Matthijs van Leeuwen
Matthijs likes data, patterns, algorithms, and information theory. He strives for data mining and machine learning methods and results that are principled, interpretable, and incorporate existing knowledge.
Matthijs is assistant professor and group leader of the Explanatory Data Analysis group. He is affiliated with the Leiden Institute of Advanced Computer Science (LIACS), the computer science institute of Leiden University, and the Leiden Centre of Data Science (LCDS). His primary research interest is exploratory data mining: how can we enable domain experts to explore and analyse their data, to discover structure and—ultimately—novel knowledge?
For this it is very important that all methods and results are explainable to domain experts, who may not be data scientists. His approach is therefore 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 of exploratory data mining. 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 finds it very interesting to do 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).
Matthijs was previously senior researcher at Leiden University (2015-2017), and 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 awards at international conferences and was awarded NWO Rubicon and FWO Postdoc grants. He co-organised a number of international conferences and workshops, such as IDA and IDEA, and co-lectured tutorials on 'Information Theoretic Methods in Data Mining'.
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