Dr Starkey completed his PhD in the application of artificial intelligence techniques to engineering problems from University of Aberdeen in 2001 and attained an Honours degree in Applied Mathematics from St Andrews University in 1993. Since then he has been awarded an Enterprise Fellowship from Royal Society of Edinburgh and Scottish Enterprise, and has a spinout company BlueFlow Ltd that commercialises the AI technology developed.
Dr Andrew Starkey is CEO of a recent spin-out company from the University of Aberdeen, BlueFlow Ltd.
Dr Starkey's main research themes are in the development of Explainable AI, Green AI and Autonomous AI. He has developed a number of novel methods in these themes and also works closely with industry in a range of areas.
Examples of previous research include robotics, econometrics (the study of financial markets), bioinformatics (in particular genomic and proteomic analysis), engineering problems and the analysis of seismic data and the integration of AI and virtual reality.
I am currently accepting PhDs in Engineering.
Please get in touch if you would like to discuss your research ideas further.
- Artificial Intelligence
- Knowledge and Information Systems
- Machine Learning
Our research specialisms are based on the Higher Education Classification of Subjects (HECoS) which is HESA open data, published under the Creative Commons Attribution 4.0 International licence.
Current research projects:
Developing AI technologies that are explainable (XAI) and low in computational cost (Green AI).
- Development of methods for the automated analysis of relevant features for a problem (Feature selection)
- Development of Autonomous learning for Robotics applications
- Development of techniques to abstract knowledge from an agent's interactions with its environment
- Text analysis, and in particular topic analysis and contextual analysis. Novel techniques developed that can dynamically identify new topics being discussed (or old topics no longer being talked about).
- Multi-label classification engines, using XAI and low computation models
- Development of novel method capable of automatically identifying and describing features of interest for a class, plus best in class predictive capability (XAI, and Green AI).
In the past, a major research topic was the Granit project, which involved the application of AI to the condition monitoring of ground anchorages. This project resulted in a number of awards including the Millennium Product Award and the John Logie Baird Award for Innovation.
Funding and Grants
Current projects include:
- Investigating data mining methods applied to econometrics
- In silico identification of functional human cis-regulatory sequence-gene linkage (funded by BBSRC) joint project with Dr Alasdair MacKenzie and Scott Davidson
- a grant from the BBSRC Research Equipment Initiative for a computer rack system to facilitate the computations required for textual bioinformatic approaches
- Analysis of seismic data for automated recognition of geological features, joint project with Dr Anne Schwab
- “Design and assessment of condition of soil anchorages in a dynamic environment using the centrifuge modelling technique” funded by EPSRC, jointly with Drs Ivanovic and Neilson and Prof Rodger and also Prof Davies of University of Dundee
- Investigation into genomic prediction for melatonin action in animals, joint project with Dr David Hazlerigg
- “Pattern recognition approaches to understand replication origin specification”, joint project with Dr Anne Donaldson and Dr Conrad Nieduszynski
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Automated Well Log Pattern Alignment and History-Matching Techniques: An Empirical Review and RecommendationsPetrophysics, vol. 64, no. 1, pp. 115-129Contributions to Journals: Articles
Assessing the Stability and Selection Performance of Feature Selection Methods Under Different Data ComplexityInternational Arab Journal of Information Technology, vol. 19, no. Special Issue 3A, pp. 442-455Contributions to Journals: Articles
- [ONLINE] DOI: https://doi.org/10.34028/iajit/19/3A/4
- [ONLINE] View publication in Scopus
An Unsupervised Autonomous Learning Framework for Goal-directed Behaviours in Dynamic ContextsAdvances in Computational Intelligence, vol. 2, 26Contributions to Journals: Articles
Stability and Accuracy of Feature Selection Methods on Datasets of Varying Data ComplexityChapters in Books, Reports and Conference Proceedings: Conference Proceedings
Short Text Classification using Contextual AnalysisIEEE Access, vol. 9, pp. 149619 - 149629Contributions to Journals: Articles
Review of Classification Algorithms with Changing Inter-Class DistancesMachine Learning with Applications, vol. 4, 100031Contributions to Journals: Review articles
Painting Music with Artificial IntelligenceNon-textual Forms: Web Publications and Websites
Painting Music: Using artificial intelligence to create music from live painted drawingsDrawing: Research, Theory, Practice, vol. 5, no. 2, pp. 209-224Contributions to Journals: Articles
Analysing fake news titles for 2016 Trump-Hillary campaign using contextual-based approaches in text analyticsInternational Journal of Advanced Research Trends in Engineering and Technology, vol. CAT PART - 1 2020, no. Editor's Issue, CATI1P219Contributions to Journals: Articles
Long range guided waves for detecting holes in pipelinesJournal of Structural Integrity and Maintenance, vol. 5, no. 2, pp. 113-126Contributions to Journals: Articles