About_

I am currently exploring using deep learning for data synthesis, and considering the trade-off between data utility and privacy. I am a member of the Centre for Digital Trust and Society at UoM.

More generally, I perform academic research and use data analysis, visualisation, statistical and machine learning methods to solve problems and uncover hidden patterns and insights in data and present them in an understandable way.

Recent papers:
→ comparing utility and disclosure risk of synthetic data with samples of Census microdata
→ comparing GANs for synthesising Census microdata
→ a new semantic and syntactic similarity measure to determine the similarity between tweets, using word embeddings.

Skills_

• academic research (problem solving and exploration, writing research papers and reports, presentation)
• collection, processing, analysis and presentation of data (e.g. pre-processing, linkage, APIs, ethics, visualisation)
• creating code/algorithms using machine learning and statistical methods

I use Python and R most frequently. Big and small data. SQL and noSQL databases. APIs. Windows and Linux. I have mathematical and programming skills (PHP, Java, C++, etc.).

Qualifications_

My PhD (with the cfpm at MMU) explored how methods such as machine learning and visualisation could be used on complex social science data, and used a large, interlinked social database as a case study.
I have:

• PhD entitled 'Machine Learning for Understanding Complex, Interlinked Social Data'
• MSc (Distinction) in Computing from MMU
• BSc (Hons) in Mathematics from the University of Manchester