I am associate professor and group leader of the Explanatory Data Analysis group at the Leiden Institute of Advanced Computer Science (LIACS), the computer science institute of Leiden University. My 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 important that methods and results are explainable to domain experts, who may not be data scientists. My 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 of exploratory data mining. Information theoretic concepts such as the Minimum Description Length (MDL) principle have proven very useful to this end. I am also interested in interactive data mining, i.e., involving humans in the loop. Finally, I am 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.
I am affiliated with the Leiden Centre of Data Science (LCDS) and university-wide Data Science Research Programme (DSRP). Broadly speaking, my research can be situated in the fields of data mining, machine learning, data science, and artificial intelligence (AI).
In press |
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Evaluating privacy of individuals in medical data. Health Informatics Journal, SAGE Publications |
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2021 |
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Estimating Conditional Mutual Information for Discrete-Continuous Mixtures using Multi-Dimensional Adaptive Histograms. In: Proceedings of the SIAM Conference on Data Mining 2021 (SDM'21), SIAM, 2021. |
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Online Summarization of Dynamic Graphs using Subjective Interestingness for Sequential Data. Data Mining and Knowledge Discovery vol.35(1), pp 88-126, 2021. (ECML PKDD journal track) |
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2020 |
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Social Fluidity in Children's Face-to-Face Interaction Networks. In: Proceedings of the Graph Embedding and Mining (GEM) Workshop at ECML PKDD 2020, 2020. |
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Discovering Outstanding Subgroup Lists for Numeric Targets using MDL. In: Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD 2020), Springer, 2020. |
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First results of a ferritin-based blood donor deferral policy in the Netherlands. Transfusion vol.60(8), pp 1785-1792, Wiley, 2020. |
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Vouw: Geometric Pattern Mining using the MDL Principle. In: Proceedings of the Eighteenth International Symposium on Intelligent Data Analysis (IDA 2020), Springer, 2020. |
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Widening for MDL-based Retail Signature Discovery. In: Proceedings of the Eighteenth International Symposium on Intelligent Data Analysis (IDA 2020), Springer, 2020. |
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Discovering Subjectively Interesting Multigraph Patterns. Machine Learning, pp 1-28, Springer |
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Interpretable multiclass classification by MDL-based rule lists. Information Sciences vol.512, pp 1372-1393, Elsevier, 2020. |