I am interested in algorithms that estimate and express uncertainty about the result of imprecise computations. Such imprecision can arise because the computational task is not analytically tractable, because a limited computational budget only allows a partial solution, or because the description of the task is itself imprecise to begin with. Probability measures provide the formal language for the description of such uncertainty. My group and I develop computer algorithms that take in and return probability measures; we call these probabilistic numerical methods.
If you need a bio-blurb for your event web-page or a talk introduction, here's a suggestion (sorry if this sounds like grandstanding, I've repeatedly been asked for such a text):
Philipp Hennig studied Physics in Heidelberg and London, and received his PhD from the University of Cambridge, UK, in 2011. He runs an independent research group at to the Max Planck Institute for Intelligent Systems in Tübingen, Germany. His group develops numerical algorithms both for and as intelligent, autonomous systems. It has been influential in the emergence of the research area of probabilistic numerical methods. Hennig works primarily in the machine learning community, but also has ties to applied mathematics, control engineering, and statistics.
Our goal is to understand the principles of Perception, Action and Learning in autonomous systems that successfully interact with complex environments and to use this understanding to design future systems