I work at the intersection of neuroscience, genetics, and machine learning: intelligent systems that learn to adapt and cooperate. I am currently at the California Institute of Technology (Caltech), as a summer exchange student from the University of Cambridge, studying consolidative memory replay in neural circuits during sleep.
I am a general, technical problem solver: I optimise to help others, constrained by my interest in biomedical and scientific breakthroughs, powered by improvements in data and compute. If you're interested in this too, I would love to hear from you.
Contact me
Book a meeting here or reach out by email.
Publications
Christopher Leung, Charlotte Houldcroft, Aylwyn Scally. Statistical inference of viral ancestral recombination graphs (2026). In preparation.
Current projects
Spontaneous neuronal activation · 2026–present
Lois group, Caltech
Computational modelling of spontaneous neuronal activity and its role in the restoration of learned behaviours following perturbation. Drawing on attractor network theory and Hebbian plasticity. Part of the Cambridge–Caltech exchange, funded by Caltech and St Catharine's College, Cambridge.
Viral ancestral recombination graphs · 2025–present
Department of Genetics, University of Cambridge
Scalable inference of ancestral recombination graphs for viral DNA, using Markov Chain Monte Carlo and perturbation theory. Awarded the J.M. Thoday Prize for the best undergraduate research project.
Deep learning for genome organisation · 2025–present
Hannon Group, Cancer Research UK Cambridge Institute
Deep learning for predicting three-dimensional chromatin organisation from sequencing data, in the laboratory of Greg Hannon. Work focused on learning sequence determinants of topologically associating domains and compartment structure. Publication in preparation.
