We are pleased to announce that Dr Jose Sousa, Research Team Leader for Personal Health Data Science, from the Sano Centre for Computational Medicine, Krakow will be visiting the Insigneo Institute on 4 December 2023 and will give a talk on 'From small data to big impact on personalised health: A case for AI lifelong learning'.

If you are attending in person, please join us afterwards for informal discussions with Dr Sousa. Refreshments will be available.

Abstract: 

Current AI research predominantly focuses on big data, which introduces challenges related to model explainability, limited access to extensive datasets, model complexity, the necessity for normalisation techniques, and requirements for data annotation and collection. These challenges contrast with human decision-making, which hinges on small data, explicability, and handling of uncertainty.

This seminar will introduce an AI Lifelong Learning approach that operates effectively with small data, offers explainable outcomes, and obviates the need for new data gathering or annotation. To witness the practicality of this approach, join us at the seminar where its application to a problem of interest, personalised health, will be demonstrated.

Biography: 

Jose Sousa managed the Advanced Informatics Core Technology Unit at QUB's Faculty of Medicine, Health, and Life Sciences. He earned a PhD from the University of Minho, Portugal, under the guidance of Profs. Ricardo Machado and Jose Mendes, focusing on complex network models for software alignment with project requirements. Before and during his PhD, he served as the Information Systems Director at I3S, a research institution of the University of Porto, handling IT infrastructure and software development. Jose Sousa worked as an AI researcher at HDRUK – Swansea/QUB and collaborated with the QUB Centre for Public Health, developing self-learning AI for pandemic response and conducting research on genetic alignment modelling and socio-technical systems. At Sano, Jose Sousa leads the Personal Health Data Science team in the development of AI technologies aimed at improving disease management, following the "Citizen Before Patient" vision.