DATAS

Understanding multimodal data streams - complex sequences of data from different sources - is a key challenge of data science. By developing mathematical descriptions of these streams, using ‘rough path' theory, it's possible to gain insights into the data that can be used to generate meaningful decisions and actions... Applications of these models range from recognising Chinese handwriting on a smartphone, to classifying physical human actions and assisting mental health diagnosis... When streamed data arrives, it rarely comes all at once, or from a single scalar source, but in multiple modes. Multimodal data streams are found in a huge range of situations and on all scales, and successfully summarising these streams is key to understanding them, and many facets of the world around us. The order of different signals in different modes in these streams provides key information. For example, the order in which glucose levels and insulin levels rise and fall in someone's blood.

Primary location: Oxford United Kingdom