Chiheb Ben Hammouda

Lehrstuhl für Mathematics for Uncertainty Quantification


Building: 1953

Room: 160

Pontdriesch 14-16

52062 Aachen


Phone: +49 177 51 177 82


Postdoctoral Research Scientist at RWTH Aachen University, Ph.D. in Applied Mathematics and Computational Science, and Graduate Engineer. My research expertise is a mixture of mathematical (stochastic) modeling, numerical analysis, and the design and implementation of computational simulation methods.


Research Areas

  • Quantitative Finance
  • Computational Chemistry/Biology
  • Optimal Control for Renewable Energy
  • Uncertainty Quantification
  • Machine Learning



  • Bayer, C., Hammouda, C.B., and Tempone, R. “Hierarchical adaptive sparse grids and quasi-Monte Carlo for option pricing under the rough Bergomi model”. Quantitative Finance (2020): 20:9, 1457-1473.

  • Hammouda, C.B., Rached, N.B., and Tempone, R. “Importance sampling for a robust and efficient Multilevel Monte Carlo estimator for stochastic reaction networks”. Statistics and Computing (2020).

  • Hammouda, C.B., Moraes, A., and Tempone, R. “Multilevel hybrid split-step implicit tau-leap”, Numerical Algorithms (2017), 74(2):527-560.


  • Bayer, C., Hammouda, C.B., Papapantoleon, A., Samet, M., and Tempone, R (2022). “Optimal Damping with Hierarchical Adaptive Quadrature for Efficient Fourier Pricing of Multi-Asset Options in Lévy Models”. ArXiv preprint arXiv:2203.08196.

  • Bayer, C., Hammouda, C.B., and Tempone, R. (2021) "Numerical Smoothing with Hierarchical Adaptive Sparse Grids and Quasi-Monte Carlo Methods for Efficient Option Pricing”. ArXiv preprint arXiv:2111.01874.

  • Hammouda, C.B., Rached, N.B., Wiechert, S., and Tempone, R. (2021) “Optimal Importance Sampling via Stochastic Optimal Control for Stochastic Reaction Networks.” ArXiv preprint arXiv:2110.14335.

  • Bayer, C., Hammouda, C.B., and Tempone, R. (2020) "Numerical smoothing and hierarchical approximations for efficient option pricing and density estimation”. ArXiv preprint arXiv:2003.05708.

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Co-Instructor of the following courses at RWTH Aachen University:

  • Numerical Methods for Stochastic Differential Equations with Connections to Machine Learning.
  • Stochastic Numerics with Applications in Simulation and Data Science.
  • Numerical methods for Random Partial Differential Equations: Hierarchical Approximation and Machine Learning Approaches.