Article with code, Wilmott Jan20

The second part of my paper  “Computation graphs for AAD and Machine Learning part II: Adjoint differentiation and AAD” was just published in Wilmott, the second in a series of three articles with code dedicated to AAD and Computational Finance in general. It covers computation graphs, backpropagation, AAD and implementation in finance, taking inspiration in the recent achievements of the Superfly Analytics group of Danske Bank.

The first part was published in Wilmott November 2019 issue.

A. Savine, “Computation graphs for aad and machine learning part ii: adjoint differentiation and AAD” Wilmott, vol. 2020, iss. 105, p. 32-45, 2020.

Excellence in Risk Management and Modelling award | RiskMinds 2019 | Winner: Superfly Analytics at Danske Bank

From Risk Minds web page: “This award is for the team who has tirelessly designed, developed and implemented new models and risk management techniques that have helped their financial institution manage risk more accurately and/or helped their institution comply with regulatory requirements.”

RiskMinds is “the world’s largest risk management event. 700+ CROs and experts from banks, buy-side, regulators, academia and beyond cover every hot topic in risk.”

Superfly Analytics presented its One Analytic Engine and Deep Analytics: Risk Management with AI. See all the slides here.