World's Best Scientists 2026 revealed!
Patrick Cheridito

Patrick Cheridito

D-Index & Metrics

Mathematics

D-Index
35
Citations
5757
World Ranking
2756
National Ranking
48

Overview

Patrick Cheridito is affiliated with ETH Zurich in Switzerland and works primarily in the field of Computer Science. Their research spans multiple subfields including Artificial Intelligence, Finance, Statistical and Nonlinear Physics, Computational Mechanics, and Ocean Engineering.

They have published extensively on topics such as stochastic processes and financial applications, model reduction and neural networks, neural networks and applications, stochastic gradient optimization techniques, reservoir engineering and simulation methods, sparse and compressive sensing techniques, and advanced numerical methods in computational mathematics.

Frequent coauthors collaborating with Patrick Cheridito include Arnulf Jentzen, Florian Rossmannek, S. Becker, Ariel Neufeld, and Christian Beck.

Patrick Cheridito has contributed to various publication venues, with multiple works appearing in arXiv (Cornell University) and the Repository for Publications and Research Data (ETH Zurich). Other venues include the SIAM Journal on Scientific Computing, the Journal of Risk and Financial Management, and the IEEE Transactions on Neural Networks and Learning Systems.

Recent papers authored by Patrick Cheridito include:

  • "Non-convergence of stochastic gradient descent in the training of deep neural networks" (2021), Repository for Publications and Research Data (ETH Zurich)
  • "Efficient Approximation of High-Dimensional Functions With Neural Networks" (2021), IEEE Transactions on Neural Networks and Learning Systems
  • "A proof of convergence for gradient descent in the training of artificial neural networks for constant target functions" (2022), Journal of Complexity

Other significant papers where Patrick Cheridito was a co-author include:

  • "Deep Splitting Method for Parabolic PDEs" (2021), SIAM Journal on Scientific Computing
  • "Pricing and Hedging American-Style Options with Deep Learning" (2020), Journal of Risk and Financial Management

Best Publications

  • Fractional Ornstein-Uhlenbeck processes

    Patrick Cheridito;Hideyuki Kawaguchi;Makoto Maejima

  • Market price of risk specifications for affine models: Theory and evidence

    Patrick Cheridito;Damir Filipović;Robert L. Kimmel

  • Mixed fractional Brownian motion

    Patrick Cheridito

  • Arbitrage in fractional Brownian motion models

    Patrick Cheridito

  • Dynamic Monetary Risk Measures for Bounded Discrete-Time Processes

    Patrick Cheridito;Freddy Delbaen;Michael Kupper

  • Second-Order Backward Stochastic Differential Equations and Fully Nonlinear Parabolic PDEs

    Patrick Cheridito;H. Mete Soner;Nizar Touzi;Nicolas Victoir

  • RISK MEASURES ON ORLICZ HEARTS

    Patrick Cheridito;Tianhui Li

  • COMPOSITION OF TIME-CONSISTENT DYNAMIC MONETARY RISK MEASURES IN DISCRETE TIME

    Patrick Cheridito;Michael Kupper

  • Equivalent and absolutely continuous measure changes for jump-diffusion processes

    Patrick Cheridito;Damir Filipović;Marc Yor

  • Deep Splitting Method for Parabolic PDEs

    Christian Beck;Sebastian Becker;Patrick Cheridito;Arnulf Jentzen

  • Coherent and convex monetary risk measures for unbounded càdlàg processes

    Patrick Cheridito;Freddy Delbaen;Michael Kupper

  • Stochastic integral of divergence type with respect to fractional Brownian motion with Hurst parameter H∈(0,12)

    Patrick Cheridito;David Nualart

  • Deep Optimal Stopping

    Sebastian Becker;Patrick Cheridito;Arnulf Jentzen

  • Regularizing fractional Brownian motion with a view towards stock price modelling

    Patrick Cheridito

  • Measuring and Allocating Systemic Risk

    Markus K. Brunnermeier;Patrick Cheridito

  • Solving high-dimensional optimal stopping problems using deep learning

    Sebastian Becker;Patrick Cheridito;Arnulf Jentzen;Timo Welti

  • Time-inconsistency of VaR and time-consistent alternatives

    Patrick Cheridito;Mitja Stadje

  • The multi-dimensional super-replication problem under gamma constraints

    Patrick Cheridito;H. Mete Soner;Nizar Touzi

  • Dual characterization of properties of risk measures on Orlicz hearts

    Patrick Cheridito;Tianhui Li

  • Optimal consumption and investment in incomplete markets with general constraints

    Patrick Cheridito;Ying Hu

  • Duality formulas for robust pricing and hedging in discrete time

    Patrick Cheridito;Michael Kupper;Ludovic Tangpi

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

For students studying Mathematics in the USA, exploring related online degrees can broaden career opportunities. Many graduates consider pursuing an one year mba programs to quickly gain business acumen alongside their technical skills. These accelerated options allow professionals to enter the workforce or advance their careers in a short time.

Flexibility is key for many students, and seeking an online mba with transfer credits accepted can make continuing education more affordable and manageable, especially for those balancing work and study. Transfer credits can reduce the time and cost required to complete a degree.

Additionally, a specialized focus on data is increasingly valuable. Graduates may consider enrolling in top analytics masters programs to enhance skills in data interpretation and decision-making, which are highly sought after in various industries.

For those beginning their journey, exploring the easiest mba programs can offer accessible entry points into advanced business education without compromising career advancement.

Trending Scientists

Recently Published Articles