“It is by logic that we prove, but by intuition that we discover.” — Henri Poincaré.
About
The consistent theme that has run through my entire career: I'm fascinated by using mathematical modelling and computer simulations to predict future behaviour and to model what can't easily be measured. Early on I realised most technical fields are really the same mathematics applied to different problems. That same interest also draws me to applications such as OTC derivatives pricing and algorithmic trading, where prediction has to contend with noisy data, changing incentives, and systems that react to being modelled.
My path runs from building a venture-backed start-up's R&D team from scratch during my early career in Ireland to AI research today. Throughout my career I noticed that the same handful of equations resurface in different disguises.
- The finite-element solvers I used to predict how coronary stents would behave during a ten-year service life are, mathematically, the very PDE methods I'd later apply to price derivatives.
- The coarse-grained molecular dynamics I ran to model fibril aggregation in Huntington's disease (published in PNAS) propagates the Langevin equation — which, as the Ornstein–Uhlenbeck process, is how interest rates are modelled in finance.
- The convex relaxations of non-convex problems I built to predict molecular structure in my PhD thesis rest on the same optimisation and linear algebra that now trains deep networks for next-token prediction.
- Those very relaxations are now an AI-research tool in their own right: they can certify that no adversarial input will fool a trained neural network (Raghunathan et al., NeurIPS 2018).
Currently my focus is Large Language Models. Beyond next-token prediction, chain-of-thought reasoning offers, for the first time, a computational model of human reasoning — something never previously measurable. I'm currently researching recursive debate frameworks for the UK AI Safety Institute — constructing obfuscated arguments to probe scalable oversight — and I'm increasingly drawn to mechanistic interpretability and to building practical AI applications.
I believe large language models are reshaping how every discipline works with data of all kinds — knowledge and expertise are being commodified, and the future belongs to those who wield these tools effectively, and to those making sure we can trust them.