Knowledge & Data Engineering Systems
The Knowledge & Data Engineering Systems (KDES) research group is part of the Information, Data and Analysis (IDA) Section. KDES brings together the fundamental research areas of Distributed Computing, Knowledge Engineering, and Data Science.
KDES's strength lies in the spectrum of theoretical backgrounds and applications ranging from large-scale Distributed Computing and Information Systems, to Edge Computing , Distributed Machine Learning/AI, and Data-centric AI, focuses on building innovative distributed data science and engineering systems.

Research Topics
- Distributed Data Management
- Distributed AI & Federated Machine Learning & Reusability
- Large-scale Data Analytics
- Information Processing Systems

Academic Staff & Members
- Academic Staff
- Affiliate / Associate Academics
- Researchers
- Interns / Alumni / External Collaborators
News & Events
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15 JanOn January 15, 2025, the TERRA project launched with a dynamic kick-off meeting at the University of Thessaly, Greece. This milestone marks the beginning of a visionary initiative funded by Horizon Europe to tackle global water-related challenges such as pollution, flooding, and coastal erosion.
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03 Dec
New paper in Data Mining and Knowledge Discovery!
Shameem Puthiya Parambath, Christos Anagnostopoulos, Saleh Abdullah M Alfahad, 'Thompson Sampling-based Recursive Block Elimination for Dynamic Assignment under Limited Budget in Pure-Exploration ', Data Mining and Knowledge Discovery, 2024 -
09 Jun
IEEE ICDM / DMF 2024 Paper accepted!
Our paper titled 'Sequential Block Elimination for Dynamic Pricing' has been accepted for publication in IEEE ICDM/DMF 2024, December 9-12 December 2024, Abu Dhabi, UAE. -
21 Nov
T Aladwani's PhD Viva Success!
Congratulations to T Aladwani for having her PhD Viva passed! Thesis title: 'Enhancing Data Representation in Distributed Machine Learning' (supervisors: Dr C Anagnostopoulos, Dr F Deligianni) -
12 Nov
Q Long's PhD Viva Success!
Congratulations to Q Long for having his PhD Viva passed! Thesis title: 'Collaborative Distributed Machine Learning: From Knowledge Reuse to Sparsification in Federated Learning' (supervisors: Dr C Anagnostopoulos, Dr F Deligianni)