About Me

I am a final-year PhD student in the Centre for Doctoral Training in Modelling of Heterogeneous Systems at the University of Warwick, based in the Department of Physics under the supervision of Prof. David Quigley. My training included a one-year CDT programme covering materials simulation and predictive modelling techniques for a Postgraduate Diploma, and I specialise in enhanced Monte Carlo sampling and predictive machine learning for complex physical systems.

My current research has two avenues:

1. Wang-Landau sampling and alloy thermodynamics
   I develop scalable variants of the Wang-Landau algorithm for calculating densities of states in alloy systems. This includes analysing the efficiency of different parallelisation strategies, improving load balancing across energy windows, and applying flat-histogram sampling to high-entropy alloy thermodynamics.

1. Machine learning for statistical physics
   I develop supervised convolutional neural-network models for committor prediction in the 2D Ising model. This work provides a practical alternative to expensive committor calculations, constructs Markov state models along learned reaction coordinates, and benchmarks nucleation rates against brute-force and geometric-cluster baselines.

My broader interests include algorithms for statistical mechanics, high-performance scientific computing, and interpretable machine learning for physical systems.