About

Recently graduated with a Master’s in Complex Adaptive Systems and a Bachelor’s in Software Engineering. Analytical and creative, with a strong curiosity and passion for problem-solving. Skilled at communicating complex ideas clearly, both in teaching and collaborative projects.

Education

Chalmers University of Technology:

• Master of science in Engineering, Complex Adaptive Systems. Aug. 2023–Jun. 2025.
• Bachelor of Engineering, Software engineering. Aug. 2020-Jun. 2023.
• Engineering preparatory year. Aug. 2019-Jun. 2020.

Culinary and Beverage Education

• Sommelier Certification – Kulinarisk Akademi, Oslo, 2012
• Chef Apprenticeship & Certification – Completed formal apprenticeship and passed professional chef exam at Michelin-starred Haga Restaurant, Oslo, 2009-2011

Experience

Teaching Assistant - Chalmers University of Technology:

• Data Structures and Algorithms. Oct. 2022-Jan. 2023, and Oct. 2024-Jan. 2025.
  Supervising students with their coursework, including help with data structures, Java, Python, and Git. Grading coursework and exams.
• Mathematical Modelling. Sep. 2024-Oct. 2024.
  Supervising students using the Socratic method to allow them to transform real world problems into solvable mathematical models.

Board - BRF Vindrosen:

• Chair of Board, Nov. 2019-Dec. 2021.
• Vice Chair of Board, Nov. 2018-Nov. 2019.

During my time on the board of BRF Vindrosen we did important renovations, including the heating system, parts of the ventilation system, waste management and started renovations of the building façade. Additionally, we digitized parts of the operation, including a website to improve interactions with the residents and a portal to streamline daily operations.

Culinary Experience – Various Restaurants

• Worked as a Chef and Sous Chef, managing daily kitchen operations, food preparation, and staff supervision.
• Mentored apprentices, ensuring high standards of hygiene and culinary skills.
• Planned menus, coordinated inventory, and maintained efficient workflows in fast-paced environments.

Projects

Sequential Graph-Based Decoding of the Surface Code using a Hybrid Graph and Recurrent Neural Network Model

Thesis | Code

Developed hybrid graph-recurrent neural network for surface code decoding based on earlier work on graph neural networks. Showed that a hybrid graph-recurrent network can generalize to long time series of surface level noise and outperform the classical minimum-weight perfect matching algorithm.

Bachelor’s thesis in Mechanics and maritime sciences

Thesis

'’Utveckling av turbulensmodeller med hjälp av maskininlärning i Python’’, in English ‘‘Development of turbulence models using machine learning in Python’’.

Skills

Programming languages and technical tools:

Python, Java, Matlab, C, C#, Haskell, Git, LaTeX, Linux, Unix Shell, PyTorch, Docker.

Experience modeling problems mathematically and implementing models in code or with other technical tools.

Communication and cooperation

Skilled at communicating technical ideas clearly, both orally and in writing, in team work, tutoring, and teaching.

Fluent in Norwegian, Swedish and English