Our core capability is being able to understand and exploit data from a variety of sources. This requires a systematic approach to data exploration, preparation and processing to achieve the desired outcomes. Our particular expertise is in finding and combining the right data to solve customer problems, whether that be from satellite imagery, IoT sensors or bespoke data soruces.

We routinely find and exploit the right data sources for our customers. This involves detailed data analytics to explore data, preparing selected data sets and then applying appropriate algorithms to exploit the insight that can be gained. To achieve this we have a dedicated team of data scientists who work with customer, commercial and publicly available data to solve problems.

Creative Data Science

By applying a systematic approach to data science, we can quickly find and evaluate data sources to see if they help in solving customer problems. However, while being systematic is the key to squeezing the most out of data and understanding its usefulness, an influential aspect of data science is creativity. By being creative, our data scientists can look at a problem and explore different ways in which it might be solved. This leads us to different data sets that may be of use, and different techniques which may bring better accuracy or insight, from statistical analysis through to machine learning and beyond.

Systematic Evaluation

Of course, creativity is important, but being systematic is crucial. By exhaustively evaluating each step of our process, we ensure that the right data sets are selected, that they are evaluated fairly, that the data is pre-processed correctly and subsequently that each algorithm which we apply is then compared on an equal footing. Ultimately this means that our customers have confidence in our solutions and that we can accurately detail the performance of the algorithms we apply. 

Case Studies & News

Services

Publications

Al-Khalili, J., Smith, A., Sen, P. (2017) "Gravity and Me: the Force that Shapes our Lives" BBC 4 science programme using an iOS and Android app to measure local relativity effects

Arscott, D., Venturini, B., Cheong Took, C., Templeton, M., Babatunde, A., Casey, M.C. (2016) "Delivering Water Security for All During Shale Gas Production" A report co-funded by Innovate UK, DECC and NERC and undertaken by the PyTerra Research Consortium Download

Ioannou, P., Casey, M.C., Grüning, A. (2015) "Spike-Timing Neuronal Modelling of Forgetting in Immediate Serial Recall" Proceedings of the International Joint Conference on Neural Networks (IJCNN) 2015. Killarney, Ireland: IEEE Download

Smith, M.I., Casey, M.C. (2012) "Solving the Big Data Problem - Smart Ways to Work Together" Big Data: Turning Big Challenges into Big Opportunities, IET Seminar, 05/12/2012 Download

Casey, M.C., Pavlou, A., Timotheou, A. (2012) "Audio-Visual Localization with Hierarchical Topographic Maps: Modeling the Superior Colliculus" Neurocomputing, vol. 97, pp. 344-356, doi: 10.1016/j.neucom.2012.05.015 Download

Casey, M.C., Yau, C.Y., Barfoot, K.M., Callaway, A.J. (2012) "Data Mining of Portable EEG Brain Wave Signals for Sports Performance Analysis: An Archery Case Study" International Convention on Science, Education and Medicine in Sport (ICSEMIS 2012). Glasgow, 19-24 July 2012 Download

Barfoot, K.M., Casey, M.C., Callaway, A.J. (2012) "Combined EEG and Eye-tracking in Sports Skills Training and Performance Analysis" World Congress of Performance Analysis of Sport IX. Worcester, 25-28 July 2012 Download

Casey, M.C., Hickman, D.L., Pavlou, A., Sadler, J.R.E. (2011) "Small-scale Anomaly Detection in Panoramic Imaging using Neural Models of Low-level Vision" Proceedings of SPIE Defense, Security, and Sensing Conference 2011 on Enhanced and Synthetic Vision, volume 8042B. SPIE, Florida, 25-29 April 2011 Download

Casey, M.C., Pavlou, A., Timotheou, A. (2010) "Mind the (Computational) Gap" Proceedings of the UK Workshop on Computational Intelligence (UKCI 2010), Essex, 8-10 September Download

Bamber, D.C., Page, S.F., Bolsover, M., Hickman, D.L., Smith, M.I., Kimber, P.K. (2010) "Adaptive Image Kernels for Maximising Image Quality" Proceedings of SPIE 7696, Automatic Target Recognition XX; Acquisition, Tracking, Pointing, and Laser Systems Technologies XXIV; and Optical Pattern Recognition XXI, 769608

Pavlou, A., Casey, M.C. (2009) "A Computational Platform for Visual Fear Conditioning" Proceedings of the International Joint Conference on Neural Networks (IJCNN) 2009. Atlanta, Georgia: IEEE Download

Casey, M.C. (2007) "Sensible Machines" Financial Sector Technology, vol. 13(3), pp. 25 Download

Taskaya-Temizel, T., Casey, M.C. (2005) "A Comparative Study of Autoregressive Neural Network Hybrids" Neural Networks, vol. 18(5-6), pp. 781-789, doi: 10.1016/j.neunet.2005.06.003 Download

Bernhardt, M., Smith, M.I., Whitehead, P.G., Hunt, L.N., Hickman, D.L., Dent, C. (2004) "A Statistical Sea-surface Clutter Model in the Long-wave Infrared" Proceedings of SPIE 5431, Targets and Backgrounds X: Characterization and Representation

Clare, P.J.C., Gulley, J.W., Hickman, D.L., Smith, M.I. (1997) "Design and Tuning of FPGA Implementations of Neural Networks" Proceedings of SPIE 3069, Automatic Target Recognition VII


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