Matthew Pugh wins National Awards 2017 with his machine learning algorithm project
On Wednesday 6th September, at the Future Industry Leaders Awards 2017, Matthew Pugh who undertook his Year in Industry (YINI) placement with EPSRC won the National Award 2017 for his project, which was to design a machine learning algorithm to classify free text records.
Matthew’s project was to find a way to screen some 35,600 records, and to identify and then classify those which could be considered problematic. The task was by no means simple because the records consisted of free text entries created independently by thousands of researchers and they varied substantially in both length and detail. Mathew developed a machine learning algorithm to assess the quality of the outputs with 98% accuracy. His algorithm enabled the division of reports into three categories two of which indicated acceptable or not acceptable quality while the third indicated a doubt on allocation which would require human checking.
Ben Ryan, Senior Manager (Research outcomes), EPSRC says: “Matthew rose impressively to the challenge we had set him and his work saved 100’s of hours of manually sorting through records. I am confident that the targeted communications thus facilitated will have contributed to a significant improvement in the quality of data about the outcomes arising from the research we fund. This project has been just one among a number of tasks undertaken by Matthew during his year with EPSRC and his contribution to our work overall has been considerable.”
The event was sponsored by The IET, Jaguar Land Rover and Rolls-Royce, as well as being supported by companies from industry, including; EPSRC, PVI, Advanced Laser Technology Ltd (ALT), Chemring Energetics UK Ltd., Carbon Architecture, Procter + Gamble, Springboard Pro, Network Rail, Amey, BAE Systems, Rittal CSM, LLW Repository Ltd. These as well as other companies are helping EDT to make a lasting impact in the lives of young people across the UK, by delivering over 40,000 experiences each year.
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