• Dr Gavin Brown Lecturer of the Year 2013

    Published: Friday, 04 October 2013

    Senior Lecturer Gavin Brown has been awarded Lecturer of the Year at the 2013 SET (Science, Engineering and Technology) Awards!

    Dr Gavin Brown’s success at the SET awards is an acknowledgment of his support for Computer Science students, and in particular the supervision of recent graduate Laura Howarth-Kirke, who was honoured with awards for the SET Student of the Year 2013 and the Best Computer Science Student for her project entitled, ‘Learning and Recognising Human Gestures using the Microsoft Kinect’.

    In her acceptance speech, Laura thanked everyone in Computer Science at The University of Manchester for her ‘fantastic degree’ and her final year project supervisor Gavin for ‘inspiration throughout my whole degree’. 

    Gavin says: ‘It was a privilege to work with Laura, and I enjoyed every minute of the project we worked on, seeing her bring the various technologies together.  When Laura was awarded the Best Computer Science Student prize, I was extremely happy.  When her name was called out as the overall SET Student of the Year, I was ecstatic, and even more so when I had to get up to receive my award!

    The Science, Engineering & Technology (SET) Student of the Year Awards are Europe’s most important awards for science and technology undergraduates. Supported by industry, and leading scientific and technical institutions, the SET awards showcase educational excellence by publicly recognising the exceptional achievements of both students and universities.

    Dr Gavin Brown is a Senior Lecturer in the School of Computer Science; Director of Undergraduate Recruitment; the School's Competition Coordinator leading the School’s Coding Dojo and representation at the international ACM programming competition; Theme Leader for the MSc Learning from Data component; and a member of the Teaching Innovation Strategy Group.

    His research is in Machine Learning; a field that creates mathematical models which can make predictions about phenomena given some background data, such as predicting whether someone is at risk of heart disease given their medical history, or predicting the price of a car given its characteristics.



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