Faculty
2025: UdS-CS Busy Beaver Teaching Award
2024: ELLIS Fellow
2018: IEEE ICDM Tao Li Award for Excellence in Research
2018: IEEE ICDM Best Paper Award
2010: ACM SIGKDD Doctoral Dissertation Runner-Up Award
Prof. Dr. Jilles Vreeken is tenured faculty at the CISPA Helmholtz Center for Information Security, where he leads the Exploratory Data Analysis group. In addition, he is Honorary Professor of Computer Science at Saarland University, Fellow of the ELLIS Society, and Faculty of the ELLIS Unit Saarbrücken. His research interests include causal inference, machine learning, and data mining. He is particularly interested in developing well-founded theory and efficient methods for gaining insight into data and models. He has authored over 130 conference and journal papers, received three best paper awards, two teaching awards, the ACM SIGKDD 2010 Doctoral Dissertation Runner-Up Award, and the IEEE ICDM 2018 Tao Li Award for Excellence in Research.
He obtained his Ph.D. in Computer Science in 2009 from Universiteit Utrecht. Between 2009 and 2013 he held a Post-doctoral Fellowship of the Research Foundation Flanders (FWO) at the University of Antwerp. Before joining CISPA in 2018, he was an independent research group leader at the DFG Cluster of Excellence on Multimodal Computing and Interaction (MMCI) of Saarland University and a Senior Researcher at the Max Planck Institute for Informatics.
Association for Computing Machinery (ACM)
Succinct Interaction-Aware Explanations
SIAM International Conference on Data Mining (SDM)
Accurately Estimating Unreported Infections using Information Theory.
National Conference of the American Association for Artificial Intelligence (AAAI)
Federated Binary Matrix Factorization Using Proximal Optimization
National Conference of the American Association for Artificial Intelligence (AAAI)
From Your Block to Our Block: How to Find Shared Structure Between Stochastic Block Models over Multiple Graphs
Proceedings of the AAAI Conference on Artificial Intelligence
International Conference on Artificial Intelligence and Statistics (AISTATS)
Information-Theoretic Causal Discovery in Topological Order
Conference on Neural Information Processing Systems (NeurIPS)
Causal Discovery from Event Sequences by Local Cause-Effect Attribution
ACM International Conference on Knowledge Discovery and Data Mining (KDD)
Learning Causal Networks from Episodic Data
International Conference on Artificial Intelligence and Statistics (AISTATS)
Identifying Confounding from Causal Mechanism Shifts.
International Conference on Machine Learning (ICML)
Learning Exceptional Subgroups by End-to-End Maximizing KL-Divergence.