Bangor’s research in this theme has built on its longstanding strengths in machine learning, data classification and pattern recognition, and has expanded its impacts into Analytics and Virtual Reality (VR). Current research focuses on human-computer interaction, data and information visualization, visual analytics, computer vision, pattern recognition, machine learning, (X)Realities, artificial life, evolutionary computing, learning analytics and medical graphics. This has recently been strengthened through the University’s partnership in the EPSRC Centre for Doctoral Training in Artificial Intelligence, Machine Learning and Advanced Computing, in partnership with Cardiff, Swansea and Aberystwyth Universities. The University is making a strategic investment to increase its research strength in artificial intelligence and machine learning in order to underpin research across environmental sciences and engineering with expertise in these key approaches to data analytics and modelling.
Research Highlights
New Principal Component Analysis feature extraction techniques supported by the Leverhulme Trust, which have been used by the England Cricket Board to identify predictors of elite performance. Development of the first open-standards, web-based Immersive VR Analytics framework to standardise the way that data-driven VR experiences are created and deployed on the web. Novel use of procedural animation agents and computer graphics enabling commercialisation of ‘Ocean Rift’, one of the leading VR experiences for a plethora of VR platforms.
Spotlight on Publications
Our research staff produce a range of publications within this theme.
Barton, B, Lenn, Y-D & Lique, C 2018, 'Observed atlantification of the Barents Sea causes the Polar Front to limit the expansion of winter sea ice', Journal of Physical Oceanography. https://doi.org/10.1175/JPO-D-18-0003.1
Polyakov, IV, Padman, L, Lenn, Y-D, Pnyushkov, A, Rember, R & Ivanov, VV 2019, 'Eastern Arctic Ocean diapycnal heat fluxes through large double-diffusive steps', Journal of Physical Oceanography, vol. 49, no. 1, pp. 227-246. https://doi.org/10.1175/JPO-D-18-0080.1
Rippeth, T, Vlasenko, V, Stashchuk, N, Scannell, B, Green, M, Lincoln, B & Bacon, S 2017, 'Tidal conversion and mixing poleward of the critical latitude (an Arctic case study)', Geophysical Research Letters, vol. 44, no. 24, pp. 12349-12357. https://doi.org/10.1002/2017GL075310
Rippeth, TP, Lincoln, BJ, Lenn, YD, Green, JA, Sundfjord, A & Bacon, S 2015, 'Tide-mediated warming of Arctic halocline by Atlantic heat fluxes over rough topography', Nature Geoscience, vol. 8, no. 3, pp. 191-194. https://doi.org/10.1038/NGEO2350
Rosier, SH, Gudmundsson, GH & Green, JA 2015, 'Temporal variations in the flow of a large Antarctic ice stream controlled by tidally induced changes in the subglacial water system', Cryosphere, vol. 9, no. 4, pp. 2397–2429. https://doi.org/10.5194/tc-9-1649-2015
Green, JAM, Way, MJ & Barnes, R 2019, 'Consequences of tidal dissipation in a putative venusian ocean', The Astrophysical Journal Letters, vol. 876, no. 2, L22. https://doi.org/10.3847/2041-8213/ab133b
Malarkey, J, Baas, JH, Hope, JA, Aspenden, RJ, Parsons, DR, Peakall, J, Peterson, DM, Schindler, RJ, Ye, L, Lichtman, ID, Bass, SJ, Davies, AG, Manning, AJ & Thorne, PD 2015, 'The pervasive role of biological cohesion in bedform development', Nature Communications, vol. 6, no. 6257. https://doi.org/10.1038/ncomms7257
Schindler, RJ, Parsons, DR, Ye, L, Hope, JA, Baas, JH, Peakall, J, Manning, AJ, Aspden, RJ, Malarkey, J, Simmons, S, Paterson, DM, Lichtman, ID, Davies, AG, Thorne, PD & Bass, SJ 2015, 'Sticky stuff: Redefining bedform prediction in modern and ancient environments', Geology. https://doi.org/10.1130/G36262.1
Roberts, JC, Headleand, C & Ritsos, PD 2015, 'Sketching Designs Using the Five Design-Sheet Methodology', IEEE Transactions on visualization and computer graphics, vol. 22, no. 1, pp. 419-428. https://doi.org/10.1109/TVCG.2015.2467271
Butcher, P, John, NW & Ritsos, PD 2021, 'VRIA: A Web-based Framework for Creating Immersive Analytics Experiences', IEEE Transactions on visualization and computer graphics, vol. 27, no. 7, pp. 3213 - 3225. https://doi.org/10.1109/TVCG.2020.2965109
David, T, Scapin Anizelli, H, Jacobsson, TJ, Gray, C, Teahan, W & Kettle, J 2020, 'Enhancing the stability of Organic Photovoltaics through Machine Learning', Nano Energy, vol. 78, 105342. https://doi.org/10.1016/j.nanoen.2020.105342
Abbood, Z, Lavauzelle, J, Lutton, E, Rocchisani, J-M, Louchet, J & Vidal, F 2017, 'Voxelisation in the 3-D Fly Algorithm for PET', Swarm and Evolutionary Computation, vol. 36, pp. 91-105. https://doi.org/10.1016/j.swevo.2017.04.001
Vidal, FP & Villard, PF 2015, 'Development and validation of real-time simulation of X-ray imaging with respiratory motion', Computerized Medical Imaging and Graphics, vol. 49, pp. 1-15. https://doi.org/10.1016/j.compmedimag.2015.12.002
John, N, Phillips, N, Ap-Cenydd, L, Pop, S, Coope, D, Kamaly-Asl, I, de Souza, C & Watt, S 2017, 'The Use of Stereoscopy in a Neurosurgery Training Virtual Environment', Presence: Teleoperators and Virtual Environments, vol. 25, no. 4, pp. 289-298. https://doi.org/10.1162/PRES_a_00270