About
A generalist working between models and matter.
What I do
I’m a mid‑career engineer based in Sweden with a Master’s in Cognitive Science and roughly a decade of professional work across academic neuroscience, energy‑market risk modelling, and embedded computer vision.
The thread connecting those very different rooms has always been statistical thinking: looking at residuals, watching distributions misbehave, and finding the assumption underneath that quietly breaks. I’m relatively quick at the diagnostic loop: see what a model is doing, guess why, propose a better fit, repeat.
The work I enjoy most starts with a question that hasn’t been answered yet (a sensor that drifts, a signal that hides a structure, or a pipeline held together by undocumented tribal knowledge) and ends with something a colleague or customer can actually use.
Core strengths
- Statistical and signal reasoning. Time series, image and signal processing (EEG / MEG / MRI and industrial vision), residual diagnostics, model refits.
- End‑to‑end problem solving. From vague idea to working system: protocol design, data collection, labelling, pipelines, automation.
- Bridging code and hardware. Pick‑and‑place machines, reflow ovens, smart cameras, e‑paper panels. I like seeing what my code is doing in the physical world.
- Translating complex systems. Maintainable documentation, clear explanations, and interfaces that survive the engineer who built them.
Tools & domains
My background is deliberately broad, but a handful of technical themes have stayed consistent across roles. In practice that means a mix of modelling, data work, and code that has to talk to physical things.
Technologies
How I work best
I have ADHD that was diagnosed late, a Non‑24 sleep tendency, and a higher‑than‑average sensory baseline. That triangle means energy management matters more to me than to most engineers, and I’ve made my peace with it.
In practice: I do my best work at 30–32 focused hours a week, on a mix of short and medium projects where progress is visible and closure is possible. I’m reliable under real deadlines and burn out fast in perpetual “everything is urgent” modes.
I aim for “good enough for reality” rather than abstract perfection, and I work best with a line of sight to the people who will actually use the thing I’m making.
Collaboration style
Direct, empathetic, low on office politics. I tend to connect easily with other neurodivergent people, and I prefer honest conversation over careful choreography.
I’ve been a team lead and stepped back voluntarily; coordinating others on top of a real technical load isn’t where I add the most value. I’m most useful as a deeply involved individual contributor in a small team.
What I’m drawn to outside work
A few long‑running interests shape what I read, build, and pay attention to. They show up in the projects and essays here in oblique ways.
Long‑running interests
- AI and cognition. Agent‑like systems, intuitive interfaces, and AI that supports human agency rather than replacing it.
- Hardware, repair, and reverse engineering. Small tools, e‑paper, batteries, firmware, and the joy of making old things work again.
- Art and storytelling. AI‑assisted surreal art, impossible spaces, the occasional short story, and slow nonfiction.
- Nature, flight, and resilience. Hang‑gliding, time outdoors, and a long interest in energy, resources, and how societies adapt to change.
What I’m looking for
I’m interested in roles and collaborations I can stay in for years, without losing sight of why the work matters.
Good fit if you need someone to
- Apply statistical and signal skills to messy real data.
- Work close to hardware, sensing, or physical products.
- Keep a line of sight to actual users or customers.
- Operate with enough autonomy and trust to do careful work.
- Contribute to something that won’t feel pointless in ten years.
If this resonates
I’d be happy to talk. The most reliable way is email; I read everything, even when I can’t answer right away.