Most of my work comes down to one question: is there a signal here, and can I trust it? Everything else — the architecture, the pipeline, the deployment — is in service of answering that honestly.
A clinical ECG and a gravitational-wave detector could not look more different. One is a few seconds of millivolts from a body; the other is a kilometres-long interferometer listening for spacetime to ripple. Yet both spend almost all of their time measuring noise, and both succeed or fail on the same craft: separating a faint, structured thing from a loud, formless background.
The toolkit overlaps more than people expect. Matched filtering, denoising, time-frequency representations, anomaly detection — and, increasingly, learned models that fold all of this into one end-to-end system. Change the units and a surprising amount transfers.
I came to this from neuroscience and biomedical signals, but the methods point outward. The plan is to keep following them — toward larger data, fainter signals, and the parts of astronomy and astrophysics that have been refining detection-in-noise for decades.
(Early draft — notes more than essay.)