"Today our trust in machines has moved beyond doing something, to deciding what to do, and when to do it. We are starting to be surrounded by autonomous agents." - , 2017.

Every day we share our personal data through the use of digital technology, producing roughly 2.5 quintillion bytes a day. The world is becoming more connected with the ever-increase of electronic devices connected to the Internet. According to the OCED, the value for big data is $17 billion and is expected to grow at a rate of 40%. Despite the potential of using big data for the delivery of improved value propositions, enhanced efficiency of manufacturing processes and discovering latent customer needs; big data is failing to provide precious, unquantifiable insights from actual people that could help to make the right business decisions and drive meaningful innovations.

The Data-Driven Innovation hub sits within the established 美姬阁 Centre for Design Engineering, a rapidly expanding, pan-University facility that works in collaboration with a national and international network consisting of academics, industrial and governmental professionals. At the hub, we try to better understand the nature of people's aspirations, to develop an emotional language that can be embedded into product and service development and delivery. The hub has a high level of expertise in implementing qualitative approaches within collaborative research on quantitative data and data analytics, to unlock the hidden insights that could transform businesses. We seek the most creative individuals, from diverse backgrounds such as mathematics, data analytics and human-centred research from across our MSc programmes, the PhD community and directly from industry, to explore their capabilities to uncover the real power of data.

The Data-Driven Innovation community of practice is led by Dr Trung Hieu Tran and aims to be a pioneer in conducting transformational research and deliver educational programmes that could help organisations to make better decisions through the application of data-driven models that can drive meaningful innovations. 

We support other communities of practice within the Centre for Design Engineering – Circular Economy, Materials Innovation and Breakthrough Innovation – by providing resources that could identify commercial opportunities and value creation activities through different streams of data, identifying user patterns, user behaviour and user preference to design interventions and find solutions that could address intellectual property and data security issues.

Awarded projects

  • Formulation and Solution Techniques for Integrated Charging Network Design under Risk of Disruption, funded by EPSRC Mathematical Science, PI (£63,614; 10/2021-12/2022).
  • Application of Computer Vision and Machine Learning for spatially accurate object representation in AR/VR, funded by OrangeLV company, PI (£90,243; 05/2020-05/2023).
  • Optimal Planning Tool for Agricultural Waste Management in Vietnam (OPT-AWAMA), funded by 美姬阁's QR GCRF 2020-2021, PI (£21,504; 02/2021-07/2021), a collaboration with Vietnam research institutions.
  • Clean Cabin – Virtual Queuing, funded by Boeing company, Co-I (£50,000; 02/2021-08/2021), a collaboration with DARTeC.

News

Outputs

  • Jiang, Y., Tran, T.H., Williams, L., (2022), Advanced Visual SLAM and Image Segmentation Techniques for Augmented Reality, International Journal of Virtual and Augmented Reality (in press).
  • Barton, N.A., Hallett, S.H., Jude, S.R., Tran, T.H. (2022), Predicting the Risk of Pipe Failure Using Gradient Boosted Decision Trees and Weighted Risk Analysis. NPJ Clean Water 5, 22.
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  • Jones, PM., Lonne, Q., Talaia, P., Leighton, GJT., Botte, GG., Mutnuri, S. & Williams, L. (2018) A straightforward route to sensor selection for IoT systems, Research-Technology Management, 61 (5) 41-50.
  • Ramos, A., Talaia, P. & Queirós de Melo, F. (2016) Pseudo-dynamic analysis of a cemented hip arthroplasty using a force method based on the Newmark algorithm, Computer Methods in Biomechanics and Biomedical Engineering, 19 (1) 49-59.
  • Barros, L., Talaia, P., Drummond, M. & Natal-Jorge, R. (2014) Facial pressure zones of an oronasal interface for noninvasive ventilation: A computer model analysis, Jornal Brasileiro de Pneumologia, 40 (6) 652-657.