Å·ÃÀÐÔ°®Æ¬â€™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 , 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
![Students working with a Virtual Reality headset](/sites/default/files/styles/3x2_900w/public/2020-05/CB_BU_29_10_19__S14_Canon_160_0.jpg?h=c8b66765&itok=Ht_1HIwR)
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, BÂ & Lique, C 2018, '',ÌýJournal of Physical Oceanography.Ìý
Polyakov, IV, Padman, L, Pnyushkov, A, Rember, R & Ivanov, VV 2019, '',ÌýJournal of Physical Oceanography, vol. 49, no. 1, pp. 227-246.Ìý
, Vlasenko, V, Stashchuk, NÂ & Bacon, S 2017, '',ÌýGeophysical Research Letters, vol. 44, no. 24, pp. 12349-12357.Ìý
, Sundfjord, A & Bacon, S 2015, '',ÌýNature Geoscience, vol. 8, no. 3, pp. 191-194.Ìý
Rosier, SH, Gudmundsson, GH 2015, '',ÌýCryosphere, vol. 9, no. 4, pp. 2397–2429.Ìý
, Way, MJ & Barnes, R 2019, '',ÌýThe Astrophysical Journal Letters, vol. 876, no. 2, L22.Ìý
Malarkey, J, Hope, JA, Aspenden, RJ, Parsons, DR, Peakall, J, Peterson, DM, Schindler, RJ, Ye, L, Lichtman, ID, Bass, SJ, Manning, AJ & Thorne, PD 2015, '',ÌýNature Communications, vol. 6, no. 6257.Ìý
Schindler, RJ, Parsons, DR, Ye, L, Hope, JA, Peakall, J, Manning, AJ, Aspden, RJ, Malarkey, J, Simmons, S, Paterson, DM, Lichtman, ID, Thorne, PD & Bass, SJ 2015, '',ÌýGeology.Ìý
, Headleand, CÂ 2015, '',ÌýIEEE Transactions on visualization and computer graphics, vol. 22, no. 1, pp. 419-428.Ìý
, John, NWÂ 2021, '',ÌýIEEE Transactions on visualization and computer graphics, vol. 27, no. 7, pp. 3213 - 3225.Ìý
David, T, Scapin Anizelli, H, Jacobsson, TJÂ & Kettle, J 2020, '',ÌýNano Energy, vol. 78, 105342.Ìý
Abbood, Z, Lavauzelle, J, Lutton, E, Rocchisani, J-M, Louchet, JÂ 2017, '',ÌýSwarm and Evolutionary Computation, vol. 36, pp. 91-105.Ìý
 & Villard, PF 2015, '',ÌýComputerized Medical Imaging and Graphics, vol. 49, pp. 1-15.Ìý
John, N, Phillips, N, Pop, S, Coope, D, Kamaly-Asl, I, de Souza, CÂ 2017, '',ÌýPresence: Teleoperators and Virtual Environments, vol. 25, no. 4, pp. 289-298.Ìý