I am a polymath scientist and engineer with a PhD in Mathematical Biology and Biophysical Chemistry from the University of Warwick. My work spans theoretical biophysics, computational chemistry (cheminformatics and quantum chemistry), bioinformatics, machine learning, and systems design.

My scientific expertise lies in molecular modelling of protein dynamics, particularly the MMP family (including MMP14, a membrane-anchored metalloprotease). I develop computational methods for membrane protein systems, free-energy landscapes, and shape-derived pharmacophore modelling of drug binding sites.

I have also contributed to drug discovery pipelines, integrating computational chemistry with statistical analysis of real-world healthcare data from UK General Practice and hospital records.

In parallel, I pursue research in time-series machine learning, with applications to financial markets and predictive analytics.

Currently, I serve as an independent science consultant collaborating on a drug discovery program at Arizona State University.

Before returning to science full-time, I worked in software engineering during the early smartphone era. I believe system design outweighs syntax—great architecture is timeless, while code is transient.

Outside of research, I am an independent market trader and an international Taekwondo instructor with over 32 years of experience.

Science, for me, is a vocation, pursued for understanding, not financial gain.