INI Newton Gateway to Mathematics in partnership with DataSıg
Unlocking Data Streams
Sequential streams of information are pervasive; things happen and are recorded. These streams can be regular with all channels updating at once like sound. Alternatively, channels can update one at a time and maybe not at all, as things happen. An example of this is an electronic health record – which might capture hospital admission, a blood test, or perhaps a continuing ECG measurement.
Managing this heterogeneous stream of data is a challenge. Often there is important information in the order of events that links the channel behaviour together. In this case smoothing the data out channel by channel, like binning data, is damaging. A powerful unifying approach is to regard the data to be the input, and let it control a dynamical system. Different behaviour can be distinguished via the different responses of the system. One can modify the system so that it is not affected by the aspects of the stream that are of little interest.
With five years funding from UKRI, the DataSıg Programme looks to address this key challenge of data science – to better understand multimodal data streams. It seeks to do this by developing mathematical descriptions of these streams, using ‘rough path’ (RP) theory; RP theory allows for the direct capture of the order in which events happen and in many cases can better model the effects of these data streams via a top down signature description of the stream that summarises the data effectively without exposing the individual data points to direct analysis.
This one day workshop will highlight a number of exciting research activities and outline some of the successful collaborations within the DataSıg Programme. Collaborations have included:
- Security and defence - action detection using signatures and computer vision to classify physical human actions from real time data
- Human computer interfaces – such as translation handwriting on mobile devices
- Astronomy – rough path models to aid the development of measurement instruments and processing techniques for astronomy telescopes
- Mental health – development of a tool which looks in an automated way at self-reported data (such as speech and mood information). Enables positioning of individuals on spectrums and potentially better feedback for clinicians/clients.
- Human disease – identifying the evolution of cancer cell lines and an early warning system for sepsis detection
Aims and objectives
Our experience of the world is multimodal and understanding multimodal data streams (complex sequences of data from different sources), is a key challenge of rough path theory and more generally for data science. This workshop therefore aims to increase awareness of the research and applications being undertaken by the DataSıg team. The Programme seeks to further develop signature-based mathematical tools for dealing with complex streamed data, and connect with partners who have the capability and the challenges to benefit from and achieve significant outcomes with the methodology. The meeting should be of interest to end-users from multiple settings including, industry, business, public sector and clinicians who are interested in collaborating and who could:
- Benefit from the development of useful open sources software tools that could be utilized in various machine learning environments
- Have needs around the interaction with complex, real world evolving data, to be able to easily tackle questions where there is a variety of different data to consume.
Talks at this workshop will highlight state-of-the-art research and success stories. Presentations will feature various examples of rough paths in action, understanding clouds (collections) of paths and their applications, log signals and controlled differential equations, as well as applications and challenges from end-users perspectives. The day will be of relevance to multiple application areas and sectors including engineering, agriculture, security, communications, human health and the social sciences.