MSc student in Applied Mathematics at the University of Oslo, interested in scientific computing, machine learning and numerical methods for PDEs.
My current academic interests focus on numerical methods for hyperbolic conservation laws, with a particular interest in combining classical numerical methods with deep learning.
- Python & PyTorch
- Machine Learning & Deep Learning
- Numerical PDEs
- Computational Fluid Dynamics
- Natural Language Processing
- Scientific Computing
Numerical experiments for hyperbolic conservation laws using Python and PyTorch, including finite-volume methods, Riemann problems, stability analysis and shock capturing.
A 1D Euler-equation solver with Sod shock-tube experiments, exact Riemann-solution comparison and advection tests.
Coursework and experiments in artificial intelligence and machine learning, including optimization, search methods, evolutionary algorithms and machine-learning techniques.
Machine-learning experiments covering polynomial regression, bias–variance analysis, ridge and lasso regression, cross-validation and gradient-based optimization.
Implementations and experiments in natural language processing, including tokenization, BPE, N-gram language models, Laplace smoothing, text classification and neural-network methods.
Machine Learning · Deep Learning · NLP · High-Performance Computing