Personal Statement
I am a NERC Independent Research Fellow in the Department of Earth, Ocean and Ecological Sciences at the University of Liverpool. My research focuses on the numerical modelling of complex magnetic materials and their capacity to record magnetic fields over geological timescales. I use advanced micromagnetic simulations, finite element models, and emerging AI-based approaches to study how non-uniform and spatially heterogeneous magnetic structures form and evolve in natural materials such as rocks and meteorites.

My work integrates computational methods from materials science with questions in geophysics, aiming to understand how nanoscale magnetic configurations influence the stability and fidelity of paleomagnetic records. This involves developing and applying efficient simulation tools that connect magnetic behaviour at the level of individual grains to larger-scale recording processes relevant to Earth and planetary magnetism.

Before joining Liverpool in 2022, I held research positions at Scripps Institution of Oceanography and the University of Edinburgh, and worked as a software engineer at EMBL Hamburg. I originally trained in computer science and high-performance computing, and continue to develop open-source models and computational methods that link magnetism, materials physics, and geological applications. At Liverpool, I lead the VIRGIL project and contribute to teaching programming and applied mathematics within Earth Sciences.

Research Overview
My research focuses on the magnetic behaviour of complex materials, using advanced numerical modelling, micromagnetic simulation, and artificial intelligence. I develop and apply high performance finite element and machine learning methods to study how non uniform magnetic domain structures in minerals, such as vortex and multi domain states, arise and how they influence the stability of magnetic remanence. A recent example is FORCINN, a neural network based approach for interpreting first order reversal curve (FORC) data, which links measured magnetic responses to underlying grain and domain state distributions in geologically significant minerals. Together, these methods are used to understand how micro and nanoscale magnetic configurations in rocks and other functional magnetic materials control their ability to record and retain magnetic signals.

Selected highlights of my research include demonstrating that non uniform single vortex domain structures in geologically significant minerals can provide stable magnetic remanence over very long timescales, developing FORCINN, a neural network based approach for interpreting first order reversal curve (FORC) data that links measured magnetic responses to underlying grain and domain state distributions, and using large scale micromagnetic and finite element simulations to connect nanoscale domain configurations with bulk magnetic measurements and material microstructure.