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Professor in Mathematical Statistics |
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Within my group, there is a vacancy for a postdoc position (application deadline: February 12th, 2023). The position is part of the new ICAI Mercury Machine Learning Lab, a collaboration of the University of Amsterdam with TU Delft and booking.com.
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 causality. 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. In 2020 he was promoted to full Professor in Mathematical Statistics at the Korteweg-de Vries Institute of the University of Amsterdam. He has won several awards for his work.
Current members of my causality research group are Philip Versteeg, Noud de Kroon, Teodora Pandeva, Philip Boeken and Leihao Chen. Former members are Sara Magliacane, Thijs van Ommen, Tineke Blom, Stephan Bongers, and Patrick Forré. Tom Claassen and Patrick Forré are currently guest researchers.
My list of publications, including BiBTeX entries, abstracts, full-texts and source code.
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 that was released as part of publications is available from my publications page.
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.
We had a weekly Causality Reading Group, which is currently making a break.
Slides and exercises for my tutorial on Causality for the Machine Learning Summer School (MLSS) 2019 in Moscow, August 26-September 6, 2019.
Slides for my part of the lecture on Causality for the Microsoft Research AI Summer School 2018, July 2-6, 2018. The second part was given by Jonas Peters.
Slides for my lecture in the ASCI APR Course on Causal Modelling, April 14, 2016.
