Current Main Research focus
One of the current main focuses of the group is on the development of new tools to uncover and model high order patterns of data.
Highlight: Check our paper on the use of Minimally Complex spin Models (MCM) to identify and model groups of highly correlated variables in binary data: here.
The procedure performs exact Bayesian model selection and takes into account all possible high order patterns of data (3-body correlations, 4-body correlations, etc.) in the detection of these « communities » of variables. The use of Minimally Complex Models provides robust predictions on dependencies between variables.
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Codes: You can find our codes that performs this « community detection » for binary data on Github:
— MinCompSpin performs an exhaustive search and works best for systems with a small number of variables (<= 15).
— MinCompSpin_Greedy performs a greedy search and works best for systems with a large number of variables (up to 128 variables).
Current group members
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PhD students: Ebo Peerbooms
Master students:
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— Mathematics: Khaled Tamimy
— Computational Science: Lotte Wolfenter, Mylène van der Maas, Aaron de Clercq, Paul Hosek, Jonas Argelo
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Master students graduated:
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— Theoretical Physics: Jair Lenssen, Karel Geraedts
— Computational Science: Sam Kamphof, Marije Dekker, Karim Semin, Maria Iosif
— Theoretical physics and Neuroscience: Martijn Klop (Utrecht University)
Open positions
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Don’t hesitate to contact me by email to ask about possible open positions or Master projects.