My interest in maths started at a kitchen table in 4th grade, when my dad and I sat down and worked through two equations with two unknowns together. Something clicked. That curiosity led me to DTU, where I picked Electrical Engineering deliberately — a good mix of theory and practice, and the broadest path into the master's tracks I cared about: autonomous systems, machine learning, software engineering. An intro course in ML sealed it. The idea that you could point a system at data and have it surface patterns no human would spot still fascinates me. I ended up in the honors programme, which gave me the freedom to overload on ML courses and run special projects with my advisor.

After DTU, I joined a B2B software company's innovation team as the only person with ML experience. By week two I was already out on my first client project. I later built solutions end-to-end on my own — from training pipelines to deployment — that are in production today, including at a Fortune 500 company. I moved to my current role because I wanted to follow projects from strategy through to operations; by day three, I was already on my first client engagement.

What originally pulled me into ML was the idea of solving problems you otherwise couldn't solve — not automating the obvious, but unlocking something new. My strongest area is building ML end-to-end: training pipelines, data pipelines, inference, and cloud deployment. The projects that really light me up are the ones where the problem itself is the hard part.

Outside client work, I chair the board of IDA AI, the AI community within Denmark's largest engineering association, where I help shape its conferences and technical programme.