Scalable and Exact Data Science for Security and Location-based Data

Incredible technological advances in data collection and storage have created a world in which we are constantly generating data. From supermarket loyalty cards and social media posts to healthcare records and credit card transactions, a digital footprint exists for every aspect of our lives. The ability of data science to analyse and act upon these complex and varied data sources has the potential to improve and revolutionise our lives in a myriad of ways, for example, through the development of driverless cars and personalised medicine.

The great challenge of data science lies in the trade-off between the speed and accuracy with which large volumes of data can be analysed and acted upon within complex data environments. Extracting deeper knowledge from data requires increasingly sophisticated mathematical models. However, applying such models introduces significant computational constraints, forcing data scientists to rely upon simpler models or approximate inference tools.

In collaboration with strategic partners, this project will bring together industry experts to investigate new approaches to data science driven by fundamental challenges in modelling and analysing large-scale spatial and security data. The data and issues within this domain are highly-significant to modern society as they cover, for example, issues pertaining to fraud detection and computer hacking, as well as understanding and predicting human behaviour within a Smart City environment.

Novel mathematical advances in computational statistics and machine learning will be developed to produce scalable techniques for applying sophisticated mathematical models to large-scale heterogeneous and structured data sources. A key component of this project is reproducibility through the creation of open-source software. These tools will allow data scientists to implement research outcomes to extract key features from complex data and make decisions with high accuracy under uncertainty.

This research agenda is designed to address the significant topical challenges of modern data science which impede its applicability within complex data environments. Through close engagement with industrial stakeholders, this research will produce a transformative approach to analysing large-scale heterogeneous data in the areas of spatio-temporal modelling and security & defence applications.

This project is supported by an impressive array of committed partners: Prowler.io, The Heilbronn Institute of Mathematical Research (HIMR) and The Alan Turing Institute (ATI), who provide significant expertise in the areas of security and spatio-temporal modelling. Through an integrative research programme with the project partners, key research outcomes will be tested and deployed on the data and systems owned by these partners, providing real-world verification of the applicability of research outputs.

Through the co-design and implementation of research objectives with project partners, the scalable data science tools created under this fellowship will contribute to the knowledge economy of the UK, by enabling researchers and practitioners to employ complex mathematical models to previously prohibitively high-dimensional data sources. Key engagements with HIMR will support the application of this research to address imperative national security challenges.

Open-source software will be developed stemming from research outcomes. This will support the far-reaching impact of this work beyond the academic community, providing tools for end-users to freely implement on a wide variety data sources beyond the security and spatio-temporal domains. This will become part of the core toolbox for both public and private sector organisation seeking to fit complex models to large data.