Financial Engineer of the Year:

Bruno Dupire

And IAQF Innovation Award Reciptient:

Antoine Savine

Award Dinner Information

Tuesday, May 19, 2026

The Yale Club

New York City

6:30pm Cocktails 7:30pm Dinner

Corporate sponsorships including tables of ten are available

Individual seats are available for $600

For more information please contact the

IAQF office at 646-736-0705 or info@iaqf.org

About the Winner: 

Bruno Dupire is head of Quantitative Research at Bloomberg L.P., which he joined in 2004. Prior to this assignment in New York, he has headed the Derivatives Research teams at Société Générale, Paribas Capital Markets and Nikko Financial Products where he was a Managing Director. He is best known for having pioneered the widely used Local Volatility model (simplest extension of the Black-Scholes-Merton model to fit all option prices) in 1993 and the Functional Itô Calculus (framework for path dependency) in 2009. After a Master's degree in Artificial Intelligence in 1982 and a PhD in Numerical Analysis in 1985 he has conducted in 1987-88 a study to apply Neural Nets to time series forecasting for Caisse des Dépôts et Consignations. 

At Bloomberg he has initiated the BQuant project, a quant platform that combines easy access to the data with the expressivity of Python. He runs the BBQ (Bloomberg Quant) seminar, the largest monthly quant seminar worldwide. He is a Fellow and Adjunct Professor at NYU and he is in the Risk magazine “Hall of Fame”. He is the recipient of the 2006 “Cutting edge research” award of Wilmott Magazine, of the Risk Magazine “Lifetime Achievement” award for 2008 and of the 2025 Financial Engineer of the Year Award of IAQF. 

About the Innovation Award Winner:

Antoine Savine is a Managing Director and the Global Head of Rates Quants at Barclays Investment Bank. In the previous years, he led Macro Analytics at Hudson River Trading. In the previous decades, he built the Fixed Income Quant group at BNP, and served as chief quantitative analyst at Danske Bank's Superfly Analytics and adjunct professor of mathematical finance at Copenhagen University. He wrote the book on adjoint differentiation (AAD) with Wiley (Modern Computational Finance, 2018), created differential machine learning with Brian Huge, and delivered multiple influential publications and conference talks. He holds a PhD in mathematical finance from Copenhagen University and a Masters (DEA) of mathematical finance from University of Paris, City.