EPSRC logo

Details of Grant 

EPSRC Reference: EP/S00159X/1
Title: Scalable and Exact Data Science for Security and Location-based Data
Principal Investigator: Nemeth, Dr CJ
Other Investigators:
Researcher Co-Investigators:
Project Partners:
Heilbronn Institute for Mathematical Res Prowler.io The Alan Turing Institute
Department: Mathematics and Statistics
Organisation: Lancaster University
Scheme: EPSRC Fellowship - NHFP
Starts: 01 June 2018 Ends: 31 May 2021 Value (£): 523,575
EPSRC Research Topic Classifications:
Information & Knowledge Mgmt Networks & Distributed Systems
EPSRC Industrial Sector Classifications:
Information Technologies
Related Grants:
Panel History:
Panel DatePanel NameOutcome
08 May 2018 EPSRC UKRI CL Innovation Fellowship Interview Panel 4 - 8 and 9 May 2018 Announced
Summary on Grant Application Form
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.

Key Findings
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
Potential use in non-academic contexts
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
Impacts
Description This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
Summary
Date Materialised
Sectors submitted by the Researcher
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
Project URL:  
Further Information:  
Organisation Website: http://www.lancs.ac.uk