Matthijs van Leeuwen
Matthijs likes data, patterns, algorithms, and information theory. He thinks that data mining and machine learning results should be explainable and interpretable.
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'.
- Stein B. van, Leeuwen M. van, Hao Wang, Purr S., Kreissl S., Meinhardt J. & Bäck T.H.W. (2017), Towards Data Driven Process Control in Manufacturing Car Body Parts. In: 2016 International Conference on Computational Science and Computational Intelligence CSCI.: IEEE CPS.
- Stein B. van, Leeuwen M. van & Bäck T.H.W. (2016), Local Subspace-Based Outlier Detection using Global Neighbourhoods. In: 2016 IEEE International Conference on Big Data (Big Data).: IEEE Publishing.
- Sander van Rijn, Hao Wang, Leeuwen M. van & Bäck T.H.W. (2016), Evolving the Structure of Evolution Strategies. In: 2016 IEEE Symposium Series on Computational Intelligence (SSCI).: IEEE Publishing. 1-8.
- Copmans D., Meinl T., Dietz C., Leeuwen M. van, Ortmann J., Berthold M.R. & Witte P.A. de (2016), A KNIME-Based Analysis of the Zebrafish Photomotor Response Clusters the Phenotypes of 14 Classes of Neuroactive Molecules, Journal of biomolecular screening 21(5): 427-436.
- Le Van T., Leeuwen M. van, Fierro A.C., Maeyer D. De, Van den Eynden J., Verbeke .L, De Raedt L., Marchal K. & Nijssen S.G.R. (2016), Simultaneous discovery of cancer subtypes and subtype features by molecular data integration, BIOINFORMATICS 32(17): i445--i454.
- Leeuwen M. van, Bie T. de, Spyropoulou E. & Mesnage C. (2016), Subjective interestingness of subgraph patterns, Machine Learning 105(1): 41-75.
- Leeuwen M. van & Galbrun E. (2015), Association Discovery in Two-View Data, IEEE Transactions on Knowledge and Data Engineering 27(12): 3190-3202.
- Van T. Le, Leeuwen M. van, Nijssen S.G.R. & Raedt L. De (2015), Rank Matrix Factorisation. In: Proceedings Advances in Knowledge Discovery and Data Mining - 19th Pacific-Asia Conference, PAKDD 2015, volume I. nr. LNCS 9077. 734-746.
- Chau P, Vreeken J, van Leeuwen M & Faloutsos C (2015), Proceedings of the ACM SIGKDD 2015 Full-day Workshop on Interactive Data Exploration and Analytics. [overig]
- van Leeuwen M & Ukkonen A (2015), Same bang, fewer bucks: efficient discovery of the cost-influence skyline. In: Proceedings of the 2015 SIAM International Conference on Data Mining.. 19--27.
- Aksehirli E., Nijssen S.G.R., Leeuwen M. van & Goethals B. (2015), Finding subspace clusters using ranked neighborhoods. In: 2015 IEEE International Conference on Data Mining Workshop (ICDMW).: IEEE Publishing. 831-838.
- Fromont E., De Bie T., Leeuwen & M. van (red.) (2015), Advances in Intelligent Data Analysis XIV nr. 9385: Springer.
- Leeuwen M. van & Cardinaels L. (2015), VIPER - Visual Pattern Explorer. In: Bifet A., May M., Zadrozny B., Gavalda R., Pedreschi D., Bonchi F., Cardoso J., Spiliopoulou M. (red.) Machine Learning and Knowledge Discovery in Databases. nr. 9286: Springer. 333-336.
- Paramonov S., van Leeuwen M., Denecker M. & De Raedt L. (2015), An exercise in declarative modeling for relational query mining.
- Chau P., Vreeken J., Leeuwen M. van, Shahaf D. & Faloutsos C. (2013), Proceedings of the ACM SIGKDD Workshop on Interactive Data Exploration and Analytics.
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