About Me
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
Engineering & Materials Science
# Big Data
# Data Integration
# Machine Learning
# Metadata
# Ontology
# Radiology
# Semantics
# Uncertainty
Organisations
Publications
Recent
Pathak, S., Schlötterer, J.
, Veltman, J., Geerdink, J.
, Keulen, M. V.
, & Seifert, C. (2024).
Prototype-based Interpretable Breast Cancer Prediction Models: Analysis and Challenges.
Nauta, M., Hegeman, J. H., Geerdink, J., Schlötterer, J.
, Keulen, M. V.
, & Seifert, C. (2024).
Interpreting and Correcting Medical Image Classification with PIP-Net. In S. Nowaczyk, P. Biecek, N. C. Chung, M. Vallati, P. Skruch, J. Jaworek-Korjakowska, S. Parkinson, A. Nikitas, M. Atzmüller, T. Kliegr, U. Schmid, S. Bobek, N. Lavrac, M. Peeters, R. van Dierendonck, S. Robben, E. Mercier-Laurent, G. Kayakutlu, M. L. Owoc, K. Mason, A. Wahid, P. Bruno, F. Calimeri, F. Cauteruccio, G. Terracina, D. Wolter, J. L. Leidner, M. Kohlhase, ... V. Dimitrova (Eds.),
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.
https://doi.org/10.1007/978-3-031-50396-2_11
Nauta, M., Hegeman, J. H., Geerdink, J., Schlötterer, J.
, Keulen, M. V.
, & Seifert, C. (2023).
Interpreting and Correcting Medical Image Classification with PIP-Net. ArXiv.org.
https://doi.org/10.48550/arXiv.2307.10404
Pathak, S., Schlötterer, J., Geerdink, J., Vijlbrief, O. D.
, Keulen, M. V.
, & Seifert, C. (2023).
Weakly Supervised Learning for Breast Cancer Prediction on Mammograms in Realistic Settings. ArXiv.org.
Wu, B., Xiao, Q., Liu, S., Yin, L., Pechenizkiy, M., Mocanu, D. C.
, Keulen, M. V.
, & Mocanu, E. (2023).
E2ENet: Dynamic Sparse Feature Fusion for Accurate and Efficient 3D Medical Image Segmentation. ArXiv.org.
https://doi.org/10.48550/arXiv.2312.04727
Maas, J., Römer, J. G. W. T.
, Baysal Erez, I.
, & van Keulen, M. (2023).
Investigating Imputation Methods for Handling Missing Data. Poster session presented at Joint International Scientific Conferences on AI and Machine Learning, BNAIC/BeNeLearn 2023, Delft, Netherlands.
Maas, J., Römer, J. G. W. T.
, Baysal Erez, I.
, & van Keulen, M. (2023).
Investigating Imputation Methods for Handling Missing Data. Paper presented at Joint International Scientific Conferences on AI and Machine Learning, BNAIC/BeNeLearn 2023, Delft, Netherlands.
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)
UT Research Information System
Google Scholar Link
Affiliated Study Programmes
Bachelor
Master
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
Tweets
Contact Details
Visiting Address
University of Twente
Faculty of Electrical Engineering, Mathematics and Computer Science
Zilverling
(building no. 11), room 4061
Hallenweg 19
7522NH Enschede
The Netherlands
Mailing Address
University of Twente
Faculty of Electrical Engineering, Mathematics and Computer Science
Zilverling
4061
P.O. Box 217
7500 AE Enschede
The Netherlands