I am currently working in computational cosmology, with a particular focus on machine learning applications for large-scale structure simulations and cosmological emulation. My research investigates how deep learning methods can be used to generate high-fidelity synthetic universes from computationally inexpensive simulations, enabling efficient modeling of nonlinear structure formation across varying cosmologies. I work extensively with N-body simulations, cosmological statistics, and generative machine learning models, with applications to mock galaxy catalogs and cosmological lightcones for next-generation galaxy surveys.
My broader research interests include cosmological structure formation, dark matter phenomenology, numerical simulations, and scientific machine learning. In particular, I am interested in combining physically motivated simulations with modern deep learning techniques to address computational challenges in cosmology.
I am originally from Norway and have a background in physics and astrophysics. My work combines numerical methods, high-performance computing, and machine learning to study the evolution of structure in the universe across a wide range of scales. I am particularly interested in developing computational tools that bridge the gap between accuracy and efficiency in modern cosmological simulations.
Academic interests
- Gravity
- Machine learning for astrophysics
- Computational cosmology
- Large-scale structure formation
- Dark matter phenomenology
Background
| Time | Degree | Place |
| 2023 - present | PhD in Astrophysics | UiO |
| 2021 - 2023 | MSc in Astrophysics Thesis: Emulating the non-linear matter power spectrum in mixed axion dark matter models | UiO |
| 2018 - 2021 | BSc in Physics and Astronomy | UiO |