Zilverling

prof.dr.ir. M. van Keulen (Maurice)

Adjunct Professor
EEMCS-CS-DMB
Zilverling 4061
m.vankeulen@utwente.nl
Business card

He received his masters’ degree in computer science at the University of Twente in 1992 and completed his PhD on Formal operation definition in object-oriented databases in 1997. His research targets robustness in data science focusing on two main threats to data science reliability: data quality and undesirable machine learning behaviour. The former is focused on data integration, semi-structured data, natural language processing, and data quality issues involved in these. He co-developed one of the most scalable XML database systems of its time: MonetDB/XQuery. Furthermore, he proposed a data integration approach, called Probabilistic Data Integration, which fundamentally incorporates handling of uncertain and of lesser quality data. He developed a probabilistic database system, called DuBio, which allows the scalable storage, manipulation and management of such uncertain data. On the threat of undesirable machine learning behaviour, he focuses on Explainable AI with the intrinsically explainable deep learning approach ProtoTree as one of the notable results of this. He is secretary of the executive board of the EDBT Association (Extending Database Technology). He is the (co-) author of about 200 publications that accumulated about 2000 citations.

Expertise

  • Computer Science

    • Events
    • Database
    • Models
    • Case Study
    • Data Integration
    • Real World
    • Social Media
    • Machine Learning

Organisations

Publications

2024

Insights into Dynamic Sparse Training: Theory Meets Practice (2024)[Contribution to conference › Poster] European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2024. Wu, B., van Keulen, M., Mocanu, D. C. & Mocanu, E.Finding blind spots: Investigating identity data matching in transnational commercialized security infrastructures and beyond (2024)[Thesis › PhD Thesis - Research UT, graduation UT]. University of Twente. Van Rossem, W.https://doi.org/10.3990/1.9789036561778Prototype-Based Interpretable Breast Cancer Prediction Models: Analysis and Challenges (2024)In Explainable Artificial Intelligence - 2nd World Conference, xAI 2024, Proceedings (pp. 21-42) (Communications in Computer and Information Science; Vol. 2153 CCIS). Springer. Pathak, S., Schlötterer, J., Veltman, J., Geerdink, J., van Keulen, M. & Seifert, C.https://doi.org/10.1007/978-3-031-63787-2_2The interaction between imputation and regression models (2024)[Contribution to conference › Poster] 22nd International Conference of AI in Medicine, AIME 2024. Baysal Erez, I., Flokstra, J., Poel, M. & van Keulen, M.Prototype-based Interpretable Breast Cancer Prediction Models: Analysis and Challenges (2024)[Working paper › Preprint]. Pathak, S., Schlötterer, J., Veltman, J., Geerdink, J., Keulen, M. v. & Seifert, C.Interpreting and Correcting Medical Image Classification with PIP-Net (2024)In Artificial Intelligence. ECAI 2023 International Workshops - XAI^3, TACTIFUL, XI-ML, SEDAMI, RAAIT, AI4S, HYDRA, AI4AI, 2023, Proceedings (pp. 198-215) (Communications in Computer and Information Science; Vol. 1947). Springer. Nauta, M., Hegeman, J. H., Geerdink, J., Schlötterer, J., Keulen, M. v. & Seifert, C.https://doi.org/10.1007/978-3-031-50396-2_11

2023

From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AI (2023)ACM computing surveys, 55(13s). Article 295. Nauta, M., Trienes, J., Pathak, S., Nguyen, E., Peters, M., Schmitt, Y., Schlötterer, J., Van Keulen, M. & Seifert, C.https://doi.org/10.1145/3583558E2ENet: Dynamic Sparse Feature Fusion for Accurate and Efficient 3D Medical Image Segmentation (2023)[Working paper › Preprint]. ArXiv.org. Wu, B., Xiao, Q., Liu, S., Yin, L., Pechenizkiy, M., Mocanu, D. C., van Keulen, M. & Mocanu, E.https://doi.org/10.48550/arXiv.2312.04727Investigating Imputation Methods for Handling Missing Data (2023)[Contribution to conference › Poster] Joint International Scientific Conferences on AI and Machine Learning, BNAIC/BeNeLearn 2023. Maas, J., Römer, J. G. W. T., Baysal Erez, I. & van Keulen, M.Investigating Imputation Methods for Handling Missing Data (2023)[Contribution to conference › Paper] Joint International Scientific Conferences on AI and Machine Learning, BNAIC/BeNeLearn 2023. Maas, J., Römer, J. G. W. T., Baysal Erez, I. & van Keulen, M.

Research profiles

Affiliated study programs

Courses academic year 2024/2025

Courses in the current academic year are added at the moment they are finalised in the Osiris system. Therefore it is possible that the list is not yet complete for the whole academic year.

Courses academic year 2023/2024

Address

University of Twente

Zilverling (building no. 11), room 4061
Hallenweg 19
7522 NH Enschede
Netherlands

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