He started by hacking video games. Now he teaches data science at a university

People often ask me that. I spent twelve years in the army, working as a team leader in radio and satellite communications. My main responsibilities were technical maintenance of equipment and training colleagues. It was a long but formative period. I joined at eighteen and learned a lot about self-organization, structured thinking, and working with people. I also learned how to teach technical topics to people who aren’t necessarily interested in learning them.

After some time, though, it felt like that chapter was closed. I didn’t see a real future beyond doing the same thing for another twenty years. And I didn’t want a role that, in extreme situations, might require me to shoot at other people.

I had two options. Either stay in the army until retirement or leave after twelve years and return to civilian life. I chose the latter. I went back to what I had skipped after high school. I enrolled at university and studied computer science.

The transition was surprisingly easy. The hardest part was sitting in class with people half my age. At the same time, I had a different kind of motivation. I studied because I genuinely wanted to. And I had never completely lost touch with civilian life.

Together with a colleague, we run courses for medical fields, social sciences, and public administration. Overall, the feedback is very positive, but it wasn’t always like that. In the beginning, I made the mistake of including topics that were too complex. Things that interested me but overwhelmed participants from non-technical backgrounds. Over time, we adjusted the courses, and I think we’re now on the right track.

The goal is not to turn doctors into programmers, but to give them a basic understanding. So they can grasp key concepts, communicate with data scientists, and continue learning. It helps when they can try things out in practice. Then it becomes clear that programming, AI, or data science can actually be useful in their work.

A very important area is natural language processing (NLP). It enables the analysis of all types of text, working with large volumes of data, and interpreting and comparing documents.

That’s a good question. I was originally interested in computer games. Not just playing them, but understanding how they work. I started experimenting with code, sometimes just to give myself more gold in a game. I wanted to “bend the rules” and modify the program. That was my initial motivation. Over time, my interest in computers grew, and in the army I completed a two-year apprenticeship as an IT systems technician and an additional training as an IT administrator.

I think what helps is showing them the underlying principles - how devices, software, or recommendation algorithms like those on TikTok actually work. Once they understand how it can benefit them and try creating something useful themselves, solving an interesting problem or automating routine tasks, it starts to make sense.

At a younger age, it also depends a lot on family and school opportunities. In some German high schools, there are special projects contributing to the final grade where students spend several months working on their own research question, usually in STEM fields. They collaborate with researchers, sometimes even with us at our institute.

I didn’t have a specific role model. My parents were supportive, but not technically oriented. It was more coincidence and personal interest, partly influenced by friends.

Curiosity is the most important factor. Also the willingness to learn, problem-solving skills, and the satisfaction of achieving results. I think it’s crucial to experience that moment when something works. I remember my computer science classes at school. They were quite boring compared to what I was already doing on my own. It was just: this is HTML, this is a browser, this is Microsoft Word. That’s not very engaging. It needs to be framed as interesting challenges.

Honestly, it was more of a coincidence. My colleague Robert Haase, who works with EOSC and the Open Science Network, couldn’t attend in June, so he asked me. We usually teach together. I agreed quickly because teaching is a part of my job that I really enjoy, and spending two days contributing to the EOSC Summer School is a great opportunity.

The plan is to run two sessions. One on Tuesday, June 16, and another on Wednesday. Both will focus on JupyterLab, an interactive tool widely used in data science for working with data and code.

In the first session, I want to present JupyterLab as an environment for data stewards, where their tasks can be supported by an AI assistant. Not just as an add-on, but as a genuinely useful and productive tool.

The second session will go deeper into Python libraries and tools that can be used in JupyterLab for data stewardship tasks, such as publishing data and documentation to repositories or validating schemas for incoming data.

I’ve lived here for almost twenty years. What I like about Leipzig is that it doesn’t feel like an anonymous big city. You can get everywhere by bike. It has a vibrant subcultural scene, plenty of bars, restaurants, and smaller clubs. At the same time, nature is close by. There are lots of parks and forests within the city, as well as a large lake district surrounding it.