Personal Summary

I am focused on integrating AI into financial decision systems at an early stage of structural change.

Trained in mathematics and risk engineering at Duke University, I work at the intersection of frontier AI and investment analysis, with a consistent emphasis on disciplined judgment under constraint.

Finance operates under regulation, risk accountability, and capital responsibility. As AI capabilities advance, the central challenge is no longer raw intelligence alone, but the rule-set that makes AI deployable: how outputs are stress-tested, governed, and embedded into real financial workflows without degrading decision integrity.

My work centers on building decision-grade standards and operating frameworks that define when AI reasoning can be trusted in high-stakes processes such as screening, valuation, and risk assessment. I translate ambiguous trade-offs into explicit criteria, create structured validation protocols, and codify the resulting rules into repeatable playbooks.

The AI-finance interface is still in its formative stage. I am interested in contributing to its first generation of practical integration rules - systems rigorous enough for regulated capital, yet adaptive enough to capture technological advantage.