Antoine Savine

Antoine Savine is a French mathematician, academic and financial derivatives practitioner with Superfly Analytics at Danske Bank, winner of the In-House System of the Year 2015 Risk award and RiskMinds’ Excellence in Risk Management and Modelling 2019 award. In the past, he has held multiple leadership positions in quantitative finance, including Global Head of Derivatives Research at BNP-Paribas.

Antoine also lectures at Copenhagen University’s Masters of Science in Mathematics-Economics, with topics including Volatility Modeling, Numerical Finance and Machine Learning in Finance.

Antoine holds a Masters in Mathematics from the University of Paris-Jussieu and a PhD in Mathematics from Copenhagen University. He is best known in quantitative finance for his contribution to risk managementvolatility and multi-factor interest rate models. As a practitioner, he was influential in the wide adoption of cashflow scripting and automatic differentiation (AAD).

Automatic Differentiation Explained in 15min, Bloomberg Bloomberg Tech Talks, 2019
slides here
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Brian Huge and Antoine Savine, Risk, October 2020

Automatic Differentiation

& Differential Machine Learning

At Danske Bank, Antoine wrote the book on automatic adjoint differentiation (AAD) and developed differential machine learning with Brian Huge, a novel family of machine learning algorithms, capable of spectacular performance by combination with AAD.

A brief presentation of differential machine learning, full story in the slides below


Automatic Differentiation (AAD) textbook on Amazon
Scripting textbook now available for pre-order, shipping Novmber 2021

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