I read Physics at Oxford and after graduating worked for some years as a Patent Examiner at the European Patent Offices in the Netherlands and Germany. On returning to the UK, I joined the Institute of Food Research to work on infrared sensor design, and my PhD research focused on chemometric analysis of the large datasets produced by infrared and Raman spectrscopy.

Since then, my interests have included applications of computational statistics, machine- and deep-learning to a wide range of data types, from electrophoresis, chemical imaging, and electromyography to the metabolomics platform technologies (NMR and LC-MS). Key areas of application have been natural product integrity issues, such as detecting adulteration of edible oils, processed fruits, meat products, coffee and spices. I lead the UEA's Centre of Expertise in Food Authenticity, part of the UK's Food Authenticity Network, a virtual reference laboratory for the measurement of food integrity. 

Much of my research is aligned to industrial application, and I collaborate closely with a range of partners in the forensic, food, pharma and instrumentation sectors. In recent years I have concentrated on the analysis of data produced by high-resolution NMR, including by the emerging low-field 'benchtop' technology. 

Concurrent with my position of Professor of A.I. in Chemistry at UEA, I am a Scientific Director at Mestrelab Research S.L, a scientific software house and division of the Bruker Corporation. My research interests here are in small molecule cheminformatics: using convolutional neural networks and graph theory to explore associations between structure and physical properties, and in applications of machine learning to automation and high-throughput analysis.