Ella is a PhD student at the Department of Applied Mathematics and Theoretical Physics (DAMTP) at the University of Cambridge, specializing in High Energy Physics. Her work is centred on the application of machine learning techniques, particularly neural networks, to study Parton Distribution Functions (PDFs). 

Ella studied at Durham University, achieving an integrated Master’s degree in Physics. During her final year here, she focused on developing an algorithm called ‘Cell resampling’ which aimed to improve the efficiency of Monte Carlo event generation by removing negative weights from samples. Ella has also gained research experience during her internship at DESY Zeuthen where she worked with the experimental ATLAS group to produce an algorithm to optimise the bin widths of a migration matrix during the process of unfolding. In October 2024, she has joined DAMTP as a PhD student.

My project:
I have started my PhD within the High Energy Physics group at DAMTP, supervised by Prof. Maria Ubiali. My research focuses on the study of Parton Distribution Functions (PDFs) using neural networks. PDFs are an element of particle physics that cannot yet be calculated from first principles, we therefore must extract them using experimental data. PDFs are essential for many calculations at high-energy proton colliders, it is therefore crucial that we can produce them to a high accuracy and precision, there are many methods of improving this. My research may also focus on how we can extract BSM physics from the PDFs that we calculate using Effective Field Theory.