Research engineer · machine learning · formal methods

Building strange things carefully.

I build and test unusual machine-learning systems: compact representations, constrained architectures, distributed inference, and the machinery needed to tell whether an idea actually works.

Current result: the model retains useful information that its own prediction step barely uses. A small probe recovers much more revision-branch signal from the same frozen state. Why that gap exists is still open. Read the result →