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About Notes Links

Useful Links

People, papers, and rabbit holes that shaped how I think about all this.

How I Ended Up Here

I studied with Eleftherios Garyfallidis (creator of DIPY) during my PhD, working on denoising diffusion MRI: Patch2Self (NeurIPS 2020, with Josh) and Patch2Self2 (CVPR 2024, with Petros, Agniva, and Josh). Studying noise means studying variance, which means studying leverage.

Conformal Prediction

If you want to learn conformal prediction from scratch, start here. I did.

A Gentle Introduction to Conformal Prediction

Anastasios Angelopoulos & Stephen Bates — arXiv · F&T monograph

Conformal Prediction Video Tutorials

Anastasios Angelopoulos — YouTube

  • Part 1: Basics
  • Part 2: Conditional Coverage
  • Part 3: Beyond Conformal Prediction

Conformal Prediction GitHub

Runnable implementations with Colab notebooks.

Leverage Scores & Randomized Linear Algebra

Most of what I know about leverage scores, I learned from Petros Drineas. I started working with him during my PhD, and he opened up an entire world of randomized numerical linear algebra that I didn’t know existed.

A lot of the leverage scores literature I absorbed came through working alongside Agniva Chowdhury, who was Petros’s PhD student at Purdue and is now an AI Research Scientist at Intel.

C. Seshadhri (Sesh)

Professor of CS, UC Santa Cruz — YouTube lectures

Statistics Notes Worth Bookmarking

Cosma Shalizi (CMU Statistics) — rigorous, opinionated, and somehow entertaining. If you bookmark nothing else from this page, bookmark these.

Advanced Data Analysis from an Elementary Point of View

Regression, smoothing, causal inference. Freely available PDF.

Almost None of the Theory of Stochastic Processes

Graduate-level. Freely available PDF.

Shalizi’s Notebooks

Information theory, complex systems, philosophy of science. A wonderful place to get lost.

Shreyas Fadnavis
About Notes