I turn healthcare needs into systems and data analysis that get deployed and used — and I know where AI helps and where it creates friction.
With a background spanning hospital IT, clinical data analysis, and academic research, I work the full loop in healthcare: understanding what users and clinical teams actually need, analysing the data (including with AI), and then designing and deploying systems that people use in practice. Governance and trust run through that work — as conditions for a system that lasts, not as the headline.
Open to roles in Health Informatics, Clinical Systems & Data Analysis, and Digital Health — in industry and research.
I spent over seven years managing servers, web infrastructure, and IT systems, including inside a clinical environment at Mashhad University of Medical Sciences. I was close enough to healthcare to see how data moved — or failed to. Systems were designed without asking the people who used them. Workarounds were everywhere. Data sat in silos. Reports reflected what was easy to record, not what was clinically meaningful.
Why do technically sound systems get abandoned, gamed, or ignored in healthcare? I moved into Medical Informatics and then Computer Science research to find a structured answer. At UCD, working on socio-technical systems analysis, workflow modelling, and human factors, I found that the failure point is almost always the same: the system was designed around data flows, not around people and their context.
During my placement at St. James's Hospital Dublin, I analysed data collection, quality management, and reporting practices across a major acute hospital. I identified data quality gaps across multiple departments, mapped fragmented clinical workflows, and produced structured outputs used by senior operational and clinical stakeholders. The problem wasn't the data — it was the mismatch between how the system was designed and how clinical work actually happens.
Across the VICTORY and ID4AI projects at UCD, I work end to end: translating clinical and research requirements into structured solutions, analysing data, and building and deploying real systems — including a production Moodle platform used by live healthcare-education cohorts. Alongside that, I evaluate how AI is adopted, trusted, and governed in clinical settings, so that what gets deployed actually holds up in use.
I don't build software from scratch — I analyse, design, and deploy systems that actually get used.
I work with stakeholders to map clinical and organisational systems — using DFD, UML, use cases, and socio-technical analysis — to surface where data quality breaks down, where accountability is unclear, and where a system will create friction rather than value.
→ Turns vague requirements into a clear, buildable solution
I analyse healthcare and operational data in Python and SQL — cleaning, modelling, and visualising — and I evaluate AI where it's used: calibration, bias, explainability, and whether a model is fit for clinical deployment. Literate with AI, not here to re-build the models.
→ Evidence-based answers and AI that's assessed before it's trusted
I configure and deploy clinical and research platforms end to end — server setup, configuration, and user onboarding (Moodle, REDCap on AWS) — and use a human-factors lens to make sure they're actually adopted, not abandoned.
→ Live systems in real use, not pilots that stall
Designed, configured, and deployed a Moodle-based learning platform for a healthcare-education project, with custom plugins — a full production deployment from server setup to the user-facing experience, now serving live cohorts.
A scoping review examining how AI systems in healthcare build or undermine organisational trust — using socio-technical systems analysis to go beyond technical compliance to whether AI is actually adopted and used safely.
The fields that shape my work.