Profile

Probabilistic (Bayesian) approaches to statistics and machine learning have become increasingly popular in recent years due to new developments in probabilistic programming languages and associated learning algorithms as well as a steady increase in overall computing power.

Probabilistic programming languages make it easier to specify and fit Bayesian models, but this still leaves us with many options regarding constructing, evaluating, and using these models, along with many remaining challenges in computation.

Our overarching scientific goal for the upcoming years is to develop a principled Bayesian workflow for data analysis that comprises the whole scientific process from design of studies, data gathering and cleaning over model building, calibration, fitting and evaluation, to the post-processing and statistical decision making. As such, we are working on a wide range of research topics related to the development, evaluation, implementation, or application of Bayesian methods. Some of my current core research areas are detailed below.