Matthijs van Leeuwen
Associate professor/Director of Education
- Name
- Dr. M. van Leeuwen
- Telephone
- +31 71 527 7048
- m.van.leeuwen@liacs.leidenuniv.nl
- ORCID iD
- 0000-0002-0510-3549
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. He is director of education of the Computer Science, Media Technology and ICT in Business and the Public Sector master's programmes. Besides this, he is member of the LIACS management team and of the interdisciplinary research programme Society, Artificial Intelligence and Life Sciences (SAILS).
More information about Matthijs van Leeuwen
PhD Candidates
News
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'We fully trust each other's abilities' -
Two directors of education for LIACS -
Matthijs van Leeuwen: 'I want to teach students responsible data science' -
Four science professors receive Senior Teaching Qualification certificate -
Improving the environment of people with dementia with the help of new software -
Four million euros for DIGITAL TWIN program -
Interactive models: Matthijs van Leeuwen receives NWO TOP grant -
First session of new series of grants matchmaking events well visited -
Annual Business Event 2015
Matthijs is assistant professor, group leader of the Explanatory Data Analysis group, Programme Manager of the Master Computer Science and member of the interdisciplinary research programme Society, Artificial Intelligence and Life Sciences (SAILS). 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 important that methods and results are explainable to domain experts, who may not be data scientists. His 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. 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.
Short bio
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 awards at international conferences and was awarded NWO Rubicon, FWO Postdoc, and NWO TOP2 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'.
Associate professor/Director of Education
- Science
- Leiden Inst of Advanced Computer Science
- Rijn S.J. van, Schmitt S., Leeuwen M. van & Bäck T.H.W. (2023), Finding efficient trade-offs in multi-fidelity response surface modelling, Engineering Optimization 55(6): 946-963.
- Li Z. & Leeuwen M. van (2023), Explainable contextual anomaly detection using quantile regression forests, Data Mining and Knowledge Discovery 37: 2517-2563.
- Dijk M.K. van, Gawehns D. & Leeuwen M. van (2023), WEARDA: recording wearable sensor data for human activity monitoring, Journal of Open Research Software 11(1): 13.
- Kroes S.K.S., Leeuwen M. van, Groenwold R.H.H. & Janssen M.P. (2023), Generating synthetic mixed discrete-continuous health records with mixed sum-product networks, Journal of the American Medical Informatics Association 30(1): 16-25.
- Li Z., Zhu Y. & Leeuwen M. van (2023), A survey on explainable anomaly detection, ACM Transactions on Knowledge Discovery from Data 18(1): 23.
- Zhong L., Leeuwen M van & Li Z. (2022), Feature selection for fault detection and prediction based on event log analysis, ACM SIGKDD Explorations 24(2): 96-104.
- Vinkenoog M., Steenhuis M., Brinke A. ten, Hasselt J.G.C. van: Janssen M.P., Leeuwen M. van, Swaneveld F.H., Vrielink H., Watering L. van de, Quee F., Hurk K. van den, Rispens T., Hogema B. & Schoot C.E. van der (2022), Associations between symptoms, donor characteristics and IgG antibody response in 2082 COVID-19 convalescent plasma donors, Frontiers in Immunology 13: 821721.
- Manuel Proenca H., Grünwald P.D., Bäck T.H.W. & Leeuwen M. van (2022), Robust subgroup discovery: discovering subgroup lists using MDL, Data Mining and Knowledge Discovery 36(5): 1885-1970.
- Vinkenoog M., Leeuwen M. van & Janssen M.P. (2022), Explainable haemoglobin deferral predictions using machine learning models: interpretation and consequences for the blood supply, Vox Sanguinis 117(11): 1262-1270.
- Marx A., Yang L. & Leeuwen M. van (2021), Estimating conditional mutual information for discrete-continuous mixtures using multi-dimensional adaptive histograms. Demeniconi C. & Davidson I. (Eds.), Proceedings of the 2021 SIAM International Conference on Data Mining (SDM). 2021 SIAM International Conference on Data Mining (SDM) 29 April 2021 - 1 May 2021: SIAM. 387-395.
- Kroes S.K., Janssen M.P., Groenwold R.H. & Leeuwen M. van (2021), Evaluating privacy of individuals in medical data, Health Informatics Journal 27(2): .
- Manuel Proença H., Grünwald P.D., Bäck T.H.W. & Leeuwen M. van (2021), Discovering outstanding subgroup lists for numeric targets using MDL. Hutter F., Kersting K., Lijffijt J. & Valera I. (Eds.), Machine learning and knowledge discovery in databases. ECML PKDD 2020 14 September 2020 - 18 September 2020 no. 12457. Cham: Springer . 19-35.
- Kapoor S., Saxena D.K. & Leeuwen M. van (2021), Online summarization of dynamic graphs using subjective interestingness for sequential data, Data Mining and Knowledge Discovery 35(1): 88-126.
- Gautrais C., Cellier P., Leeuwen M. van & Termier A. (2020), Widening for MDL-Based Retail Signature Discovery. Berthold M.R., Feelders A. & Krempl G. (Eds.), Advances in intelligent data analysis XVIII. IDA 2020. International Symposium on Intelligent Data Analysis (IDA 2020) 27 April 2020 - 29 April 2020 no. 12080. Cham: Springer. 197-209.
- Vinkenoog M. Hurk K. van den Kraaij M. van Leeuwen M. van Janssen M.P. (2020), First results of a ferritin‐based blood donor deferral policy in the Netherlands, Transfusion 60(8): 1785-1792.
- Manuel Proença H. & Leeuwen M. van (2020), Interpretable multiclass classification by MDL-based rule lists, Information Sciences 512: 1372-1393.
- Faas M. & Leeuwen M. van (2020), Vouw: geometric pattern mining using the MDL principle. In: Berthold M., Feelders A. & Krempl G. (Eds.) Advances in intelligent data analysis XVIII. IDA 2020. no. 12080 Cham: Springer . 158-170.
- Kapoor S., Saxena D.K. & Leeuwen M. van (2020), Discovering subjectively interesting multigraph patterns, Machine Learning 109(8): 1669-1696.
- Gawehns D., Veiga G. & Leeuwen M. van (2019), Focus on Dynamics: a proof of principle in exploratory data mining of face-to-face interactions. 5th International Conference on Computational Social Sciences, Amsterdam. 17 July 2018 - 20 July 2019. [conference poster].
- Manuel Proença H., Klijn R., Bäck T.H.W. & Leeuwen M. van (2019), Identifying flight delay patterns using diverse subgroup discovery, Proceedings of the Symposium Series on Computational Intelligence (SSCI'18). 2018 Symposium Series on Computational Intelligence 18 November 2018 - 21 November 2018. Bangalore, India: IEEE. 60-67.
- Vinkenoog M., Janssen M. & Leeuwen M. van (2019), Challenges and Limitations in Clustering Blood Donor Hemoglobin Trajectories. Lemaire V., Malinowski S., Bagnall A, Bondu A., Guyet T. & Tavanard R. (Eds.), Advanced Analytics and Learning on Temporal Data. AALTD 2019. International Workshop on Advanced Analysis and Learning on Temporal Data (AALTD 2019) 20 September 2019 - 20 September 2019. Lecture Notes in Computer Science no. 11986. Cham: Springer International Publishing. 72-84.
- Leeuwen M. van, Chau D.H., Vreeken J., Shahaf D. & Faloutsos C. (2019), Addendum to the Special Issue on Interactive Data Exploration and Analytics (TKDD, Vol. 12, Iss. 1): Introduction by the Guest Editors. [other].
- Os H.J.A. van, Ramos L.A., Hilbert A., Leeuwen M. van, Walderveen M.A.A. van, Kruyt N.D., Dippel D.W.J., Steyerberg E.W., Schaaf I.C. van der, Lingsma H.F., Schonewille W.J., Majoie C.B.L.M., Olabarriaga S.D., Zwinderman K.H., Venema E., Marquering H.A. & Wermer M.J.H. (2018), Predicting Outcome of Endovascular Treatment for Acute Ischemic Stroke: Potential Value of Machine Learning Algorithms, Frontiers in Neurology 9: 784.
- Leeuwen M. van, Chau P., Vreeken J., Shahaf D. & Faloutsos C. (Eds.) (2018), Editorial: TKDD Special Issue on Interactive Data Exploration and Analytics. ACM Transactions on Knowledge Discovery from Data: ACM.
- Rijn S.J. van, Schmitt S., Olhofer M., Leeuwen M. van & Bäck T. (2018), Multi-Fidelity Surrogate Model Approach to Optimization. Aguirre H. (Ed.), GECCO'18 Proceedings of the Genetic and Evolutionary Computation Conference Companion. GECCO 2018 15 July 2018 - 19 July 2018. New York: ACM. 225-226.
- Stein B. van, Leeuwen M. van & Bäck T. (2017), Local Subspace-Based Outlier Detection using Global Neighbourhoods, 2016 IEEE International Conference on Big Data (Big Data). : IEEE. 1136-1142.
- Le T. van, Nijssen S., Leeuwen M. van & De Raedt L. (2017), Semiring Rank Matrix Factorisation, IEEE Transactions on Knowledge and Data Engineering 29(8): 1737-1750.
- Dzyuba V., Leeuwen M. van & De Raedt L. (2017), Flexible constrained sampling with guarantees for pattern mining, Data Mining and Knowledge Discovery 31(5): 1266–1293.
- Dzyuba V. & Leeuwen M. van (2017), Learning what matters – Sampling interesting patterns. Kim J., Shim K., Cao L., Lee J.G., Lin X. & Moon Y.S. (Eds.), Advances in Knowledge Discovery and Data Mining. Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD'17) 23 May 2017 - 26 May 2017 no. Lecture Notes in Computer Science vol. 10234. Cham: Springer. 534-546.
- Stein B. van, Leeuwen M. van, Wang H., Purr S., Kreissl S., Meinhardt J. & Bäck T.H.W. (2017), Towards Data Driven Process Control in Manufacturing Car Body Parts, 2016 International Conference on Computational Science and Computational Intelligence CSCI. International Conference on Computational Science and Computational Intelligence (CSCI 2016) 15 December 2016 - 17 December 2016: IEEE CPS.
- Dzyuba V. & Leeuwen M. van (2017), Learning what matters - Sampling interesting patterns. Ceci M., Hollmén J. & Todorovski L. (Eds.), Machine Learning and Knowledge Discovery in Databases. ECMLPKDD 18 September 2017 - 22 September 2017 no. Lecture Notes in Computer Science vol. 10535. Cham: Springer. 425-441.
- Paramonov S., Leeuwen M. van & Raedt L. de (2017), Relational data factorization, Machine Learning 106(12): 1867-1904.
- Rijn Sander van, Hao Wang, Leeuwen M. van & Bäck T.H.W. (2016), Evolving the Structure of Evolution Strategies, 2016 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE SSCI 2016 6 December 2016 - 9 December 2016: IEEE Publishing. 1-8.
- Leeuwen M. van, Bie T. de, Spyropoulou E. & Mesnage C. (2016), Subjective interestingness of subgraph patterns, Machine Learning 105(1): 41-75.
- Stein B. van, Leeuwen M. van & Bäck T.H.W. (2016), Local Subspace-Based Outlier Detection using Global Neighbourhoods, 2016 IEEE International Conference on Big Data (Big Data). IEEE International Conference on Big Data 2016 5 December 2016 - 8 December 2016: IEEE Publishing.
- 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 T. van, 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 & Galbrun E. (2015), Association Discovery in Two-View Data, IEEE Transactions on Knowledge and Data Engineering 27(12): 3190-3202.
- Paramonov S., Leeuwen M. van, Denecker M. & Raedt L. de (2015), An exercise in declarative modeling for relational query mining. Inoue K., Ohwada H. & Yamamoto A. (Eds.), Inductive Logic Programming. ILP 2015. 25th International Conference, ILP 2015 20 August 2015 - 22 August 2015 no. LNCS 9575. Cham: Springer. 166-182.
- 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. [other].
- Aksehirli E., Nijssen S.G.R., Leeuwen M. van & Goethals B. (2015), Finding subspace clusters using ranked neighborhoods, 2015 IEEE International Conference on Data Mining Workshop (ICDMW). The 3rd International Workshop on High Dimensional Data Mining 14 November 2015 - 14 November 2015: IEEE Publishing. 831-838.
- Fromont E., Bie T. de & Leeuwen M. van (Eds.) (2015), Advances in Intelligent Data Analysis XIV. Lecture Notes in Computer Science no. 9385. Cham: Springer.
- Leeuwen M. van & Cardinaels L. (2015), VIPER - Visual Pattern Explorer. Bifet A., May M., Zadrozny B., Gavalda R., Pedreschi D., Bonchi F., Cardoso J. & Spiliopoulou M. (Eds.), ECML PKDD: Machine Learning and Knowledge Discovery in Databases. ECMLPKDD 7 September 2015 - 11 September 2015 no. 9286. Cham: Springer. 333-336.
- Leeuwen M. van & Ukkonen A. (2015), Same bang, fewer bucks: efficient discovery of the cost-influence skyline. Venkatasubramanian S. & Ye J. (Eds.), Proceedings of the 2015 SIAM International Conference on Data Mining. 2015 SIAM International Confernce on Data Mining 30 April 2015 - 2 May 2015: SIAM. 19-27.
- Van T. le, Leeuwen M. van, Nijssen S.G.R. & Raedt L. de (2015), Rank Matrix Factorisation. Cao T., Lim E.P., Zhou Z.H., Ho T.B., Cheung D. & Motoda H. (Eds.), Proceedings Advances in Knowledge Discovery and Data Mining. Advances in Knowledge Discovery and Data Mining - 19th Pacific-Asia Conference, PAKDD 2015 19 May 2015 - 22 May 2015 no. LNCS 9077. Cham: Springer. 734-746.
- Chau P., Vreeken J., Leeuwen M. van, Shahaf D. & Faloutsos C. (Eds.) (2013), IDEA '13 Proceedings of the ACM SIGKDD Workshop on Interactive Data Exploration and Analytics: ACM.