Driven by curiosity.
Built on rigor.
I grew up asking why markets get things wrong — and building tools to fix it. I spent four years studying economics, public policy, and environmental science at the University of Toronto, learning to see the world through data.
What drives me is simple: information asymmetry is everywhere — in M&A deals, in fashion resale markets, in climate risk pricing. I build AI tools that close those gaps, giving people and institutions the edge that better data creates.
I am headed to Georgetown McDonough MSBA in Fall 2026, going deeper at the intersection of quantitative finance, machine learning, and market intelligence. Long term, I want to be in the rooms where the hardest market problems are being solved — and to be building the tools that solve them.