accessible overview of my research.

(last updated: March 2024)