My PhD examined embodied knowledge and medical image interpretation - how experts make images meaningful through members’ methods (indexicality, accountability, professional vision). Treating images as co-produced artefacts, I showed how meaning arises from situated practice, instruments, and local standards of evidence. I now extend this lens to computational representation: how algorithmic systems, computer models, and simulations translate bodies, environments, and work into parameters, meshes, datasets, and dashboards - and how representational decisions (data selection, discretisation, model coupling, interface defaults, documentation) shape what counts as credible knowledge, what becomes visible or invisible, and whose work “shows up” as evidence. In short, I move from how experts read images to how we build images that are read by machines, and how both forms of representation organise practice and authority. Since 2021, I’ve developed a growing interest in the development of real-time simulation models - most notably 'digital twins'.