CV

The Physics of Machine Learning on Oscillator Ising Machines — Equilibrium Propagation, energy-based learning, and neuromorphic hardware. Thesis submission May 2027.

Master of Mathematical and Theoretical Physics, Distinction. BA Physics, First Class — ranked 1st in a cohort of ~200 in second-year examinations.

Introduced GradEP, extending Equilibrium Propagation from shaping energy minima to training energy gradients, and built FlowEqProp, the first EP-trained flow matching generative model (Best Paper, ICONS '26). Developed EP training for Oscillator Ising Machines to state-of-the-art neuromorphic accuracy under realistic hardware constraints; a hardware implementation is underway with Nokia and TU Eindhoven. Uncovered a saddles-to-minima topological crossover governing glassy dynamics on rugged energy landscapes (submitted to Nature Physics).

Led short-horizon risk forecasting: gradient-boosted and interpretable EBM models over 1,500+ engineered features and 20M+ records, improving recall by 50% over prior baselines, plus LLM pipelines for semantic incident categorisation and case-note surfacing.

Evaluated frontier LLM abilities in model compression, mechanistic interpretability, and feature steering for ML R&D tasks.

Co-authored two Physical Review B papers on multi-band superconductivity and impurity effects in FeSe.

Enhanced the FETCH2 radiation modelling codebase; presented benchmarking results at Imperial College.

Supervised undergraduate Statistical Mechanics and Mathematical Methods classes at Cambridge for three years, and a Master's dissertation on saddles-to-minima crossovers in kinetically constrained Ising models.

• Best Paper Award, ICONS '26 — FlowEqProp.
• Ranked 1st of ~200, Oxford Physics second-year examinations.

Languages: Python, Julia, MATLAB, C++, Java, R, FORTRAN.

ML: PyTorch, TensorFlow, XGBoost, LightGBM, InterpretML.

Infrastructure: SLURM/HPC, AWS, Git, Bash.