Research & education
Where the engineering was formalised
Three institutions, one thread: learning to hold a result to a standard before believing it.
Boston University
Dates to confirm
Master's degree
Graduate study, and the point where the machine learning work stopped being self-taught and became method.
Massachusetts Institute of Technology
Dates to confirm
Research experience
Research conducted at MIT: technical research and experimentation, and the discipline of holding a result to a standard before believing it.
Research experience at MIT — not an MIT degree program.
Trinity College
Dates to confirm
Computer Science · Undergraduate study, alongside collegiate squash
Studied Computer Science while competing for Trinity College, one of the most successful programmes in collegiate squash.
Research approach
Graduate study and research changed how I build, more than what I build.
Before it, my experimentation was intuition with a stopwatch: try something, see if it feels better, keep it. Research replaced that with a method — define the question precisely, build the measurement first, control what you can, and treat a promising result with suspicion until it survives a real test.
That habit is the single most transferable thing I took into industry. Most of the difference between an AI system that demos well and one that can be relied on is whether someone built the evaluation before they built the enthusiasm.
Note on accuracy: the MIT entry above describes research experience associated with MIT. It is not a degree from MIT, and is not described as one anywhere on this site.
Publications & talks
Nothing listed yet
Publications, preprints and talks will be listed here as they are confirmed. Nothing is listed that hasn't been.
Contact
Building something at the intersection of AI and the real world?
I'm glad to hear from engineers, founders, researchers, coaches and athletes — and from anyone working on AI systems that have to be right rather than merely impressive.