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 Data Systems, Data 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.
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Academic Staff & Members
News & Events
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28 MayOur Distributed AI paper 'CL-FML: Cluster-based & Label-aware Federated Meta-Learning for On-Demand Classification Tasks' authored by Aladwani, T., Anagnostopoulos, C. , Puthiya Parambath, S. and Deligianni, F has been accepted in the 1th IEEE International Conference on Data Science and Advanced Analytics (DSAA 2024), San Diego, CA, United States, 6-10 October 2024. Keywords: Federated Learning, Meta-Learning, Clustering, Data Augmentation.
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14 Jun
We are hiring! A new tenure-track faculty opening
We are hiring! Great opportunity for talented and ambitious academics interested in Distributed/data-centric AI and Knowledge-based Systems. We have a new tenure-track faculty opening at University of Glasgow School of Computing Science in the general areas of Data Systems, Data-centric AI, and Knowledge & Data Engineering. Applicants whose research expertise bridges Data Engineering, Artificial Intelligence and Knowledge Discovery are particularly welcome. Deadline: 10th July. -
02 Jul
Our DMKD Sequential Learning paper is accepted!
Our Sequential Learning paper 'Sequential Query Prediction based on Multi-Armed Bandits with Ensemble of Transformer Experts and Immediate Feedback', has been accepted in Data Mining and Knowledge Discovery journal, authored by S Parambath, C Anagnostopoulos, and R Murray-Smith. Keywords: Multi-armed bandits, Query recommendation, Immediate User Feedback, Large Language Models (LLMs), Transformers. -
28 May
ECML PKDD 2024 Paper!
Our Distributed AI paper 'The Price of Labelling: A Two-Phase Federated Self-Learning Approach', has been accepted in ECML PKDD 2024At: September 9-13, Vilnius, authored by T Aladwani, S Parambath, C Anagnostopoulos, F Delignianni. Keywords: Federated Learning, Self-learning, Pseudo-labeling, Data Augmentation. -
06 Sep
New Distributed AI Paper on Decentralized & Personalized Federated Learning
Our Distributed AI paper 'Decentralized Personalized Federated Learning based on a Conditional Sparse-to-Sparser Scheme' authored by Qianyu Long, Qiyuan Wang, Christos Anagnostopoulos, and Daning Bi is now available on arXiv (arXiv:2404.15943). Keywords: Distributed AI, Dynamic aggregation, Personalized Federated Learning.