Working with Syngenta on modern statistical techniques for assessing and predicting herbicide performance (Supervisors: Evangelos Evangelou and Kostas Papachristos). Arron read his BSc in mathematics at King’s College London, and an MSc in statistics at the University of Kent. His master’s dissertation involved analysing the proliferation of an SIR epidemic, having introduced a vaccine to the population. His research involves assessing and predicting herbicide performance using modern statistical and machine learning techniques, and is in collaboration with Syngenta. Arron enjoys playing squash, listening to music, and travelling.

Research project title:
Modern Statistical techniques for assessing and predicting herbicide performance

Supervisor(s):
Evangelos Evangelou, Kostas Papachristos

Project description:
Typically, thousands of potential herbicides will undergo a sequence of screening tests (assay tests) in the lab. Each time ineffective compounds will be discarded and those remaining are assessed against a more complex set of criteria, with the final few undergoing rigorous field trials. Evidently, the data from the early trials will exhibit high uncertainty and subjectivity. In most applications, a herbicide is assessed against a range of criteria. Therefore, a method to combine multiple criteria according to their significance for scoring each herbicide is required. Arran’s research involves creating a model to predict the herbicide’s performance on each test using information such as dosage, plant species, and the chemical’s structure which can be presented as a graph. Modern regression methods such as support vector regression, neural networks, and Gaussian process regression are employed to exploit the relationships between plant species and families of chemicals in order to improve predictive performance.