Teaching

My teaching philosophy centres on one conviction: the most durable understanding comes from seeing the same idea at two levels simultaneously — the abstract structure and the concrete consequence. In a field as applied as Computer Science, a student who can only state a theorem is less equipped than one who can also say "here is where this breaks down in production, and here is what you do about it."

My own background makes this dual-level teaching natural. Before returning to academia, I spent nearly a decade as a Senior Developer and Solutions Architect at Deloitte and Accenture — designing APIs that served tens of millions of weekly requests, managing PII-sensitive financial systems, and leading Agile delivery teams across global accounts. That experience means I can tell a student not just how a software design pattern works, but why a large engineering organisation adopted it, and what failure mode it was invented to prevent. When I teach evolutionary algorithms, I can connect the convergence theory to the aerodynamic optimisation problem I actually implemented. When I TA machine learning, I can describe the gap between a model that scores well on a benchmark and one a stakeholder will trust in deployment.

In practice this means my classroom is deliberately interactive. I design sessions around problems first — a concrete challenge is presented before the technique that solves it, so students develop intuition for why a method exists before learning its mechanics. Office hours are treated as collaborative debugging sessions, not just answer-delivery windows. Assignments are calibrated to be difficult enough that students feel genuine satisfaction when they succeed, and real-world enough that they can describe what they built in a job interview.

My goal is to produce graduates who are not only technically capable, but who understand that every system they build will be used, maintained, and eventually replaced by people — and who design accordingly.