Ruben Seyer
About Ruben Seyer
My interest lies at the intersection of Bayesian inference and machine learning, where we develop computational methods for statistics. In particular I work with Markov Monte Carlo methods and applications to spatial statistics and point processes. My latest research concerns designing non-reversible samplers, and applying stochastic gradient methods to MCMC and piecewise deterministic Markov processes to automatically turn samplers into gradient samplers.
For more information, see https://ruben.seyer.se/