Max Planck Research Group Leader

Office: S2.006

Max-Planck-Ring 4

72076 TÃ¼bingen

Germany

Max-Planck-Ring 4

72076 TÃ¼bingen

Germany

+49 7071 601 572

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.

- For detailed information about our work, please see our group's homepage.
- A general overview of probabilistic numerical methods can be found at http://www.probabilistic-numerics.org.
- A while ago I was interviewed by Katie Gorman and Ryan Adams for Talking Machines. The interview provides a bit of a general introduction. In a bittersweet coincidence, this episode of the podcast opens with an obituary for my incredible PhD advisor, Sir David MacKay.

- Video of my talk on Bayesian Optimization at the Gaussian Process Summer School 2015
- I organised the Machine Learning Summer School 2015 (videos and slides). During this MLSS, I also did a tutorial on probabilistic numerics
- Videos of my Gaussian Process tutorial at the RNLS summer school at the ETH Zürich are online. This is the most recent and most compact video of my GP tutorial. If you want to learn about GPs in 90 minutes, I recommend watching this one instead of the older ones listed below.
- I organised the Machine Learning Summer School 2013 (videos and slides)
- Videos of my talks at the MLSS are up (slide animations only work in Adobe Reader):
- I spoke on GPs at the Gaussian Process Winter School 2013. Here is a video, and the slides of this talk (slide animations only work in Adobe Reader).

can be found here. Don't trust it to always be up to date. 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 now 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.*

Inference Probability Numerical Methods

55 results
(BibTeX)

**Using an Infinite Von Mises-Fisher Mixture Model to Cluster Treatment Beam Directions in External Radiation Therapy **
In pages: 746-751 , (Editors: Draghici, S. , T.M. Khoshgoftaar, V. Palade, W. Pedrycz, M.A. Wani, X. Zhu), IEEE, Piscataway, NJ, USA, Ninth International Conference on Machine Learning and Applications (ICMLA), December 2010 (inproceedings)

**Approximate Inference in Graphical Models**
University of Cambridge, November 2010 (phdthesis)

**Expectation Propagation on the Maximum of Correlated Normal Variables**
Cavendish Laboratory: University of Cambridge, July 2009 (techreport)

**Bayesian Quadratic Reinforcement Learning**
NIPS Workshop on Probabilistic Approaches for Robotics and Control, December 2009 (poster)

**Point-spread functions for backscattered imaging in the scanning electron microscope **
*Journal of Applied Physics *, 102(12):1-8, December 2007 (article)