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My first degree was in Cognitive Science at Sheffield University where I became interested in models of brain function and human and animal behaviour. I am also interested in learning AI models of time-series data in order to try and understand the underlying processes generating such data, with a focus on biological, clinical and ecological big data. My Ph.D at Birkbeck College, entitled "The Automatic Explanation of Multivariate Time Series" was sponsored by the Engineering and Physical Sciences Research Council; Honeywell Hi-Spec Solutions, UK; and Honeywell HTC, USA. I spent two Summers working at Honeywell HTC on research and development.

As a Senior Lecturer at Brunel University London I have worked in conjunction with Leiden University Medical School and UCL on gene regulatory networks and this work is currently being expanded to explore networks across systems of increasing complexity. My current projects include modelling high dimensional gene expression data (focussing on integration of multiple studies for wheat), modelling visual field test data from Moorfield’s Eye Hospital, and exploring the dynamics of fish populations in the Northern Atlantic using Hidden Markov Models in conjunction with the Canadian Department of Fisheries and Oceans and DEFRA.


Selected Invited talks • 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

Scutari, M., Vitolo, C. and Tucker, A. (2018) 'Learning Bayesian Networks from Big Data with Greedy Search'. Statistics and Computing. pp. 1 - 15. ISSN: 0960-3174 Open Access Link

Journal article

Ayed, S., Arzoky, M., Swift, S., Counsell, S. and Tucker, A. (2018) 'An Exploratory Study of the Inputs for Ensemble Clustering Technique as a Subset Selection Problem'.SAI Intelligent Systems Conference. London. 6 Proceedings of SAI Intelligent Systems Conference. pp. 1041 - 1055. ISSN: 2194-5357 Open Access Link

Conference paper

Alyousef, AA., Nihtyanova, S., Denton, C., Bosoni, P., Bellazzi, R. and Tucker, A. (2018) 'Nearest Consensus Clustering Classification to Identify Subclasses and Predict Disease'. Journal of Healthcare Informatics Research, 2 (4). pp. 402 - 422. ISSN: 2509-4971 Open Access Link

Journal article

Uusitalo, L., Tomczak, MT., Müller-Karulis, B., Putnis, I., Trifonova, N. and Tucker, A. (2018) 'Hidden variables in a Dynamic Bayesian Network identify ecosystem level change'. Ecological Informatics, 45. pp. 9 - 15. ISSN: 1574-9541

Journal article

Gredin, V., Bishop, D., Broadbent, D., Tucker, A. and Williams, A. (2018) 'Experts Integrate Explicit Contextual Priors and Environmental Information to Improve Anticipation Efficiency'. Journal of Experimental Psychology: Applied, 24 (4). pp. 509 - 520. ISSN: 1939-2192 Open Access Link

Journal article
More publications(111)