Writing
Model breakdowns, practical guides, and how I approach problems in data.
Most of it comes back to where methods hold, where they break, and what to build around them.
Averages Are the Wrong Summary for Most Things You Measure
On heavy-tailed data the mean is neither typical nor stable. A simulation of what goes wrong, and how to choose the statistic that fits the decision.
Convexity Is What Makes a Problem Solvable, and Almost Nothing Is Convex
Most applied optimization quietly swaps the real problem for a convex one. An experiment on what that swap costs, and how to tell when it is expensive.
The Newcomb Paradox: What a Thought Experiment Can Teach Us About Trusting Models
A sixty-year-old decision puzzle about a near-perfect predictor, and what it says about the gap between predicting an outcome and acting on the prediction.
When the Outlier Is the Signal: Reimagining Anomaly Detection for High-Stakes Engineering
In monitoring, the deviation is the reason the system exists. What changes when you stop treating outliers as noise.
The Beautiful Mathematics Behind Machine Learning
The linear algebra, calculus, probability, and information theory under modern machine learning, followed into financial markets and fraud detection.