Applied scientist working at the intersection of math and machine learning.
I build things with machine learning and spend too much time thinking about the math behind them. Lately I've been digging into quantization theory, numerical linear algebra, and prediction markets.
If you want to work together or hire me for a project, email me at prateekchandrajha@gmail.com.
A complete, self-contained walkthrough of the easiest proof of the JL lemma. Every tool — expectation, MGFs, Markov, Chernoff, chi-squared concentration, union bound — built from definitions with examples, showing how small pieces snap together into one of the most useful results in high-dimensional geometry.
How random rotations, optimal quantizers, and a one-bit trick compress LLM memory by 6x while staying within 2.7x of the information-theoretic limit. Includes a puzzle about high-dimensional geometry.