Georgios Leontidis
Image:
Petter Bjørklund / SFI Visual Intelligence

Georgios Leontidis

Meet Georgios, our new Professor in Machine Learning

We warmly welcome Georgios Leontidis as a new Professor at SFI Visual Intelligence's hub at UiT The Arctic University of Norway.

Meet Georgios, our new Professor in Machine Learning

We warmly welcome Georgios Leontidis as a new Professor at SFI Visual Intelligence's hub at UiT The Arctic University of Norway.

By Petter Bjørklund, Communications Officer at SFI Visual Intelligence

Georgios, originally from Greece, was a Full Professor in Machine Learning at the University of Aberdeen. He served as the Founding Director of the Interdisciplinary Institute’s Data and AI stream.  Additionally, he co-directed the NOK 190M UKRI AI Centre for Doctoral Training in Sustainable Understandable Agri-food Systems Transformed by Artificial Intelligence, known as SUSTAIN.

From machine learning foundations to real-world impact

Georgios has a multidisciplinary background blending engineering, biomedical informatics, computer science and machine learning.  He earned a BSc in Electronic Engineering and an MSc in Informatics in Medicine and Biology both in Greece.  Later he completed his PhD in Computer Science and Machine Learning at the University of Lincoln in the UK.

His work spans both the theoretical development of machine learning methods and their translation into practical systems for industry and innovation. Over the past 15 years, he has worked on deep learning, self-supervised learning, domain adaptation, capsule networks, transformers, uncertainty estimation, federated learning, and explainable AI.

"These methods have been applied across several domains, including agri-food systems, energy, environmental science, biodiversity, medical imaging, industrial systems, and sustainable decision-making," Georgios explains.

AI for sustainable, reliable, and understandable decision-making and innovation

At Visual Intelligence and the newly established [AI]2 – Arctic Institute for AI in Science and Innovation, which he co-directs, Georgios will focus on research related to limited training data, confidence and uncertainty, as well as explainability and reliability.  His work also closely aligns with several of Visual Intelligence’s innovation areas including energy, earth observation, medicine and health and marine and environmental applications.

“My research has always been motivated by the question of how we can make AI useful in the real world, especially when data are incomplete, noisy, distributed, or difficult to interpret,” Georgios says.

He is especially interested in AI systems that can operate under real-world constraints. In many scientific and industrial settings, data are not available in the clean, large-scale, fully labelled form that many AI methods assume.

"Instead, they may be fragmented across organisations, affected by changing environments, collected from heterogeneous sensors, or embedded in complex physical, biological, and social systems," Georgios says.

For Georgios, this is where SFI Visual Intelligence has a crucial role to play.

“Developing AI that is robust, uncertainty-aware, interpretable, and able to learn from limited or imperfect data is essential if we want these systems to support high-stakes decision-making,” he explains.

“This is particularly important in areas such as climate resilience, healthcare, energy systems, defence, and environmental monitoring, where mistakes can have significant social, economic, or ecological consequences,” Georgios concludes.

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