Joris M. Mooij

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Associate Professor
Amsterdam Machine Learning Lab (AMLab)
Informatics Institute
University of Amsterdam

Contact information:
Telephone: +31 (0)20 5258426
Email: My email address: user@domain, with user=j.m.mooij and

Visiting address:
Science Park 904
1098 XH Amsterdam
Room C2.117

Postal address:
IvI, Faculty of Science
Postbus 94323
1090 GH Amsterdam
The Netherlands

Vacancy: PhD position in Reinforcement Learning

One PhD position in Reinforcement Learning is available in my group. The position is funded by Microsoft Research and will be supervised by me and Danielle Belgrave of Microsoft Research Cambridge. The goal of the project is the development and application of a novel framework for personalised health interventions that combines elements of contextual bandits, causal prediction, and online learning. The deadline for applying is June 1st, 2018.


Brief biography

Joris M. Mooij studied mathematics and physics and received his PhD degree with honors from the Radboud University Nijmegen (the Netherlands) in 2007. His PhD research concerned approximate inference in graphical models. During the next three years, he worked on causal discovery as a postdoc at the Max Planck Institute for Biological Cybernetics in Tübingen (Germany). In 2011 he obtained an NWO VENI grant, which allowed him to do a second postdoc, this time at the Radboud University Nijmegen. In 2013 he became Assistant Professor at the Informatics Institute of the University of Amsterdam (the Netherlands). In the next years, he obtained an NWO VIDI grant and an ERC Starting Grant, allowing him to start his own research group, consisting of 3 PhD students and 3 postdocs, focussing entirely on causal discovery. The research topics addressed by his group span the entire spectrum from causal modeling, discovery, prediction, validation and application and combine mathematical, algorithmic, statistical and modeling aspects. In 2017 he was promoted to Associate Professor. He has won several awards for his work.

Group members

Current members of my research group are Philip Versteeg, Stephan Bongers, Thijs van Ommen, Patrick Forré and Tineke Blom. Tom Claassen is a guest researcher.


My list of publications, including BiBTeX entries, abstracts, full-texts and source code.


libDAI: A free/open source C++ library for Discrete Approximate Inference methods

libDAI is a free/open source C++ library (licensed under BSD 2-clause / FreeBSD) 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 software

Other software that was released as part of publications is available from my publications page.

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 data that you would like to contribute, please contact me.

Causality Reading Group

We have a weekly Causality Reading Group. You can join in if you are interested (and can be physically present).



Slides for my lecture in the ASCI APR Course on Causal Modelling, April 14, 2016.

Master AI

I am involved in teaching for the Master Artificial Intelligence. Currently I teach for and coordinate the Machine Learning 2 course.

Introduction to Group Theory (for physicists)

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