His research interests are machine learning, knowledge representation and reasoning, and their applications in image, video, spectra and text data analyses and more recently education. He played an important role in the development of an algebraic framework for machine learning and knowledge representation, Lattice Machine; the original concept of contextual probability for probabilistic modelling, which can be used for uncertainty reasoning/quantification, probability estimation and machine learning; a generic similarity measure, neighbourhood counting, and its specialisations on multivariate data, sequences, tree and graph structures. He is currently leading two research programmes: (1) contextual probability for decentralised learning and causal learning; (2) discovery AI to support the discovery of signatures (e.g., biomarkers), causalities and hypotheses. He has over 370 publications in these areas. 

He is principal investigator of 11 regional, national and international projects (totalling £5.2m) in the areas of image/video analytics (EPSRC funded MVSE 2021-2024, Horizon 2020 funded DESIREE and ASGARD, FP7 funded SAVASA, Royal Society funded VIAD), spectral data analytics (EPSRC funded VIPIRS on virus detection 2020-2022), text analytics (INI funded DEEPFLOW, Royal Society funded BEACON), and intelligent content management (FP5 funded ICONS); and is co-investigator of several other EU funded projects (totalling £13m). 

Prior to joining Queen’s, he was Professor of Computer Science in the School of Computing, Ulster University, in various roles including Head of the AI Research Centre, and Research Director. He is an associate editor of IEEE Transactions on Cybernetics, founding Chair of IEEE SMCS Northern Ireland Chapter (2009-2018), and a member of IEEE SMCS Board of Governors (2010-2013). 

More details can be found from his Discovery AI Lab.