Explanatory Data Analysis group

Dr. Matthijs van Leeuwen

Dr. Matthijs van Leeuwen
Dr. Matthijs van Leeuwen
Associate professor & group leader

Associate professor & group leader Website Google Scholar profile LinkedIn profile Twitter profile

The short (compressed) version

Matthijs likes data, patterns, algorithms, and information theory. He strives for data mining and machine learning methods and results that are principled, interpretable, and exploit existing knowledge.

The longer version

Matthijs is associate professor and group leader at the Leiden Institute of Advanced Computer Science (LIACS), Leiden University. He is Programme Manager of the Master Computer Science, and affiliated with SAILS and DSRP, the university-wide research programmes for artificial intelligence (AI) and data science. His primary research interests are exploratory data mining and interpretable machine learning: how can we enable domain experts to explore and analyse their data, to uncover patterns and make predictions, and—ultimately—discover novel knowledge?

For this it is important that methods and results are explainable to domain experts, who may not be data scientists. His signature approach is to define and identify patterns that matter, i.e., succinct descriptions that characterise relevant structure present in the data. Which patterns matter strongly depends on the data and task at hand, hence defining the problem is one of the key challenges in his research. Information theoretic concepts such as the Minimum Description Length (MDL) principle have proven very useful to this end. Matthijs is also interested in interactive data mining, i.e., involving humans in the loop. Finally, he is interested in fundamental data mining research for real-world applications, both in science (e.g., life sciences, social sciences) and industry (e.g., manufacturing and engineering, aviation), as this is the best way to show that the theory works in practice.


Matthijs was previously a (tenure track) assistant professor (2017-2020) and senior researcher (2015-2017) at Leiden University, and a postdoctoral researcher at KU Leuven (2011-2015) and Universiteit Utrecht (2009-2011). He defended his Ph.D. thesis, titled Patterns that Matter, in February 2010, at Universiteit Utrecht. He won several best paper and reviewer awards at international conferences and was awarded NWO Rubicon, FWO Postdoc, NWO TOP2, and NWO TTW Perspectief grants. He is General Chair of the IDA Council and editorial board member of Data Mining and Knowledge Discovery. Further, he co-organised a number of international conferences and workshops, and co-lectured tutorials on 'Information Theoretic Methods in Data Mining'.

More information, including CV, at www.patternsthatmatter.org

Selected recent publications

In press
Vinkenoog, M, Toivonen, J, van Leeuwen, M, Janssen, M & Arvas, M The added value of ferritin levels and genetic markers for the prediction of haemoglobin deferral. Vox Sanguinis
van Dijk, R, Gawehns, D & van Leeuwen, M WEARDA: recording wearable sensor data for human activity monitoring. Journal of Open Research Softwarewebsite
Li, Z, Zhu, Y & van Leeuwen, M A Survey on Explainable Anomaly Detection. Transactions on Knowledge Discovery from Data vol.18(1), ACM, 2024.website
Li, Z & an Leeuwen, M Explainable Contextual Anomaly Detection using Quantile Regression Forests. Data Mining and Knowledge Discovery, Springerwebsite
Lopez-Martinez-Carrasco, A, Proença, HM, Juarez, JM, van Leeuwen, M & Campos, M Novel approach for phenotyping based on diverse top-k subgroup lists. In: Proceedings of the Conference on Artificial Intelligence In Medicine (AIME 2023), Springer, 2023.
Lopez-Martinez-Carrasco, A, Proença, HM, Juarez, JM, van Leeuwen, M & Campos, M Discovering Diverse Top-k Characteristic Lists. In: Proceedings of the 21st International Symposium on Intelligent Data Analysis (IDA 2023), Springer, 2023.
Papagianni, I & van Leeuwen, M Discovering Rule Lists with Preferred Variables. In: Proceedings of the 21st International Symposium on Intelligent Data Analysis (IDA 2023), Springer, 2023.
van der Arend, B, Verhagen, I, van Leeuwen, M, van der Arend, M, van Casteren, D & Terwindt, G Defining migraine days, based on longitudinal E-diary data. Cephalalgia
Yang, L, Baratchi, M & van Leeuwen, M Unsupervised Discretization by Two-dimensional MDL-based Histogram. Machine Learning, Springerwebsite
Kroes, SKS, van Leeuwen, M, Groenwold, RHH & Janssen, MP Generating synthetic mixed discrete-continuous health records with mixed sum-product networks. Journal of the American Medical Informatics Association vol.30(1), Oxford University Press, 2023.