Michael Y. Li

Hi! I'm a Computer Science PhD student at Stanford, where I'm advised by Noah Goodman and Emily Fox.

Previously, I graduated summa cum laude and Phi Beta Kappa from Princeton, where I was fortunate to work with Tom Griffiths and Ryan Adams.

Research

I work broadly on reasoning in LLMs. My previous research explores how to integrate LLMs into statistical/data science workflows in principled, statistically rigorous ways. Before that, I worked on probabilistic methods (e.g., Sequential Monte Carlo). My email is firstname.middle_initial.lastname@stanford.edu!

Automated Hypothesis Validation with Agentic Sequential Falsifications

LLMs + sequential hypothesis tests with Type-I error control

BoxingGym: Benchmarking Progress in Automated Experimental Design and Model Discovery

A benchmark for LLM driven experimental design and model discovery

Learning to Learn Functions

We propose hierarchical Bayesian models of how people learn to learn functions and validate our model in behavioral experiments.

Design and source code from Jon Barron's website