Fuming Yang
Research Fellow at Harvard · Incoming PhD student at MIT
I study two brains in parallel — the artificial and the biological. On the artificial side, I work on representation geometry and training dynamics in AI; on the biological side, I'm leading data-acquisition technologies for the whole mammalian brain connectomics project at Harvard. I'll be starting my PhD at MIT in Fall 2026. Most of my recent work develops new technologies for studying both.
Selected papers
Science of AI
A label-free phase coordinate β(t)/βc(t) for representation dynamics,
derived from a passive GMM probe and verified across SAEs on Pythia, SSL on
CIFAR-10/100, and grokking on modular arithmetic.
Stop SAE training at 5% on early-to-mid LM layers and recover ≥ 96% per-atom
POS purity at 20× less compute. Five-seed evaluation across Pythia-160M /
410M / 1.4B; degrades with layer depth (99% at L6, 88% at L18).
An activity-dependent rule that prunes weights during training under a fixed
broadcast budget. *Equal contribution.
AI for Science
Recovers a global ordering of tens of thousands of EM sections from sparse
pairwise alignments via graph condensation and super-chains.
VQ-VAE with a transformer prior reaches ~1000× compression on nanoscale EM
volumes while preserving downstream segmentation accuracy.
Other