I started my career as a scientist, with a PhD in physics followed by a couple of post-docs in computational biology. I’ve now taken the sideways step from science to science writing—check out my book, Ageless: The new science of getting older without getting old.
I decided to make this slightly unusual leap between physics and biology because I wanted to work on ageing. Ageing is probably the most fascinating problem in contemporary science and, I believe, understanding it is the greatest humanitarian mission of our time. Ageing, and all the diseases it causes, is the single largest cause of human suffering in the modern world and, by treating the underlying ageing process, we could potentially prevent many of these diseases simultaneously. To find out more, read my book!
I worked as a post-doctoral researcher in the Luscombe Lab at the Francis Crick Institute in London, using machine learning techniques to try to make sense of genomic data and medical records. I used neural networks to try to model where proteins and nucleosomes bound to DNA, and random forests and penalised regression models to predict patient outcomes using medical records data.
I also worked as a post-doc at King’s College London, analysing survival and imaging data in C. elegans to dissect how genes in their neurons detect food in the environment, altering their lifespan.
My PhD was in physics, examining magnetism and superconductivity by taking new materials to particle accelerators and firing beams of particles called muons at them. Check out my thesis, or MµCalc, the software I developed for Bayesian dipole field analysis.