The Data Science group is a section of the Institute for Computing and Information Sciences at the Radboud University Nijmegen. Our group, headed by Prof. Tom Heskes, consists of about 40 to 50 researchers.
We develop theory and methods for scalable machine learning and information retrieval to analyze big data and address challenging problems in science and society. We are involved in various projects with other groups, both within and outside the Radboud University. Research funding mainly comes from NWO, TTW, and the EU.
We are part of Radboud AI and contribute to Radboud AI for Health.
Inge Wortel moderated the course “AI in Perspectief” from the Radboud Academy on how AI impacts our life, which further featured contributions from Frederik Zuiderveen Borgesius, Martha Larson, and Tom Heskes.
On March 23, the AI for Energy Grids Lab was officially launched. Within this lab, Charlotte Cambier van Nooten studies how graph neural networks can improve the efficiency and reliability of our energy grid under daily supervision of Yuliya Shapovalova and Tom van de Poll (Alliander).
Tom Heskes contributed to the Studio Oost NL podcast on how small and medium-sized enterprises (SMEs) can get started with AI: [https://www.ru.nl/ai/news-events/news/vm-eigen-news/studio-oost-nl-how-can-smes-get-started-ai/].
Chris Kamphuis (with Faegheh Hasibi and Arjen de Vries) had his paper on entity annotations for the MS Marco collections accepted for 46th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’23)! Furthermore, two full papers ("Cross-Market E-Commerce Question Answering by Jointly Ranking Questions and Products" by Negin Ghasemi, Arjen de Vries, Djoerd Hiemstra et al.; "Safe Deployment for Counterfactual Learning to Rank with Exposure-Based Risk Minimization" by Harrie Oosterhuis et al.) and a tutorial ("Recent Advances in the Foundations and Applications of Unbiased Learning to Rank" by Harrie Oosterhuis et al.) have been accepted at SIGIR'23 and will be presented in Taipei.
The paper titled "Fairness Gerrymandering: Unfair, Undetectable Manipulation" by Tim de Jonge and Djoerd Hiemstra was accepted at the annual ACM Conference on Fairness Accountability and Transparency (ACM FAccT 2023).
Combining Graphical and Algebraic Approaches for Parameter Identification in Latent Variable Structural Equation Models.
DaS: Ankur, Inge, Johannes - Published in the proceedings of the 26th International Conference on Artificial Intelligence and Statistics (AISTATS 2023)
A Simple Unified Approach to Testing High-Dimensional Conditional Independences for Categorical and Ordinal Data
DaS: Ankur, Johannes - Published in the Proceedings of the 37th AAAI Conference on Artificial Intelligence (AAAI-23)
DaS: Hideaki, Faegheh - Published in the Proceedings of the 31st ACM International Conference on Information and Knowledge Management (CIKM '22)
DaS: Faegheh - Published in the Proceedings of the 29th International Conference on Computational Linguistics (COLING '22)
DaS: Chris, Faegheh, Arjen - Published in the Proceedings of the International Conference on Design of Experimental Search & Information REtrieval Systems (DESIRES '22)
DaS: Emma, Faegheh, Arjen - Published in the Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '22)
Towards individualized monitoring of cognition in multiple sclerosis in the digital era: a one-year cohort study
DaS: Gabriel, TomH - Published in Multiple Sclerosis and Related Disorders
DaS: Gabriel, TomH - Published in European Journal of Human Genetics
DaS: Yuliya, TomH - Published in BMC Bioinformatics