Measurement provides data that is used as the basis for drawing inferences, informing decisions and controlling systems. These activities depend on the reliability and traceability of data and on the inter-operability of measuring systems. These characteristics are established through theoretical analysis, modelling and data analytics.
In the Data Science group we are building the capability to ensure that information derived from data is as reliable as possible and the quality of that information is quantified. We are developing techniques for optimisation and sampling to enable reliable inference under uncertainty, particularly in the context of large-scale problems. We are extending our work in measurement uncertainty evaluation to cover new methods of data analytics, such as machine-learning and classification tasks. We are building on our work in software testing to ensure that the algorithms and software used to process data can be demonstrated as trustworthy. Our capability in these areas will be applied across various sectors to provide case studies, demonstrators and facilitate external uptake.
Our work is focused on the following themes:
Our research and measurement solutions support innovation and product development. We work with companies to deliver business advantage and commercial success.
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