I studied Theoretical Physics at the University of York, graduating in 2012 with first-class honours. I remained at York to complete my PhD under the supervision of Prof Rex Godby. For my thesis, Electrons in Model Nanostructures, I was awarded the K. M. Stott Prize for excellence in scientific research.
I subsequently held postdoctoral research positions at the Max Planck Institute of Microstructure Physics in Halle, Germany, with Prof Eberhard Gross, and Durham University with Dr Nikitas Gidopoulos. Our research advanced the fields of condensed matter physics and quantum chemistry.
I am currently a Lecturer at the University of York, where I teach theoretical and computational physics. I have several academic roles, including serving on the Standing Academic Misconduct Panel for the Faculty of Sciences. I am also an active member of the Institute of Physics, the Engineering and Physical Sciences Research Council (EPSRC) Peer Review College, and the European Theoretical Spectroscopy Facility (ETSF).
My research focus is many-body quantum mechanics, with a particular 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. By using 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.