Ben graduated with his MMath in Mathematics from the University of Reading in July 2024, after completing his thesis titled “General Second-Order L^infty Variational Problems” which investigated what happens in the calculus of variations when you try to minimise the supremum instead of an integral. During this research, Ben was introduced to how numerical analysis can be used to provide insights into complex mathematical phenomena, particularly in PDE theory, where exact solutions can be elusive but applications are vast. Ben is also excited to learn more about machine learning, which has interested him since A Level.

Outside of mathematics, Ben runs a photography instagram account, and enjoys cooking and gaming.

Project title:
Statistical Surrogate Modelling for Rapid Uncertainty Quantification in Proton Beam Therapy

Supervisor(s):
Tristan Pryer, Christian Rohrbeck

Project description:
Proton beam therapy is a precise form of cancer treatment that targets tumours while minimising damage to surrounding healthy tissue. A key challenge is that planning these treatments relies on performing complex and time-consuming simulations, which can take days to run. This makes it difficult for clinicians to quickly adapt treatment plans in response to changes in a patient’s anatomy during their course of therapy, such as a shrinking tumour. My research aims to address this problem by developing fast statistical “surrogate” models that can accurately mimic the results of these slow models in much shorter time frames, using Gaussian processes. The goal is to create a tool that enables clinicians to rapidly perform uncertainty quantification, to see how uncertainties in the patient setup could affect the treatment outcome.