Simon Martina-Perez

I am currently finishing my DPhil in mathematics. I work at the interface of mathematics, statistics, and biophysics, and I seek to understand how cells in a biological context work together in embryogenesis, regeneration, and pathology. Recent advances in experimental techniques, microscopy, and data processing have enabled researchers to collect vast amounts of experimental data at unprecedented spatial and temporal resolution. Automated high-throughput experiments, for example, allow researchers to study thousands of experimental and genetic conditions at once, often at single-cell resolution or finer. Given all this data, we need advanced mathematical and statistical techniques to analyse and interpret what we observe experimentally. It’s a real pleasure to work together with a wide range of people, experimental, and clinical, to unravel some of the most exciting problems in quantitative biology!

I teach the first-year courses in applied mathematics.

Simon Martina-Perez et al. ‘Bayesian uncertainty quantification for data-driven equation learning.’ Proceedings of the Royal Society B: Biological Sciences, 477(2254), 10 2021.

Simon Martina-Perez et al. ‘Efficient Bayesian inference for mechanistic modelling with high-throughput data.’ PLoS Computational Biology, 18(6), 6 2022.