Exploiting structure for
reliable computation.

I am an applied mathematician and Assistant Professor at the Mathematical Institute of Utrecht University. My research spans theory, algorithm design, and numerical analysis, with a focus on structure-aware numerical and scientific-machine-learning methods for stochastic systems. These systems may involve high dimensionality, nonsmooth quantities of interest, rare events, partial observations, memory, or coupled dynamics, making reliable estimation, prediction, and decision-making particularly challenging. My overarching aim is to develop scalable methods that make such problems tractable while maintaining mathematical rigour and a principled balance between accuracy and computational cost.

Rather than simply increasing computational effort, I reformulate or represent problems so that numerical methods can exploit their analytic, probabilistic, or hierarchical structure. Depending on the problem, this may involve recovering integrand regularity through smoothing and Fourier representations; reducing complexity and effective dimension through model- and dimension-reduction techniques; reducing variance through importance sampling; or designing informed, adaptive, and hierarchical sampling and quadrature strategies guided by estimates of error, variance, and cost.

I use scientific machine learning both within structure-aware numerical methods—for example, to learn transformations, reduced representations, feature maps, or controls—and as a direct tool for approximating parameters, forecasts, value functions, or policies. Stochastic optimal control likewise plays two roles in my work: it helps design automated and efficient numerical methods, particularly path-dependent importance-sampling schemes, and provides a framework for computing policies for decision-making under uncertainty in energy systems and markets.

Across mathematical and computational finance, energy systems, markets and climate resilience, and stochastic reaction networks arising in biochemical and biological systems, I connect quantifiable accuracy, variance, stability, and complexity to reliable estimation, prediction, and decisions.

Alongside my research, I teach in bachelor’s and master’s programmes, including the national Mastermath programme, and supervise PhD candidates and master’s and bachelor’s students. I co-designed and now coordinate Utrecht University’s master’s track in Mathematical and Computational Finance and serve as an Associate Editor of Statistics and Computing.