Research integrates mathematical (stochastic) modeling, numerical analysis, and advanced computational methods to address complex problems in engineering and science. Focuses on developing novel strategies such as smoothing techniques, dimensionality and variance reduction, improved sampling (hierarchical/adaptive/importance sampling), and machine learning. His work is application-driven, focusing on numerical and machine learning methods in quantitative finance, optimal control and reinforcement learning for power systems management and trading in energy markets, forward and inverse problems in stochastic reaction networks, and data-driven methods for forecasting extreme events.
Research Experience
Currently an Assistant Professor at the Mathematical Institute, Utrecht University; served as a Postdoc at RWTH Aachen University, Germany, from 2020 to 2023.
Education
PhD in Applied Mathematics and Computational Science from KAUST in 2020.
Background
Areas of expertise include Quantitative Research Methods, Mathematical Modelling, Financial Management, Computational Modelling, Stochastic Modelling, and Numerical Modelling. Specializes in Applied Data Science, Foundations of Complex Systems, and Energy in Transition.