Executive leader bridging mathematics, machine learning, and business strategy
As Managing Director and Head of Machine Learning at TWG Global AI, I lead quantitative AI model research, development, and deployment across a $55B investment portfolio, combining deep technical expertise with executive business acumen. My unique background spans pure mathematics research at MIT to building production ML systems serving millions of users globally.
My career journey has taken me from academic research in algebraic topology to quantitative finance at a derivative strategy hedge fund, then to global fintech where I deployed ML models for financial inclusion across emerging markets. Today, I focus on enterprise AI strategy, helping organizations leverage machine learning for competitive advantage.
I earned my Ph.D. in Mathematics from MIT at age 21, specializing in algebraic topology under Lars Hesselholt. My undergraduate degree is from UC Irvine, where I graduated Magna Cum Laude with Phi Beta Kappa honors at age 17. This mathematical foundation provides the analytical rigor that drives my approach to machine learning and quantitative modeling.
Throughout my career, I've focused on translating complex mathematical concepts into practical business solutions. Whether developing risk models for hedge funds, building credit scoring systems for underbanked populations, or architecting enterprise MLOps infrastructure, I excel at bridging the gap between advanced analytics and business value.
I remain passionate about education and mentoring students in STEM fields, staying active in several mentoring programs at my undergraduate alma mater, UCI. I believe in giving back to the academic community that shaped my career and helping the next generation of data scientists and mathematicians achieve their potential.