Research Themes

Uncertainty Theory

Dr Cuzzolin is a world expert in the theory of belief functions, having published two monographs on the topic. His main contribution is a geometric approach to uncertainty theory in which every measure can be represented as a point of an appropriate convex space. The group is active on a number of topics in this field, including: probability and possibility transformation for efficient computation, decision making, the algebra of decision spaces (frames of discernment), and the total belief theorem.

Computer Vision

The AI and Vision group, in close collaboration with Oxford University's Torr Vision Group, has a multi-year experience in hot computer vision topics such as action, gesture and activity recognition, pose estimation, segmentation and matching of articulated bodies, voxelset analysis, video retrieval. Machine learning techniques employed range from deep learning and convolutional neural networks, to hidden Markov models, metric learning, dimensionality reduction, and discriminative part-based models.

Machine Learning

The group is active in machine learning (in particular metric learning for dynamical models, weakly supervised classification, imprecise dynamical models), and its application to problems such as big data, gait and daily activity analysis for dementia diagnosis and monitoring, vehicle classification via inductive loops, and activity localisation and recognition. More recently our interests are moving towards continual, self-supervised and federated learning.

Artificial Intelligence

Artificial intelligence is already part of our lives. Smart cars will engage our roads in less than ten years' time; shops with no checkout, which automatically recognise customers and what they purchase, are already open for business. But to enable machines to deal with uncertainty, we must fundamentally change the way machines learn from the data they observe so that they will be able to cope with situations they have never encountered in the safest possible way. Interacting naturally with human beings and their complex environments will only be possible if machines are able to put themselves in people's shoes: in other words, to read our minds.


The early recognition of the various forms of dementia is a difficult task, as mild symptoms and other health issues with similar traits can complicate their clinical evaluation.
We propose an integrated framework for the early discriminative assessment and monitoring of dementia, based on the development of novel machine learning methodologies for classifying time series and unconstrained videos, and their application to both daily activities and laboratory tasks involving a combination of gait and cognitive tasks, which have demonstrated an ability to discriminate in early stages.


The Visual AI Lab collaborates very closely with the Cognitive Robotics group, led by Professor Nigel Crook and Dr Matthias Rolf. In particular, we are interested in human-robot interaction, EEG classification coupled with humanoid robots for the creation of emotional robot avatars, and robot assistants for laparoscopic surgery.
The two groups are involved in the Intelligent Transport Systems doctoral training programme, and collaborate with the Autonomous Driving research group led by Dr Andrew Bradley.