Information Retrieval Group Hosts Visiting Research Students

The Information Retrieval group recently welcomed five visiting research students:

  • Giuseppe Spillo is a 3rd-year PhD student at the University of Bari, Italy, in the SWAP Research Group. His main research interests are Multimodal and Knowledge-aware Recommender Systems and Green AI. His visiting period at UoG focused on quantisation approaches in multimodal recommendation scenarios.
  • Marco Braga is a 3rd-year PhD student at the University of Milano-Bicocca, Italy, in the IKR3 Research Group. His main research interests are Efficient Tuning of Large Language Models on specific domains, tasks, or languages. During his visiting period at UoG, he focuses on adapting Information Retrieval models to specific domains.
  • Chiara Mallamaci is a final-year Master's student in Artificial Intelligence and Data Science at the Polytechnic of Bari. During her visiting period, she has been focusing on sequential recommendation systems, working on quantisation methods to improve recommendation efficiency.
  • Daniel Namaki is a final-year Master's student in Artificial Intelligence and Data Engineering at the University of Pisa. During his visiting research period, he has focused on enhancing answer quality in Retrieval-Augmented Generation systems through context diversification techniques.
  • Emmanouil Georgios (Akis) Lionis is a final-year Master's student in Artificial Intelligence at the University of Amsterdam (UvA), currently researching Long Document Retrieval by Learning Sparse Representations for his thesis. The visit was supported by the ELLIS honor program, which connects exceptional UvA research students with professors from other institutions. Akis will start as a PhD student at Glasgow in Fall 2025.

Upcoming workshop: Federated and Privacy Preserving AI in Biomedical Applications, July 2025

As part of IEEE ICDCS in Glasgow, Dr Fani Deligianni and Dr Idris Zakariyya are organising a workshop to discuss the latest advancements and challenges in applying federated learning and privacy-preserving AI techniques to biomedical applications. The focus will be on ensuring data privacy and security while enabling collaborative research and innovation in healthcare.

 

First published: 31 May 2025

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