Artificial Intelligence Research Group

The Artificial Intelligence group is conducting research that advances the state-of-the-art in intelligent systems, applying reasoning, learning and explainability mechanisms to real-world data science applications.

The group conducts internationally recognised research in: case-based reasoning, machine vision, intelligent sensing, semantic technologies and natural language understanding.

A key focus of our research is addressing the question of how to extract context-aware knowledge from data to develop intelligent systems that support decision-making.

The AI research group has been active for over 20 years and has a strong international record in applied research that is highly relevant to industry; e.g. oil & gas, Aerospace, medical decision support and digital health systems, pharmaceutical product design, on-line recommenders, and tourism information systems. Our research has attracted funding worth £4M+ from EPSRC, FP7, Horizon2020, InnovateUK, UKIERI and SFC and innovation centres.

Current Funded Projects

  • selfBACK project: funded by H2020 is a healthcare programme centred round self-management and has the potential to save both time and monetary resources in the field of low back pain. Case-based reasoning is used to recommend a personalised self-management plans which the patient receives through a smartphone app, providing advice to reinforce and monitor adherence.
  • Artificial Intelligence in Fracture Detection: funded by the SBRI and the Scottish Government, this project (in partnership with is aimed at developing machine learning and computer vision algorithms which aid in the diagnostics of fractures within data collected by NHS Grampian. We will explore concepts such as multi-agent systems, transfer learning and Natural Language Processing to further extend the capabilities of the classification.
  • IntelliScan: funded by SBRI is a system to automate language and content generation for the Citizens Advice Scotland.
  • FitSense: funded by Data Lab aims to develop an effective falls prediction system that can be installed in peoples’ homes. This system will help residents live well and independently in their homes for longer, prevent hospital admissions and enable early discharge.
  • Prophecy: funded by OGIC
  • Complyants: funded by OGIC
  • LiberEat Data Analytics: Funded by The Data Lab, this project is intended to design systems and methodologies that aid the LiberEat App to better collect, understand and analyse the data from its customers, so that these can be presented to new stakeholders (i.e. restaurants, food retailers, healthcare) in novel and relevant ways. This project will also consider the rights of protection and anonymisation of the customers’ data, as well as the artificial intelligence used to automatically extract and process such data.
  • FitChat: funded by EPSRC GetAMoveOn Network+
  • Attendr: funded by RGU EIG

Previous Funded Projects

  • Childhood Obesity Data Scoping Challenge: funded by The Data Lab, this project was dedicated to perform a data scoping amongst a wide variety of sources (scientific and gray literature, industrial outputs, government records, etc.) to understand where is it possible to find information about the direct and indirect causes of childhood obesity, using the UK as a case of study. This will lead to future projects in collaboration with UNICEF (, as the collected data will be used to train machine learning algorithms and to design better guidelines for childhood obesity prevention in other countries.
  • Opinion Detection in Product Reviews: funded by KTP this project helps enterprises to uncover and summarise business-critical insights from customer experience data. The aim of this project is to develop a commercial software system to accurately detect and understand topics and opinions in various customer conversations such as product reviews, complaint emails, or voice calls.

Group Members

Current Research Students