ml researcher @ ucla · 2026

i'm working with a phd student in the ucla math department on linear self-attention mechanisms, looking at how attention can be approximated efficiently without the usual quadratic cost. the manuscript is still in progress, but the core idea is figuring out where linear approximations hold up against standard attention and where they break down. it's given me a much deeper handle on the math underneath transformer architectures, not just how to use them.