My research interests focus on learning algorithms to detect rare or abnormal events from industrial data in situations where faulty data is sparse or non-existent. I am keen to apply such methods across several industry sectors as there are clear benefits to industry by improving maintenance methods through modern data analysis. Currently these methods are being applied in the manufacturing, computing and transport sectors of industry.
I am a Senior Research Fellow with over 10 years’ experience in the fields of data analysis and machine learning as well as a strong track record in industry-academia collaboration.
I am an effective collaborator, having won, or been a part of externally funded projects with staff from all 5 of the University's faculties.
I have been PI/Co-I on multiple Innovate UK and DASA bids worth nearly £6million in total (worth over £2.1 million for the University). These grants have been in collaboration with academic partners (University of Southampton and University of Nottingham) and industrial partners within the maritime, railway, aerospace, high performance computing and manufacturing sectors.
I was entered into REF 2014 (UoA 19) as an Early Career Researcher and I also undertook a large part of the research that formed one of the impact case studies. This case study was featured on the Universities Alliance website.
Through my role as a Researchers’ Network Champion and as a member of the HR Concordat Implementation Group, I promote researcher support for all staff up to the level of reader.
I received my PhD from Portsmouth in 2011 and the MMath degree from the University of Reading in 2005. I previously worked at Clearswift Ltd where I used machine learning techniques to automatically identify certain types of images in email attachments.