PhD candidate in Mathematics at NYU Courant, building machine-learning and dynamical-systems models for high-dimensional data — from cortical circuits to deep networks trained from scratch.
I work where applied mathematics meets machine learning — designing models with a real theoretical spine, then proving they hold up against data.
My research applies deep learning and optimization to high-dimensional, neural data: crafting loss functions and minimization schemes that recover the structure underneath. The same toolkit — rigorous modeling, gradient-based optimization, and disciplined experimentation — is what I'm looking to bring to an AI/ML or quantitative research internship.
Currently seeking a Summer 2027 internship in AI/ML or quantitative research. If your team values mathematical rigor and models that actually hold up, I'd love to talk.