The UK funding bodies have published the results of the UK’s most recent national research assessment exercise, the Research Excellence Framework (REF) 2021. At the University of Oxford, the Mathematical Institute and the Department of Statistics made a joint submission under the heading of Mathematical Sciences.
One important element of REF is impact case studies, a set of examples of ways in which research has had impact outside of academia, in industry, public policy, public health, and public understanding.
Among the highlights of the research impact case studies was the use of rough path theory to improve the effectiveness of machine learning in Chinese handwritten character recognition for mobile phones.
Emanating from rough path theory, mathematical signatures, developed at the University of Oxford, have been combined with machine learning to enable lightweight, fast, and accurate recognition of complex and unpredictable data streams from different sources.
The methodology has been a key contributor to prize winning practical applications ranging from recognition of finger-drawn Chinese characters on mobile devices to analysing health data. In a paper published in the Annals of Mathematics (2010), Professors Terry Lyons and Ben Hambly showed how ‘mathematical signatures’ from ‘rough path theory’ could be faithfully used to capture the key features of an evolving situation, capturing patterns and allowing accurate prediction and analysis. This lead Lyons and his team to develop signatures into an effective tool to describe the interactions between complex data streams in data science.