Dr Nadhir Ben Rached

Profile

I received the Diplôme d’Ingénieur degree from École Polytechnique de Tunisie (EPT), La Marsa, Tunisia, in 2012, and the Master's degree and the Ph.D. degree in applied mathematics and computational science from King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia, in 2013 and 2018, respectively.

In January 2019, I was a Postdoctoral Researcher at the Alexander von Humboldt Chair of Mathematics for Uncertainty Quantification at RWTH Aachen University, Aachen, Germany.

In October 2022, I was appointed Lecturer in Statistics at the School of Mathematics, Faculty of Engineering and Physical Sciences. From October 2022 to October 2024, I was 50% seconded to work with the UK Met Office to influence and deliver high-impact research in the area of weather and climate science and services. This unique role aimed to provide new links between the Met Office and the School of Mathematics.

I am Associate Editor for Statistics and Computing

Research interests

My research focuses on stochastic mathematical modelling and stochastic numerics to address complex real-world problems. These challenges often stem from high dimensionality, rare events, and low regularity, all of which contribute to significant computational costs in numerical simulations. My overarching goal is to design computationally efficient frameworks that balance accuracy and computational effort, enabling the practical and rigorous solution of such problems.

A central focus of my work involves developing and analysing advanced Monte Carlo methods and variance reduction techniques. I have gained significant expertise in hierarchical Monte Carlo approaches, such as Multilevel Monte Carlo (MLMC) and Multi-Index Monte Carlo (MIMC). I am particularly interested in the connection between importance sampling and stochastic optimal control (SOC) for efficient rare events simulation, especially for systems governed by stochastic differential equations (SDEs), McKean–Vlasov SDEs, and stochastic reaction networks (pure jump processes).

My research is application-driven, with ongoing projects and interests in computational finance, wireless communications, biological and chemical systems, supply chain modelling and simulation, and power procurement in cellular wireless networks. I have a strong interest in using stochastic optimal control to tackle practical decision-making problems such as:

• Optimal inventory management for supply chains, where uncertainty in demand and lead times must be efficiently handled.

• Optimised power procurement strategies for wireless networks powered by uncertain renewable energy sources, where balancing cost and reliability under uncertainty is essential.

In these areas, I focus on both the formulation of suitable SOC models and the development of numerical schemes capable of solving them efficiently in high-dimensional settings or when the problems deviate from classical control structures.

Ultimately, my research aims to advance the development of scalable, robust algorithms for the simulation, optimisation, and control of stochastic systems with significant real-world impact.

Qualifications

• PhD in Applied Mathematics and Computational Science, KAUST
• MSc in Applied Mathematics and Computational Science, KAUST
• BSc in Polytechnic Engineering, Ecole Polytechnique de Tunisie

Professional memberships

• Fellow of The Higher Education Academy
• Society for Industrial and Applied Mathematics

Research groups and institutes

• Modern Applied Statistics
• Statistics
• Probability and Financial Mathematics
• Statistical Methodology and Probability

Current postgraduate researchers

• Mendrika Rakotomanga

Projects

<li><a href="//phd.leeds.ac.uk/project/2006-modelling-and-stochastic-simulation-of-supply-chains">Modelling and Stochastic Simulation of Supply Chains</a></li> <li><a href="//phd.leeds.ac.uk/project/1509-variance-reduction-techniques-for-rare-events-simulations">Variance Reduction Techniques for Rare Events Simulations</a></li>