I obtained my Master's degree in Theoretical Physics with first-class honours from the University of York in 2012, where I remained to complete my PhD under the supervision of Prof. Rex Godby. My thesis, Electrons in Model Nanostructures, was awarded the K. M. Stott Prize for excellence in scientific research. From 2016 to 2019, I held a postdoctoral research position at the Max Planck Institute of Microstructure Physics in Halle, Germany, working with Prof. Eberhard Gross, followed by a position at Durham University with Dr Nikitas Gidopoulos from 2019 to 2021.
Since 2021, I have been a Lecturer at the University of York, where I teach theoretical and computational physics. I hold several academic roles, including serving on the Standing Academic Misconduct Panel for the Faculty of Sciences and as the Machine Learning Academic Theme Lead for the N8 Centre of Excellence in Computationally Intensive Research. I am also an active member of the Institute of Physics, the EPSRC Peer Review College, and the European Theoretical Spectroscopy Facility (ETSF).
My research focus is many-body quantum mechanics, with an emphasis on modelling how electrons are excited in materials. I am a lead author of the iDEA code, a Python library for exploring and understanding many-body quantum systems. With iDEA, I identify limitations in widely used models based on the most popular approaches to quantum mechanics, such as density functional theory and many-body perturbation theory, and propose strategies to improve their accuracy and predictive power.
Kohn and Sham's approach to density functional theory is the most popular method in materials science; however, it is notoriously unreliable for calculating electron excitation properties.
Modelling the response of electrons to an applied electric field remains a challenge; yet determining the flow of charge through a material is crucial for the design of molecular junctions.
Many-body perturbation theory is commonly used to calculate the spectral function; however, increasing the accuracy of this approach is challenging owing to the computational cost.