Dr Catherine Higham
- Research Associate (School of Computing Science)
- Honorary Research Fellow (School of Mathematics & Statistics Administration)
My current position as a senior member of Prof. Rod Murray-Smith’s Inference, Dynamics and Interaction group involves applying state-of-the-art machine learning and statistical methods to inverse problems in image processing arising from recent advances in Quantum Optics. The research associate position is supported by the EPSRC UK Quantum Technology Programme under grant P/M01326X/1.
My previous position was Research Associate on the FP7 funded TiMet project 2012-2015, which brought together leading centres for the emerging discipline of systems biology. My role focused on developing, testing and analysing new methods for parameter estimation and model selection using state of the art Markov Chain Monte Carlo sampling within a non-parametric Bayesian statistical framework. Overall, this work is motivated by the challenge of handling novel experimental data and developing quantitative answers to questions raised by experimentalist colleagues.
After a graduating with a mathematics degree from Oxford, I worked for Novaction, a brand/marketing consultancy based in Paris, France, who sponsored me through an business MBA at City University, London. After moving to Scotland, I worked as a strategic analyst at Scottish Hydro-Electric.
Following a career break, I independently prepared and submitted a research proposal to the Daphne Jackson Trust, and obtained a two-year Fellowship 2006-2008. This was followed by a Lord Kelvin/Adam Smith PhD scholarship from the University of Glasgow, 2008-2012.
My PhD work took place in Professor Darren Monckton’s lab, which has accumulated one of the most comprehensive datasets concerned with unstable human DNA mutations. The remit for my PhD was to add value to this unique data resource by developing, calibrating and comparing new mathematical models describing the biological processes underlying the DNA dynamics of diseased individuals.
My research interests are concerned with the development of machine learning algorithms that harness recent technological advances for data sensing and processing for scientific understanding and industrial/commercial application.
I have built up a range of skills in machine learning, deep learning, statistical inference, high performance computing mathematical modelling, and large-scale scientific computation. I have also gained a wide range of experience in interdisciplinary research alongside experimental scientists.