Research
“If people do not believe that mathematics is simple, it is only because they do not realize how complicated life is.”
— John von Neumann
Research Interests
My research interests lie at the intersection of numerical methods and applied probability, with applications in finance, such as stochastic control, (F)BSDEs, stochastic modeling in financial markets and risk management, and scientific machine learning.
Publications
Zhipeng Huang, and Cornelis W. Oosterlee. Convergence of the Markovian iteration for coupled FBSDEs via a differentiation approach. arXiv preprint arXiv:2504.02814 (2025)
Negyesi, Balint, Zhipeng Huang, and Cornelis W. Oosterlee. Generalized convergence of the deep BSDE method: a step towards fully-coupled FBSDEs and applications in stochastic control. arXiv preprint arXiv:2403.18552v2 (2025) arXiv preprint arXiv:2403.18552v2 (2025)
Zhipeng Huang, Balint Negyesi, and Cornelis W. Oosterlee. Convergence of the deep BSDE method for stochastic control problems formulated through the stochastic maximum principle. Mathematics and Computers in Simulation 227 (2025): 553-568. https://doi.org/10.1016/j.matcom.2024.08.002
Michael CH Choi, and Zhipeng Huang. Generalized Markov chain tree theorem and Kemeny’s constant for a class of non-Markovian matrices. Statistics & Probability Letters 193 (2023): 109739. https://doi.org/10.1016/j.spl.2022.109739
Supervisions at Utrecht University
BSc Thesis: Henrik van de Langemheen, Feb - Jul, 2025
Topic: The COS method for computing the first hitting time of polynomial processesMSc Seminar Project: Jochem Lange and Martijn Brouwer, Feb - Jun, 2024
Topic: Uncovering the underlying dynamics from data using machine learning techniques