I am a researcher in the University of Bristol Business School, with a PhD in Sociology and specialist training in Science and Technology Studies. Over the past 15 years, I have built a research career examining how knowledge, evidence, risk, and expertise are produced, represented, and made accountable across medicine, artificial intelligence, robotics, data governance, and emerging simulation technologies. My work is grounded in STS, with particular interests in the epistemic and political dimensions of algorithmic systems, computer models, simulations, and digital twins.

 

I am especially interested in how representational choices are made, justified, and mobilised as evidence in practice: how bodies, environments, infrastructures, and forms of work are translated into images, datasets, parameters, dashboards, interfaces, and models. Across my research, I ask how these computational and visual representations shape what counts as credible knowledge, what becomes visible or invisible, whose expertise is recognised, and how uncertainty is managed in real-world settings.

 

My PhD examined embodied knowledge and medical image interpretation, focusing on how experts make images meaningful through situated practices, professional vision, indexical reasoning, and local standards of evidence. I now extend this lens to computational representation: how algorithmic systems, simulations, and digital twins are designed, validated, documented, and used as evidence in clinical, regulatory, and organisational contexts.

 

Since 2019, I have held five postdoctoral research roles on applied interdisciplinary projects in AI, robotics, medical imaging, digital twins, and data governance. These include AI in the Clinic (2019-2021), Enhancing Cardiac Care Through Extensive Sensing (2021), the UKRI Trustworthy Autonomous Systems programme (2021-2024), Transparency Regulation Toolkits for Responsible AI (2024), and the AI Development Best Practice Pipeline Tool (2026-). Through these projects, I have collaborated with clinicians, radiologists, biomedical researchers, computer scientists, roboticists, engineers, data scientists, nurses, patients, SMEs, policymakers, and publics.

 

My current research focuses on three connected areas. First, I am developing STS-informed research on medical digital twins, examining how simulation models are assembled, tested, and made clinically meaningful. Second, I work on AI governance, transparency, assurance, and responsible innovation, including my collaboration with Oldfield Consultancy on an AI Development Best Practice pipeline for UK SMEs, aligned with emerging expectations around transparency, assurance, accountability, and governance. Third, I am expanding my work into climate resilience and environmental governance, particularly the use of digital twins and modelling to support adaptive planning in healthcare infrastructure.

 

Across this work, I ask: what counts as evidence, who has the authority to make it credible, and how are risk and uncertainty made accountable in practice? How do standards, datasets, models, interfaces, and documentation configure what simulations and algorithmic systems can do — and what they hide? My aim is to contribute to interdisciplinary research that helps to both analyse and build models, algorithmic systems, simulations, and digital twins that are explainable, auditable, socially robust, and responsive to public value.