About

I'm interested in how nature finds elegant solutions to complex problems, as well as the pressures or constraints which help to produce them. I work in theoretical neuroscience and machine learning.

Specifically, I study efficiency in natural learning and representations, and focus on models of the cortex. This includes energy-based models (especially predictive coding), network topology, and Cholletian intelligence.

At Mercor, I am a project lead and machine learning researcher.

At Algoverse, I am a machine learning research mentor and senior author.

At Launchpad, I've led and contributed to machine learning research projects, and was the head of education.

At Berkeley, I taught the Linguistics 198 course on linguistic relativity [see syllabus], and am a student in the class of 2026.

This fall, I will be pursuing an MPhil in NeuroAI and Intelligent Systems at Cambridge.

I'm happy to talk about research, collaboration, or breaking into the field; feel free to contact me.