Notes & resources
Things I’ve written, worked through, or found useful while learning and building.
Research & writing
Review paper
The Role of Statistical Foundations in Machine Learning: Bias, Uncertainty, and the Limits of Predictive Models
An 18-page review connecting statistical theory with published case studies on bias, uncertainty, and fairness in machine learning. Read or download the PDF.
Technical report
Auditing the Objective
A co-authored technical report on making the objectives of engagement-driven recommendation systems explicit, measurable, and auditable.
Teaching
Workshop slides
Git Gud workshop
Slides for a 30-minute Git, GitHub and GitLab workshop for the De Anza Game Dev Club, with a small browser game for practicing commits, branches and pull requests.
Learning references
Learning roadmap
ML Road
A learning path through machine learning, deep learning, LLMs, and AI agents, linking to courses from their original publishers. Adapted from Shengjia Yan’s ml-road collection.