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 domain=uva.nl

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

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

News

Research interests

My research interests are causal discovery (both fundamental questions and applications in biology), approximate inference and machine learning methods.

I recently started my own research group. Current members are Philip Versteeg, Stephan Bongers, Sara Magliacane, Thijs van Ommen, Patrick Forré and Tineke Blom. Tom Claassen is a guest researcher.

Publications

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

Software

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.

UAI 2014 Workshop "Causal Learning: Inference and Prediction"

We organized a workshop on causal learning at UAI last year. For more details, see the workshop webpage.

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

I organize the weekly Causality Reading Group.

Teaching

ASCI APR Course

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 and coordinate the Machine Learning 2 course.

Introduction to Group Theory (for physicists)


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