I'm a mathematician with a graduate degree from UC Santa Cruz, where I specialized in algebraic topology. My thesis produced an original theoretical extension of UMAP — a state-of-the-art manifold learning algorithm whose mathematical foundations lie in topological data analysis and simplicial geometry.

That work required building intuition for high-dimensional structure, constructing rigorous proofs in novel territory, and translating abstract mathematics into computational results — skills that transfer directly into quantitative research.

I am now applying this foundation to financial mathematics, with a focus on derivatives pricing and volatility modeling. My current project calibrates the Heston stochastic volatility model to live SPY options data, building the full pipeline from market data ingestion through implied volatility extraction, model calibration, and a semi-analytic Greeks engine.