Joris M. Mooij
Research interests
My research interests are causal discovery and approximate inference methods.
I have done my PhD research at the Radboud University Nijmegen, under supervision
of Prof. Dr. H.J. Kappen, on approximation
techniques for calculating probabilities in large, complex probabilistic models.
After obtaining my PhD, I started working on causal discovery as a postdoc at the
Department: Empirical Inference
headed by Prof. Dr. B. Schölkopf at the
Max Planck Institute for Biological Cybernetics in Tübingen, Germany. After three years, I returned to Nijmegen, this time to the machine learning group headed by Prof. Dr. T. Heskes, with a VENI grant to continue my research on causal discovery.
Publications
My list of publications, including BiBTeX entries, abstracts and full-texts.
Software
libDAI: A free/open source C++ library for Discrete Approximate Inference methods
libDAI is a free/open source C++ library (licensed under GPL) that provides implementations of various (deterministic) approximate inference methods for discrete graphical models. libDAI supports arbitrary factor graphs with discrete variables (this includes discrete Markov Random Fields and Bayesian Networks).
For more information, see the special page on libDAI. Other licensing options are available upon request.
Data sets
Cause-effect pairs
We are building a benchmark data set for causal discovery algorithms which focuses on the
two-variable case. If you have interesting data that you would like to contribute please contact Jakob Zscheischler.
Coffee talks
Together with Dimitris Mavroeidis I organize the weekly "coffee talks" of the Machine Learning group
(see also the coffee talks schedule).
Teaching
Introduction to Group Theory (for physicists)
