Abstract: With consistently growing data sets and increasingly complex models, the frontiers of applied statistics is found in high-dimensional spaces. Unfortunately most of the intuitions that we take for granted in our low-dimensional, routine experiences don’t persist to these high-dimensional spaces which makes the development of scalable computational methodologies and algorithms all the more challenging. In this talk I will discuss the counter-intuitive behavior of high-dimensional spaces and the consequences for statistical computation.

Bio: Michael Betancourt’s 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 him 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. He is developing both the theoretical foundations and the practical implementations of these algorithms, the latter specifically in the software ecosystem Stan.

Speaker: Michael Betancourt

Affiliation: Columbia University

Place of Seminar: Aalto University