A few things about why I build, what I’m learning, and how I work.
I started Cell Engine to use AI and simulation to understand how cells behave. I’m interested in cancer, how cells respond to bacteria and viruses, and how they interact in multicellular environments. Eventually, I’d like to explore how generative AI could help propose possible solutions to investigate through simulation and research. Cell Engine is an early step toward those questions, and I’m still learning the biology needed to model them well.
My thesis at DFKI was my first professional research experience. Designing the DiversityLens pipeline, working with a GPU cluster for the first time, and dealing with technical issues remotely pushed me to become more disciplined and consistent. I learned to investigate problems more carefully and understand how the parts of a system fit together. That experience made me more deliberate about how I plan, build, and troubleshoot my work.
When I notice a problem, I find it hard to just leave it there. My instinct is to investigate, learn what I’m missing, and try to make progress. Cell Engine comes from that impulse: understand a small part of a difficult problem, then see whether I can contribute something useful. I’m comfortable starting with a lot to learn, and I tend to keep following a question once it has caught my attention.