A technical path into investment thinking.

I began in electronics and computer engineering, then moved into applied AI: graph neural networks, enterprise language-model pipelines, information extraction, and machine-learning systems.

An early role at an asset manager made markets feel like the most interesting system of all. They combine data and mathematics with incentives, narratives, institutions, and uncertainty. Financial engineering became the bridge between my technical background and that interest.

At NYU, I’m developing that bridge through valuation, derivatives, credit risk, portfolio construction, fixed income, and investment research. My goal is to do work where analytical rigor supports consequential financial decisions.