Artificial Intelligence
AI is not magic — it is math, history, and human choice. We build from first principles: probability, search, neural networks, language models. Then we ask who built these systems, for whom, and what they're encoding about the world.
Machine Learning
ML algorithms are not neutral mathematical facts — they are choices about what to optimize, whose data counts, who bears the error. We derive every algorithm before using it, implement from scratch before touching any library.
& Algorithms
Every data structure is an argument about the world. Every algorithm is a strategy, a tradeoff, a value judgment. We implement every structure before using the library version. No LeetCode grind culture — deep projects that transfer.
One draft in a
broader research
program.
This curriculum proposal doesn't stand alone. It is the curriculum-design thread of a three-part research program on equitable community-college computing education I am developing under the mentorship of Jeff Anderson.
The other two threads are empirical (what actually helps community-college students stay in CS?) and methodological (how do you audit a curriculum for whose learning it supports?). The companion hub — the index of papers, working notes, and experiments — is the Modeling Bench.