Summary
Chiheb Hammouda is an Assistant Professor at Utrecht University and an applied mathematician with a decade of experience developing efficient numerical and machine learning methods for high-dimensional, low-regularity, and rare-event problems. He holds a PhD from KAUST and previously led research on uncertainty quantification and advanced Monte Carlo techniques as a postdoc at RWTH Aachen. His work spans theory, algorithm design, and applications in quantitative finance, renewable energy control and trading, stochastic reaction networks, and forecasting extreme events. He focuses on practical performance gains via smoothing, hierarchical approximations, variance reduction, importance sampling and adaptive sampling, often combining classical numerical analysis with modern ML. Notably, his PhD produced award-winning contributions on hierarchical adaptive sparse grids and robust multilevel Monte Carlo estimators that bridge finance and computational biology. Colleagues value his ability to translate rigorous analysis into scalable algorithms for real-world, computation-heavy problems.
10 years of coding experience
3 years of employment as a software developer
Doctor of Philosophy - PhD, Applied Mathematics and Computational Science, Doctor of Philosophy - PhD, Applied Mathematics and Computational Science at KAUST (King Abdullah University of Science and Technology)
Engineer's degree, Economics and Scientific Management., Engineer's degree, Economics and Scientific Management. at Tunisia Polytechnic School
Master's Degree, Applied Math and Computer Science, Master's Degree, Applied Math and Computer Science at King Abdullah University of Science and Technology
English, French, Arabic, German