Research NEWS

on top of things

Our data scientists are at the forefront of pioneering machine learning research. They engage in continuous intellectual and scientific exchange and are active members in various research collaborations with internationally renowned Swiss universities and technology institutions, such as the ETH Zurich, to develop the next generation of machine learning algorithms.

By partnering with ZENAI, you chose world-class talent, deep and proven technical knowledge as well as expertise in the financial environment. ZENAI’s research findings and capabilities are continuously embedded into our products and strategic consulting. 

The following is a short-list of publications, co-written by ZENAI engineers. Their findings are widely acclaimed and often cited in the academic field of machine learning.

Featured Publications

* ZENAI employee / research collaboration

NEW Pricing and hedging American-style options with deep learning

Sebastian Becker*, Patrick Cheridito*, Arnulf Jentzen*

Uniform error estimates for artificial neural network approximations for heat equations
Lukas Gonon, Philipp Grohs, Arnulf Jentzen*, David Kofler*, David Šiška
Solving high-dimensional optimal stopping problems using deep learning

Sebastian Becker*, Patrick Cheridito*, Arnulf Jentzen*, Timo Welti

Deep splitting method for parabolic PDEs

Christian Beck, Sebastian Becker*, Patrick Cheridito*, Arnulf Jentzen*, Ariel Neufeld

A stochastic partial differential equation model for limit order book dynamics

Rama Cont, Marvin S. Mueller*

Analysis of the generalization error: Empirical risk minimization over deep artificial neural networks overcomes the curse of dimensionality in the numerical approximation of Black-Scholes partial differential equations

Julius Berner, Philipp Grohs, Arnulf Jentzen*

Solving stochastic differential equations and Kolmogorov equations by means of deep learning

Christian Beck, Sebastian Becker*, Philipp Grohs, Nor Jaafari*, Arnulf Jentzen*

Deep optimal stopping

Sebastian Becker*, Patrick Cheridito*, Arnulf Jentzen*

Machine learning approximation algorithms for high-dimensional fully nonlinear partial differential equations and second-order backward stochastic differential equations

Christian Beck, Weinan E, Arnulf Jentzen*