Dublin · Medical AI & research engineering

I build systems that make evidence, signals and structure easier to reason with.

I work across machine learning, biomedical data, intelligent sensing and the practical systems that make research reproducible and useful. I care about the path from a defensible question to an inspectable system—not only the final model score.

0.176BPP · ECM on Kodak
692source-traceable clinical passages
10,599apo–holo pairs explored
12EEG model families compared

Each figure is explained and bounded in its case study.

Selected work / 01—05

Research, with the scaffolding left visible.

Questions, methods, reported evidence and limits—presented together so the reader can judge the work rather than decode a list of tools.

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How I work

Build the instrument.
Interrogate the reading.

01

Start with the failure mode

I want to know what a system should refuse, where a metric misleads, and which assumption will break first.

02

Make evidence inspectable

Manifests, exact citations, explicit splits and narrow status labels make a project easier to question—and stronger for it.

03

Keep the system real

Models eventually meet APIs, sensors, latency, hardware and people. I enjoy working in that seam.

A—

About the trajectory

From embedded systems and IoT to biomedical machine learning.

My undergraduate work moved between hardware, web systems and machine learning. The recurring interest was never a single stack; it was how information moves through a system, where it degrades and how decisions can remain intelligible.

That led from crop-surveillance prototypes and neural compression into protein graphs, EEG analysis and evidence-grounded clinical retrieval—and now to postgraduate medical-AI study at UCD.

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Research conversations

Questions, critique and collaboration are welcome.

If you’re working on a biomedical ML problem, evidence-grounded system or sensing challenge, I’d be glad to compare notes.

Write to me