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. 

Information, Data and Analysis Section

Data-centric AI

Distributed AI

Edge Computing

Data Management Systems

 

 

Research Topics

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

 

Research Projects & Materials

University of Glasgow cloisters

Academic Staff & Members

News & Events

  • 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)
  • 09 Jun

    IEEE BigData 2024 Paper accepted!

    Our paper titled 'LIFE: Leader-driven Hierarchical & Inclusive Federated Learning' has been accepted for publication in IEEE BigData 2024, December 15-18, 2024, Washington DC, USA
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