The current Board of the Data Science Platform, for the two-year term Jan 2023 - Dec 2024, consists of the following members (in alphabetical order):

• Charlie Abela
  • Charlie’s research interests lie in the area of large-scale graph data mining and machine learning. In particular he focuses on methods for deep graph analysis, graph representation learning and knowledge graphs with the goal of developing efficient and practical approaches for applications within several domains including the humanities, healthcare and manufacturing.
• John Abela
  
  • John is a computer scientist by training but was actively involved in industry for over two decades.  His main Data Science interests are: predictive modelling, machine (and deep) learning, text mining, natural language processing, machine vision, and data visualization.
• Johann Briffa - Chair
  • Johann's research interests involve Coding Theory (both Error Control and Data Compression),  Signal / Image Processing, and High-Performance / GPU Computing. His interests in data science are therefore concerned mostly with applications of signal and image processing, as well as the optimisation of implementations on high-performance systems.
• Jean Paul Ebejer
  
  • JP's main interest in data science is its application to bioinformatics and cheminformatics. Specifically the building of computational models for the discovery of new drugs and to gain novel molecular biology insights. His group has successfully applied deep learning and transfer learning approaches to virtual screening problems.
• Adrian Muscat
  
  • From a strictly data science perspective, Adrian is mainly interested in building and developing machine learning models for predictive analytics (and likewise for automation), making use of both tabular (structured) data and unstructured data, mainly image, video and text data and their fusion. From a research perspective his interest intersects with artificial intelligence and computational cognitive science in a bid to improve the machine learning models perception and enable reasoning.  
• Kenneth Scerri - Coordinator
  • Kenneth's methodological work focuses on signal processing with special interest in temporal and spatio-temporal analysis and modelling. This interest has brought contributions in various fields including transportation, biomedical analysis, industrial anomaly detection and environmental studies.
• Gianluca Valentino
  
  • Gianluca's interest in Data Science lies in research into machine learning techniques related to computer vision, anomaly detection, reinforcement learning and time series analysis, as well as data-driven modeling and analysis for operational environments. He is interested in the application of these techniques to any domain, but in particular to particle accelerators, Earth Observation, bioinformatics and aerospace.