Lecturer in Applied Mathematics

Simon Martina-Perez

  • I develop novel mathematical and statistical techniques to analyse, model, and predict biophysical phenomena.
  • The best part of teaching at Oxford is getting to know one’s students well and really making an impact through small-scale teaching.
  • Lately, I’ve been using maths to understand cancer evolution and how to best design drug treatment protocols for neuroblastoma patients.

Profile

I recently finished my DPhil in applied mathematics before starting clinical training as a student on the Oxford graduate-entry medicine course. Being interested in clinical medicine as well as mathematics,  I work at the interface of mathematics, statistics, and biophysics. 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!

Teaching

I teach the first-year courses in applied mathematics.

Selected Publications

Jennifer Kasemeier and Simon Martina-Perez et al. ‘Identification of Neural Crest and Neural Crest-Derived Cancer Cell Invasion and Migration Genes Using High-throughput Screening and Deep Attention Networks.’  bioRxiv pre-print, 2024.03. 07.583913

Simon Martina-Perez et al. ‘Optimal control of collective electrotaxis in epithelial monolayers.’ Bulletin of Mathematical Biology 86 (8), 95 2024.

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.

Subjects
Simon Martina-Perez
simon.martina-perez@magd.ox.ac.uk

As an applied mathematician, I have the pleasure to work with a wide range of people, experimental, and clinical, to unravel some of the most exciting problems in modern quantitative biology!