Long story short, I am a once and future physicist currently masquerading as a statistician in order to expose the secrets of inference that statisticians have long kept from scientists. More seriously, my research focuses on the development of robust statistical workflows, computational tools, and pedagogical resources that bridge statistical theory and practice and enable scientists to make the most out of their data.

The pursuit of general but scalable statistical computation has lead me to the intersection of diffential geometry and probability theory where exploiting the inherent geometry of high-dimensional problems naturally leads to algorithms such Hamiltonian Monte Carlo and its generalizations. Along with some amazing colleagues I am developing both the theoretical foundations and the practical implementations of these algorithms, the latter specifically in the software ecosystem Stan.