Research Themes

Belief Functions and
Imprecise Probabilities
Computer Vision
(Applied) Machine Learning
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.
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.
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.
The AI and Vision group collaborates very closely with the Cognitive Robotics group, led by HoD Dr Nigel Crook. 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 heavily involved in the Intelligent Transport Systems doctoral training programme for autonomous navigation.