Antoine Savine
  • Male
  • Copenhagen
  • Denmark
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Short Bio:
Antoine Savine is a leading practitioner and a lecturer in methematical and computational finance. He is the author of the Modern Computational Finance book with Wiley.
Antoine holds a MsC (mathematics) from the University of Paris-Diderot and a PhD (mathematics) from Copenhagen University. He is well known in the quantitative finance community for influential work on cash-flow scripting, multi-factor interest rate models, generalized derivatives in the context of local and stochastic volatility models, and the wide adoption of AAD in financial systems. His latest research focuses on 'Differential Machine Learning' and application in finance.
Danske Bank
Job Title:
Quantitative Research -- Superfly Analytics
Job Function:
AI/ML Models
Number of employees:
10.000 to 24.999
LinkedIn Profile:
Contributing, Networking

Antoine Savine's Blog

Differential ML on TensorFlow and Colab

Posted on May 25, 2020 at 11:30am 0 Comments

Brian Huge and I just posted a working paper following six months of…


Automatic Differentiation in 15 Minutes -- video tutorial with application in machine learning and finance

Posted on April 17, 2020 at 7:53am 0 Comments

Recorded in Bloomberg's London offices in November 2019:

slides here

Deep Analytics: Risk Management with AI

Posted on December 10, 2019 at 1:30am 0 Comments

We first provide a mini-tutorial on  Adjoint Algorithmic Differentiation (AAD) (also known as back-propagation in machine learning). We then illustrate how  neural networks may be used to compute dynamic values and risks of trading books with applications to risk management of derivatives,  valuation adjustments (XVA), counterpart credit risk, FRTB and SIMM margin valuation adjustments (MVA). We also describe new techniques to substantially improve deep learning on simulated data, and…


Recorded workshop from Kings College London: AAD, Backpropagation and Machine Learning in Finance

Posted on October 30, 2019 at 7:00am 1 Comment

Automatic Adjoint Differentiation (AAD) and back-propagation are key technologies in modern machine learning and finance. It is back-prop that enables deep neural networks to learn to identify faces on photographs in reasonable time. It is AAD that allows financial institutions to compute the risks of complex derivatives books in real time. The two technologies share common roots.

See the AAD book here:…


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