Chiheb Ben Hammouda

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Graduate Engineer, applied mathematician, and computational scientist focused on quantitative finance, uncertainty quantification, computational chemistry/biology, and numerical analysis and simulation of stochastic processes. My research area lies in the interface of mathematical modeling, numerical analysis and computational simulation for
i) Developing new efficient numerical methods for pricing financial derivatives based on Monte Carlo, multilevel Monte Carlo, Quasi-Monte Carlo, sparse grids, Fourier, and deep learning methods.
ii) Developing new methods for uncertainty quantification in option pricing based on polynomial chaos expansion, and Monte Carlo methods.
iii) Developing new efficient simulation techniques for a reliable and robust estimation of various statistical quantities for stochastic biological and chemical systems. These methods are based on Monte Carlo, multilevel Monte Carlo, and importance sampling techniques.
The main programming languages/frameworks used in my research projects are C++, Python, Matlab, SQL, and TensorFlow.

Selected Publications
• Hammouda, C.B., Moraes, A., and Tempone, R. (2017). Multilevel hybrid split-step implicit tau-leap for stochastic reaction networks, Numerical Algorithms, 74(2):527-560.
• Bayer, C., Hammouda, C.B., and Tempone, R. (2018). Hierarchical adaptive sparse grids and quasi-Monte Carlo for option pricing under the rough Bergomi model. arXiv preprint: arXiv:1812.08533 (to appear in Quantitative Finance).
• Hammouda, C.B., Rached, N.B., and Tempone, R. (2019). Importance sampling for a robust and efficient Multilevel Monte Carlo estimator for stochastic reaction networks. arXiv preprint: arXiv:1911.06286.

• Bayer, Christian, Chiheb Ben Hammouda, and Raul Tempone. "Numerical smoothing and hierarchical approximations for efficient option pricing and density estimation." arXiv preprint arXiv:2003.05708 (2020).