Dr Andrew Starkey
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 true Artificial Intelligence methods, in particular in the areas of automated AI (resulting in automated data mining), and autonomous learning (where te AI method can learn and adapt to any given environment). He has developed novel methods for automatically determining features of interest.
These techniques have been successfully applied to a number of different fields, including 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.
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
Page 1 of 5 Results 1 to 10 of 42
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
Predicting Supervise Machine Learning Performances for Sentiment Analysis Using Contextual-Based ApproachesIEEE Access, vol. 8, pp. 17722-17733Contributions to Journals: Articles
Unsupervised Temporospatial Neural Architecture for Sensorimotor Map LearningIEEE Transactions on Cognitive and Developmental SystemsContributions to Journals: Articles
Machine Autonomy: Definition, Approaches, Challenges and Research GapsIntelligent Computing. Arai, K., Bhatia, R., Kapoor, S. (eds.). Cham: Springer pp. 335-358, 24 pages.Chapters in Books, Reports and Conference Proceedings: Conference Proceedings
Semi-automated data classification with feature weighted self organizing mapICNC-FSKD 2017 - 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery. Institute of Electrical and Electronics Engineers Inc. pp. 136-141, 6 pages.Chapters in Books, Reports and Conference Proceedings: Conference Proceedings
Application of feature selection methods for automated clustering analysis: a review on synthetic datasetsNeural Computing and Applications, vol. 29, no. 7, pp. 317-328Contributions to Journals: Articles