Professor Elaine Martin OBE
Prof of Industrial Statistics


B.Sc. (Civil Engineering) Glasgow University 1984
B.Sc. Statistics (First Class) Glasgow University 1989
Ph.D. (Statistics) University of Glasgow 1993

Previous Positions

Dean of Research Faculty of Science, Agriculture and Engineering 2003 - 2006


Fellow of the Royal Statistical Society 1989
Fellow of the Institute of Chemical Engineering 2004
Chartered Engineer 2004

Honours and Awards

Awarded OBE for Services to Science - 2010

Appointed Fellow of the Royal Academy of Engineering - 2012

Attendance at Buckingham Palace Reception in recognition of contribution to UK Science and Engineering. 2006

IChemE Outstanding Achievement in Chemical and Process Engineering award, 2012

IChemE Chemical Engineering Project of the Year Award, 2012

As an industrial statistician and engineer, I have worked at the interface of disciplines as this is where research challenges typically materialise. My strength of working with other disciplines has ensured that process/product understanding is integrated into the objectives of the research, thereby ensuring that the outcomes can be used in practice. By adopting this philosophy, my research has made significant contributions to R&D in the UK chemicals, specialty chemicals, food &drink, home and personal care (HPC) and bio/pharmaceutical sectors. More specifically my expertise lies in the area of transforming data available from processes and products into information and ultimately knowledge that can be used to characterise the system under investigation with the goal of aiding decision making with regard to the objectives of the study. I have thus drawn on my statistical background to apply both existing tools to new problems as well as developing, along with my PhD students and research associates, novel algorithms and solutions. In terms of the application of existing tools, I have drawn from techniques in multivariate statistics, design of experiments and Bayesian analysis. A particular challenge has been that of modifying/applying said tools to small data sets such as those observed in development. Areas where the research team has developed new concepts to address challenges in multivariate statistical process control and feature extraction from complex data-bases have included linear and non-linear statistical modeling and non-parametric statistics.