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.
Engineering & Materials Science
# Big Data # Data Integration # Machine Learning # Metadata # Ontology # Radiology # Semantics # Uncertainty
Xiao, Q. , Wu, B., Yin, L. , van Keulen, M., & Pechenizkiy, M. (2023). Can Less Yield More? Insights into Truly Sparse Training. Poster session presented at ICLR 2023 Workshop on Sparsity in Neural Networks, Kigali, Rwanda. https://drive.google.com/file/d/1kbWZ9ejU9XvtOMRtAcVYmcoRCDIWj3zy/view
Nauta, M., Schlötterer, J. , van Keulen, M. , & Seifert, C. (2023). PIP-Net: Patch-Based Intuitive Prototypes for Interpretable Image Classification. Abstract from 2nd Explainable AI for Computer Vision Workshop, XAI4CV 2023, Vancouver, British Columbia, Canada.
Nauta, M., Schlötterer, J. , van Keulen, M. , & Seifert, C. (2023). PIP-Net: Patch-Based Intuitive Prototypes for Interpretable Image Classification. In CVPR 2023 (pp. 2744-2753)
Nauta, M. (2023). Explainable AI and Interpretable Computer Vision: From Oversight to Insight. [PhD Thesis - Research UT, graduation UT, University of Twente]. University of Twente. https://doi.org/10.3990/1.9789036555753
Tran, T. H. A., Wiesner, M. L. , & van Keulen, M. (2022). Influence of discretization granularity on learning classification models. Paper presented at BNAIC/BeNeLearn 2022 Joint International Scientific Conferences on AI and Machine Learning, Mechelen, Belgium. https://bnaic2022.uantwerpen.be/BNAICBeNeLearn_2022_submission_8652
Yenidogan, B. , Pathak, S., Geerdink, J., Hegeman, J. H. , & Van Keulen, M. (2022). Multimodal Machine Learning for 30-Days Post-Operative Mortality Prediction of Elderly Hip Fracture Patients. In B. Xue, M. Pechenizkiy, & Y. S. Koh (Eds.), Proceedings - 21st IEEE International Conference on Data Mining Workshops, ICDMW 2021 (pp. 508-516). (IEEE International Conference on Data Mining Workshops, ICDMW; Vol. 2021-December). IEEE. https://doi.org/10.1109/ICDMW53433.2021.00068
Baysal Erez, I. , & van Keulen, M. (2022). Understanding dynamic sparse training capabilities in accommodating sparse data. 1-6. Paper presented at European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML-PKDD 2022, Grenoble, France.
Sohail, S. A. , Bukhsh, F. A. , van Keulen, M., Krabbe, J. G., & Hruby, P. (2022). Evaluating Clinical-Care Metadata Share and its FAIRification using the REA Ontology. In H. Weigand, T. Prince Sales, & P. Johanesson (Eds.), VMBO 2022, Value Modelling and Business Ontologies 2022: Proceedings of the 16th International Workshop on Value Modelling and Business Ontologies (VMBO 2022), held in conjunction with the 34th International Conference on Advanced Information Systems Engineering (CAiSE 2022), June 06–10, 2022, Leuven, Belgium (Vol. 3155). (CEUR Workshop Proceedings). CEUR. http://ceur-ws.org/Vol-3155/paper3.pdf
van Bruxvoort, X. , & van Keulen, M. (2021). Framework for assessing ethical aspects of algorithms and their encompassing socio-technical system. Applied Sciences (Switzerland), 11(23), . https://doi.org/10.3390/app112311187
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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.