Visual Artificial Intelligence Laboratory (VAIL)

About us

The Visual Artificial Intelligence Laboratory was founded in 2012 by Professor Cuzzolin under the name of 'Machine Learning' (and later 'Artificial Intelligence and Vision') research group, and has since conducted work at the boundaries of human action recognition in computer vision. Prof Cuzzolin is a leading scientist in the mathematics of uncertainty, in particular random set and belief function theory.

Our research interests span a number of frontier topics in:

  • computer vision (action and activity detection, future event prediction, video captioning and scene understanding)
  • machine learning (continual learning, federated learning, self-supervision and metric learning)
  • artificial intelligence (epistemic AI and machine theory of mind, but also neurosymbolic AI),
  • robotics (with a focus on surgical robotics), autonomous driving (the detection of road events for situation awareness)
  • AI for healthcare (the monitoring of people in care homes, the early diagnosis of dementia, empathetic healthcare via theory of mind).

More information about VAIL

Research impact

Road event detection in autonomous driving, with colored boxes around the relevant road agents to be detected

The group has built, in just a few years, a leadership position in the field of deep learning for action detection, with some of the best detection accuracies to date and the first ever system able to localise multiple actions on the image plane in (better than) real time. The team's effort is now shifting towards topics at the frontier of computer vision, such as future action prediction, deep video captioning and the development of a theory of mind for machines.

The Lab currently runs on a budget of around £3M (not fully incorporating the €4.3M Horizon 2020 project SARAS or the €3M FET Epistemic AI we are coordinating), with currently eight live projects funded by Horizon 2020, the Leverhulme Trust, Innovate UK, Huawei Technologies, UKIERI, and the School of Engineering, Computing and Mathematics. The budget is projected to further significantly increase in 2021.

Prof Cuzzolin's reputation in uncertainty theory and belief functions comes from the formulation of a geometric approach to uncertainty in which probabilities, possibilities, belief measures and random sets are represented and analysed by geometric means. This has recently developed into an effort to reshape the foundations of artificial intelligence to better incorporate and model second-order, 'epistemic' uncertainty: an approach that we call Epistemic Artificial Intelligence.


Fabio Cuzzolin

Fabio Cuzzolin

Professor of Artificial Intelligence

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Name Role Email
Dr Andrew Bradley Senior Lecturer
Dr Alexander Rast Lecturer in Computing
Dr Matthias Rolf Senior Lecturer


Active projects

Project title and description Investigator(s) Funder(s) Dates

Epistemic Artificial Intelligence

Although artificial intelligence (AI) has improved remarkably over the last years, its inability to deal with fundamental uncertainty severely limits its application. This proposal re-imagines AI with a proper treatment of the uncertainty stemming from our forcibly partial knowledge of the world.

Fabio Cuzzolin Horizon 2020 From: March 2021
Until: February 2025

Smart Autonomous Robotic Assistant Surgeon (SARAS)

SARAS aims at developing the next-generation of surgical robotic systems that will allow a single surgeon to execute Robotic Minimally Invasive Surgery (R-MIS) without the need of an expert assistant surgeon.

Fabio Cuzzolin Horizon 2020 From: January 2018
Until: December 2021

Knowledge Transfer Partnership with Createc

Fabio Cuzzolin Innovate UK From: May 2019
Until: April 2021

Artificial intelligence for autonomous driving

Dr Andrew Bradley, Fabio Cuzzolin From: March 2019

Some novel paradigms for analyzing human actions in complex videos

Fabio Cuzzolin From: April 2018
Until: August 2021

Theory of mind at the interface of neuroscience and AI

Fabio Cuzzolin Leverhulme Trust From: February 2020
Until: December 2023

Deep learning for complex activity detection in videos

Fabio Cuzzolin From: February 2020
Until: February 2023

Knowledge Transfer Partnership with Supponor

Fabio Cuzzolin, Dr Alexander Rast Innovate UK