This is a short collection of resources I have found useful. It is not meant to be complete in any sense. I update this page occasionally as I come across new material.

Statistical Inference: Classical & Modern

1. An Overview of Large Language Models for Statisticians — Wenlong Ji et al. The American Statistician, 2026.

2. Intermediate Statistics (36-705) — Larry Wasserman, Carnegie Mellon University. Lecture notes.

3. STATS 300C: Theory of Statistics — Emmanuel Candès, Stanford University. Course and lecture notes.

Diffusion Models

1. What Are Diffusion Models? — Lilian Weng. Blog post.

2. Generative Modeling by Estimating Gradients of the Data Distribution — Yang Song. Blog post.

3. Diffusion Models for Discrete Data — Aaron Lou, Stanford CS236. Lecture slides and video.

Research Writing

1. Ten Simple Rules for Mathematical Writing — Dimitri Bertsekas. Lecture slides.

2. Checklist for Effectively Writing Papers in Stat-ML — Aaditya Ramdas. Writing checklist.

3. A Student’s Guide to Writing with ChatGPT — OpenAI. Student writing guide.