NHH and finance
The academic story is broader than finance alone. I completed both a Bachelor of Science in
Economics and Business Administration and a Master in Economics and Business Administration at NHH,
with Financial Economics as the main profile in the master's degree. The strongest signal inside that
path is the thesis: an A-graded empirical project on European corporate divestitures using large
transaction datasets and firm-level financial data.
The most useful thing I took from the thesis was learning how to gather, work with, and structure
large amounts of data, use that material for meaningful analysis, and interpret the results
critically rather than taking them at face value.
UC Berkeley
The Berkeley semester added international context, a new academic environment, and a broader frame
of reference while still fitting into the same pattern: difficult material, high pace, and a need
to adapt quickly.
What mattered most was getting experience from a different academic setting and having to adapt
quickly to it.
Football
The football background is easiest to understand in concrete terms. It starts in Viking's academy from age 12
to 18, including a national league title and an NM final with the G16 team,
before later senior football across Viking 2, Start 2, Åsane, Sotra, and Vidar.
It also includes a major hip surgery in 2022, no match return until 2024, and then a final season
in 2025 that ended with promotion. What matters about that history is not status language. It is
years spent in competitive environments with routines, feedback, setbacks, rehabilitation, and the
need to keep performing over time.
Modern tools and workflows
I spend a lot of time learning as much as I can about AI. I follow new models and tools closely, test workflows,
and try to understand where they actually create leverage in research-heavy work.
Berkeley also widened that interest by putting me around people who were closer to AI engineering and
more technical AI work. The part that matters most to me, though, is responsible use. If information
is sensitive, I think carefully about what should stay local, what can be abstracted, and how to use
code and AI without being careless with data.
That means I am interested not only in what AI can do, but also in how to build workflows that are
actually usable inside real companies: faster first passes, better structure, and clear limits around
confidentiality and judgment.
