My interest in data analytics and AI automation didn't start in a classroom — it started on-site, at major international sporting events, where the same frustrations kept resurfacing project after project. There was never an efficient system to manage the dozens of moving resources an event of that scale demands.
Transportation was the worst offender. Tracking vehicle inventory, matching the right drivers to the right routes based on their training, keeping headcounts accurate for the day — it was less a workflow and more a daily gamble.
On one event, syncing vehicle requirements, route assignments, and driver musters required three separate programs running at once. Something broke almost every time. It wasn't a process — it was a constant fight against the tools meant to support it.
That instinct to find the root cause, not just patch the symptom, is the same one that's always pulled me toward research. Long before /N3U existed, I was the person reading the paper, not just the abstract — trying to understand why a finding held up, or didn't, rather than taking the headline at face value. That habit of digging into the evidence is what eventually turned into a genuine specialty: helping researchers pressure-test their own findings with the same rigour I once wished someone had brought to the field.
