Projects

My work sits at the intersection of mathematics, computing, and real-world systems. I develop numerical methods that turn complex physical and operational problems into efficient, scalable computations.

A central theme is coupling — combining different models, methods, or system components so each part is solved in the most effective way. This approach is particularly powerful in applications where standard methods become too slow, too expensive, or fail to scale.

Virus and Molecular Simulations

Biomolecular physics · Drug discovery · Computational chemistry

Accurately predicting how molecules interact is key to drug design, but computationally expensive.

I develop coupled numerical methods that significantly improve how these interactions are simulated, combining different techniques to increase both accuracy and efficiency.

Impact:

Faster molecular simulations
Reduced computational cost
Scalable pipelines for drug discovery

Impact:

Reduced operational costs for water utilities
More efficient resource allocation
Improved resilience of supply systems

Optimising Water Supply

Infrastructure · Sustainability · Operations optimisation

Water infrastructure systems are costly, intricate, and face ongoing demands for greater efficiency.

This project aims to optimise water distribution and cut operational costs across supply networks by integrating mathematical modelling with system-wide optimisation. The objective is to enhance efficiency throughout the networks while ensuring reliability and maintaining service quality.

Coupled FEM–BEM

Multi-physics simulation · Engineering software

Real-world systems rarely fit into a single model. They span domains, physics, and scales.

This project develops a flexible framework that allows different numerical methods to work together seamlessly, enabling complex systems to be solved more efficiently and modularly.

Fast Solvers for Isogeometric Analysis

CAD-integrated simulation · Manufacturing

Integrating simulation directly into design workflows is powerful, but computationally demanding.

This project introduces fast solvers that keep computation efficient even at high accuracy, making simulation-driven design more practical.

Impact:

More efficient multi-physics simulations
Modular, scalable solver architectures
Better performance for complex systems

Impact:

Faster design–simulation cycles
High-accuracy computation at lower cost
Better integration with CAD workflows

Parallel Solvers for Fluid Flow

Fluid dynamics · High-performance computing

Large-scale fluid simulations often hit computational limits.

I developed scalable parallel solvers for Stokes flow that exploit problem structure to significantly improve performance as systems grow.

Impact:

Faster large-scale simulations
Efficient use of parallel hardware
Reduced computational bottlenecks