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
Finding blind spots: Investigating identity data matching in transnational commercialized security infrastructures and beyond. University of Twente. Van Rossem, W.https://doi.org/10.3990/1.9789036561778Prototype-based Interpretable Breast Cancer Prediction Models: Analysis and Challenges. Pathak, S., Schlötterer, J., Veltman, J., Geerdink, J., Keulen, M. v. & Seifert, C.Interpreting and Correcting Medical Image Classification with PIP-NetIn Artificial Intelligence. ECAI 2023 International Workshops - XAI^3, TACTIFUL, XI-ML, SEDAMI, RAAIT, AI4S, HYDRA, AI4AI, 2023, Proceedings (pp. 198-215). 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, 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. ArXiv.org. Wu, B., Xiao, Q., Liu, S., Yin, L., Pechenizkiy, M., Mocanu, D. C., Keulen, M. V. & Mocanu, E.https://doi.org/10.48550/arXiv.2312.04727Investigating Imputation Methods for Handling Missing Data. Maas, J., Römer, J. G. W. T., Baysal Erez, I. & van Keulen, M.Investigating Imputation Methods for Handling Missing Data. Maas, J., Römer, J. G. W. T., Baysal Erez, I. & van Keulen, M.Weakly Supervised Learning for Breast Cancer Prediction on Mammograms in Realistic Settings. ArXiv.org. Pathak, S., Schlötterer, J., Geerdink, J., Vijlbrief, O. D., Keulen, M. v. & Seifert, C.Interpreting and Correcting Medical Image Classification with PIP-Net. ArXiv.org. Nauta, M., Hegeman, J. H., Geerdink, J., Schlötterer, J., Keulen, M. v. & Seifert, C.https://doi.org/10.48550/arXiv.2307.10404PIP-Net: Patch-Based Intuitive Prototypes for Interpretable Image ClassificationIn CVPR 2023 (pp. 2744-2753). Nauta, M., Schlötterer, J., van Keulen, M. & Seifert, C.

Research profiles

Affiliated study programs

Courses academic year 2023/2024

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 2022/2023

Address

University of Twente

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

Navigate to location

Organisations

Scan the QR code or
Download vCard