I'm doing an MPhil in Computational Biology at the University of Cambridge (Fitzwilliam College). Before that: a First in Mathematics from the University of Bristol, and a stint as an investment banking analyst at J.P. Morgan Cazenove.
I'm interested in machine learning for perturbation biology, drug target discovery, and early-stage startups — mostly where those three overlap.
See what I'm doing now, or get in touch.
Recursion bet that massive automated cell imaging plus ML beats hypothesis-driven discovery. The platform is real; the open question is whether it produces differentiated clinical assets faster than the money runs out.
GEARS uses a gene-relationship graph to predict expression changes for unseen single and two-gene perturbations. The headline numbers are strong, but later benchmarks suggest simple baselines close much of the gap.