interpretability

This project studies feature learning in deep neural networks: how useful internal features emerge during training, how they become organized, and how they support generalization.

This paper studies the internal geometry of sentence embeddings, showing how latent semantic features can be identified, composed, and used for more interpretable embedding-based systems.

This project asks whether language representations admit canonical structures: stable representational forms determined by linguistic tasks, semantic relations, or the geometry of meaning itself.