Education
Short description of my courses and available thesis projects.
Thesis project topics
These thesis topics are broadly defined available every year, and are embedded in ongoing research projects. Note: these are broad definition, please contact me via email for specifics.
- Computational design optimization of medical implants and implanting procedures (Python, VTK, FEM).
- Developping new soft-material simulations for biomedical applications, e.g. virtual surgeries (Python, GPU).
- Simulating the cellular mechanics of blood flow in diseases (thrombosis, diabetes, malaria), based on HemoCell (HPC, CFD, C++).
- Extreme-accuracy fluid simulations on supercomputers (HPC, lattice Boltzmann, C++).
- Energy efficient computing for Digital Twins (HPC, C++, Python).
- Quantum gravity simulations, large-scale Monte-Carlo simulations on complex graphs (Python, Causal Dynamical Triangulation, Quantum Ising model).
If you are interested in any of these topics, please contact me in email for more details on the specific goals. You can also take a look at the Research page for some examples of similar projects.
Example theses
- Modeling Turbulence in Fontan Circulation with Extreme Resolution based on the Lattice Boltzmann Method
- Computational modelling of thrombotic risk in the Fontan circulation
Scientific Computing
Programme: Computational Science Master
Goals
The course focuses on developing numerical algorithms to solve prototypical partial differential equations. Students will learn how to discretize differential equations using finite difference approximations, analyze the stability and accuracy of finite difference schemes, and implement these schemes in code to solve a variety of scientific and engineering problems. Topics covered include:
- Derivation of finite difference formulas for various derivatives
- Explicit and implicit finite difference methods for ordinary and partial differential equations
- Stability analysis techniques, such as the Courant–Friedrichs–Lewy (CFL) condition
- Accuracy and convergence of finite difference schemes
- Applications to problems such as heat transfer, fluid flow, and wave propagation
Furthermore, the course provides a brief introduction to advanced numerical methods (finite volume, finite element, and lattice Boltzmann method).
Stochastic simulations
Programme: Computational Science Master
Goals
The course introduces the foundations of the most important stochastic methods in computer simulations. These methods are applicable in a wide range of context that spans from modelling the behavior of network systems to financial systems, to in-silico clinical trials. The main objectives are:
- Understand the foundations of probability theory and how it is applicable to various stochastic processes.
- To be able to construct and evaluate stochastic models to simulate various real-world systems from finance to biomedicine.
- To be able to analyze and test the validity of such models.
- To be able to interpret correctly the predictions of these stochastic models.
Project Computational Science
Programme: Informatics Bachelor
Goals
The purpose of this intensive, hands-on course is to successfully complete a computational science project in a small team. The final deliverable is a report, accompanied by a functional software which simulates a real-world phenomenon and performs statistical analysis. It is specifically required for any project to have both a modeling & simulation component as well as a statistical data analysis component. In the previous block in the Minor Computational Science you completed smaller assignments in only one of these two components. In this course you will perform a larger project which combines the two.
Posters from previous years