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.
The paper titled "Find the Funding: Entity Linking with Incomplete Funding Knowledge Bases" by Faegheh, in collaboration with Gizem, Amin, and Georgios, has been accepted at COLING 2022. Check out its arXiv version at https://arxiv.org/abs/2209.00351 if you are interested.
Harrie Oosterhuis from the Data Science group at iCIS received the best paper award at the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2021) for his solo-authored paper titled "Computationally Efficient Optimization of Plackett-Luce Ranking Models for Relevance and Fairness". The paper can be found here.
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: 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
DaS: Harrie - Published in IJCAI 2021 proceedings
DaS: Hideaki, Faegheh, Arjen - Published in the Proceedings of the 44th international ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '21)