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Allan Tucker is Reader in the Department of Computer Science where he heads the Intelligent Data Analysis Group consisting of 17 academic staff, 15 PhD students and 4 post-docs. He has been researching Artificial Intelligence and Data Analytics for 21 years and has published 120 peer-reviewed journal and conference papers on data modelling and analysis. His research work includes long-term projects with Moorfields Eye Hospital where he has been developing pseudo-time models of eye disease (EPSRC - £320k) and with DEFRA on modelling fish population dynamics using state space and Bayesian techniques (NERC - £80k). Currently, he has projects with Google, the University of Pavia Italy, the Royal Free Hospital, UCL, Zoological Society of London and the Royal Botanical Gardens at Kew. He is academic lead on an Innovate UK, Regulators’ Pioneer Fund (£740k) with the Medical and Health Regulatory Authority on benchmarking AI apps for the NHS. He serves regularly on the PC of the top AI conferences (including IJCAI, AAAI, and ECML) and is on the editorial board for the Journal of Biomedical Informatics and Medical Informatics and Decision Making. He is hosting a special track on "Explainable AI" at the IEEE conference on Computer Based Medical Systems in 2019. He has been widely consulted on the ethical and practical implications of AI in health and medical research by the NHS, and the use of machine learning for modelling fisheries data by numerous government thinktanks and academia.


Selected Invited talks • Transparency and the Importance of Explanation, International Congress on the Exploration of the Sea ASC 2019 • How to Analyse Big Data, The Royal Society of Medicine (KEYNOTE), 2014 • Artificial Intelligence, Useful Tools or Robot Overlords, Skeptics in the Pub, London 2014 • Machine Learning Approaches to Modelling Fisheries Data, Plymouth Marine Laboratory, 2013 • The Intelligent Data Analysis of Natural Process Data, Harbin Institute of Technology, China (also to Makerere University School of Computing and IT, Uganda via Skype) 2012 • Integrating marine biomass data using foodweb expertise, British Ecological Society, 2012 • Probabilistic Models for Understanding Ecological Data: Case studies in Seeds, Fish and Coral, Computational Sustainability, 2012 (KEYNOTE) • Bioinformatics tools in predictive ecology: applications to fisheries, The Royal Society 2011(KEYNOTE)

Program Committees I am / have been on the program committee for many international conferences including: • Knowledge Discovery in Databases - KDD (Research), • AI in Medicine (Board Member), • American Association for AI conference - AAAI, • International Joint Conference in AI - IJCAI • General chair for the A ranked (ERA) symposia IDA 2013 (Council Member),

I review for numerous journals including: • Nature Protocols, • PLOS ONE, • IEEE Transactions on Evolutionary Computation, • AI in Medicine, • Journal of Biomedical Informatics (on Editorial Board)

Newest selected publications

Tucker, A., Wang, Z., Rotalinti, Y. and Myles, P. (2020) 'Generating High-Fidelity Synthetic Patient Data for Assessing Machine Learning Healthcare Software'. npj digital medicine.Open Access Link

Journal article

Ghoshal, B., Tucker, A., Sanghera, B. and Wong, WL. (2020) 'Estimating Uncertainty in Deep Learning for Reporting Confidence to Clinicians in Medical Image Segmentation and Diseases Detection'. Computational Intelligence. ISSN: 0824-7935

Journal article

Dagliati, A., Geifman, N., Peek, N., Holmes, JH., Sacchi, L., Bellazzi, R., et al. (2020) 'Using topological data analysis and pseudo time series to infer temporal phenotypes from electronic health records'. Artificial Intelligence in Medicine, 108 (August 2020). pp. 1 - 14. ISSN: 0933-3657 Open Access Link

Journal article

Yousefi, L., Swift, S., Arzoky, M., Saachi, L., Chiovato, L. and Tucker, A. (2020) 'Opening the black box: Personalizing type 2 diabetes patients based on their latent phenotype and temporal associated complication rules'. Computational Intelligence. ISSN: 0824-7935 Open Access Link

Journal article

Ghoshal, B., Lindskog, C. and Tucker, A. (2020) 'Estimating Uncertainty in Deep Learning for Reporting Confidence: An Application on Cell Type Prediction in Testes Based on Proteomics'. Springer International Publishing. pp. 223 - 234. ISSN: 0302-9743

Conference paper
More publications(134)