I’m a Research Fellow in Computational Mathematics at the Flatiron Institute, a part of the Simons Foundation. My primary interest lies in the development of statistical and machine learning methods with applications in the natural sciences. Much of my research is motivated by Bayesian modeling problems in pharmacometrics and epidemiology. I study Markov chain Monte Carlo and approximate Bayesian inference, as well as hybrids which combine the two paradigms.

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.


  • 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: July 2023)