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 differential 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.