About

The Purpose of this Blog

This blog is a place for me to share fascinating insights on past and present breakthroughs in applied mathematics and probability theory. It is also a place for me to share the projects that I create; designed to illustrate the value (and I hope, the joy) of those breakthroughs and insights.

If a person comes away from this blog thinking, “wow, that is interesting, I never thought of it that way”, then it will have achieved what I dreamed for it to do.

A Brief Rundown of My Technical Achievements

In 2011, I received an MSci in Astronomy and Physics from UCL. My Masters thesis was on “Black Hole Thermodynamics and the Information Loss Paradox”, and my advisor was Prof. Ian Ford.

I then went on to obtain a PhD in the Physics of Astronomical Detectors from the then Quantum Sensors Group at the Cavendish Laboratory, University of Cambridge. My PhD advisor was Prof. Stafford Withington, and my thesis was titled “Thermal Transport and Noise in Micro-Engineered Support Structures for Detector Applications”: an investigation in novel methods for the accurate simulation of heat transport, and for the utilisation of phase-coherent thermal waves to create low-dimensional “heat interferometers”. The application of this form of heat interferometry was proven empirically (for the first time) to reduce noise in superconducting detectors.

I graduated from Cambridge in 2016, and went on to co-found an augmented reality startup, called Sention, with my good friend Alexander Bridi. Whilst at Sention, I researched and developed novel solutions to a broad range of computer vision problems: from pixel-accurate background and foreground removal in natural scenes, to projection-invariant feature discovery, to blind real-time lens distortion correction (to state just a few). All research and algorithms from my time at Sention were proprietary.

In 2023, I joined Converge, where I currently work as an Algorithms Engineer. I use data from embedded concrete sensors to build machine-learning tools that predict in-situ curing times across seasons, far in advance of the concrete pour. These forecasts help contractors plan further ahead and increase their use of slower-curing, lower-carbon concrete: thereby minimising the carbon footprint of their projects. I also research and develop novel sensing devices for the next generation of material-property detection. My work at Converge has led me to co-author several patents spanning new approaches to sensing materials; empirical and physics-based methods for extracting information about their properties; and explainable, interpretable machine-learning techniques for analysing the resulting data.