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  Discovering similarities between unstructured data


   Department of Mathematics & Statistics

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  Dr P A Knight, Dr Francesca Arrigo  Applications accepted all year round  Funded PhD Project (UK Students Only)

About the Project

A key area in data analysis is to uncover patterns in seemingly unstructured data. In this PhD project we look at mathematical tools for uncovering apparent similarities in large networks using the tools of graph alignment. Networks (or graphs) are a powerful tool to represent systems of interacting or related data. They can be used to understand a broad range of real-world applications: from the Internet of Things to social systems such as online networks and twitter. We use graph alignment to assess the similarity between different networks in order to identify common actors. Alternatively, differences can indicate anomalous behaviour or alternative pathways.

The project is a partnership between academia and industry and attracts generous funding and an enhanced stipend. As part of the project, the successful applicant will spend at least 3 months working at one of Dstl’s sites in the south of England. There will be opportunities to present the results of the research both nationally and internationally.

The focus of this PhD would be on adapting existing graph alignment methods and developing new approaches so that they can handle large-scale (~1 million nodes), directed (i.e. nonsymmetric) networks and represents a potential breakthrough for many real-world applications. Furthermore, it would benefit from an expertise built upon a successful previous collaboration between Dstl and the University of Strathclyde.

In the first instance, applicants should contact Dr Philip Knight ([Email Address Removed]) with a CV and brief cover letter.

Keywords

Mathematics. Machine Learning. Network theory. Graph theory. Data analysis.


Mathematics (25)

Funding Notes

Funding is through EPSRC. An ICASE award.

Where will I study?

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