I came to engineering through a side door. Over six years founding and leading my high school's Model UN — including proposing technology-adjacent resolutions at the UN General Assembly — the wall was always the same: the shape of the problem was clear, but not the tools to build a solution. That gap is what brought me to computer engineering.

The double minor in psychology and cognitive science wasn't incidental. Studying intelligence as a unified concept — how it emerges in biological systems, how we formalize it in machines, and where those two accounts diverge — CE provided the technical foundation, and the minors the framework for asking why any of it matters.

At the University of Waterloo's TRuST Network, the focus was on how inductive reasoning and model over-generalization systematically degrade performance on statistical minority groups — and building tools to surface those failures for policymakers who can act on them. At RBC Borealis, the question was infrastructural: what does it actually take to deploy AI agents reliably in a regulated financial environment, where a hallucination isn't an inconvenience but a liability?

Being part of the IPC's advisory body made the other side of that work concrete. Regulation doesn't move at the speed of model development, and that gap has consequences. Understanding how governance frameworks are actually constructed — what they can and can't capture about the systems they're meant to govern — is, arguably, as technically important as understanding the models themselves.

Over twelve years as a competitive rhythmic gymnast on the Canadian senior national team. It shaped a lifelong commitment to disciplined, iterative improvement — the kind that doesn't have a defined endpoint.