Martin completed his Eng.D. in Computer Science at The University of York in 2016, during which he investigated computational approaches for the analysis of large scale -omics datasets. His thesis focussed on the study of non-targeted LC-MS and NMR data and the issues of systematic and non-systematic noise, as well as the dimensionality problem where nvar ≫ nobs. His thesis culminated in the application of techniques to the development of a software analysis pipeline suitable for use by the end-user.
Prior to his doctorate, Martin worked for 5 years as a software engineer for the embedded systems sector, having developed both user and kernel-mode software for prototype and research-level systems.
Prior to this, he completed his Masters degree in Chemistry, having studied computational algorithms for the analysis of celestial chemical abundances. Martin is currently working as a Research Associate at the McInerney Lab, investigating the application of data-driven approaches to the study of introgression ("the merging of evolving entities"). This has involved the development of new, and consolidation of existing tools, in order to facilitate the creation of a no-SQL database using an architecture based on graph-theory. It is theorised that this systems-biological approach will facilitate the study of the flow of genetic information between organisms, assisting in the discovery of both genetic hubs and isolated pools.