Head of Machine Learning at Beckhoff Automation
I work on low‑latency real‑time machine learning and A.I. techniques, applied to robotics, motion control and industrial automation settings. Otherwise, I am very interested in science, economics and all‑things‑tech.
Experience
I joined Beckhoff Automation in 2017, where I am heading a team of applied scientists and software engineers as well as leading the design and development of the TwinCAT Machine Learning framework that allows to utilize artificial intelligence in real-time systems and motion control. The company is build around a unified software-based approach to automation, using regular PCs instead of custom embedded hardware. Before that I managed the software-focused research projects of the DMG MORI group, including the projects in collaboration with the “it's OWL” research cluster.
Academia
My formal research work in the area of string theory was focused on F-theory phenomenology and related mathematical questions, ultimately trying to unify gravity and particle physics. After completing a PhD at the Max Planck Institute for Physics in Munich, I continued as an NSF-funded post-doc at the Northeastern University in Boston, USA.
More
Computers, electronics and fundamental science have been at the center of my interest for as long as I can remember. Since leaving academia, I have been very interested in AI/ML research (deep learning and neural networks) as well as blockchain technology. When I find the time, I also try to keep this website updated…