Hey, I’m Gordon, an AI developer from Germany, with a strong coffee habit, and a passion for solving real-world problems using computer vision, machine learning, and deep learning.
What I Do
- 🧠 AI & Deep Learning – I develop robust models for object detection, segmentation, and visual analysis.
- 🔍 Computer Vision – I tackle tough computer vision problems in domains like robotics, underwater vision and ecological informatics.
- 🧰 MLOps & Deployment – I build clean, reproducible, and GPU-ready pipelines using Docker, Python, PyTorch, and MLflow..
- ✍️ Technical Writing – I break down complex models (like SSD or YOLO) into understandable code walkthroughs and blog posts.
Why This Site?
This site is my digital workbench, a place to:
- Share practical solutions to real computer vision problems.
- Publish code, tools, and experiments I wish I had found earlier.
- Document my projects, side quests, and lessons learned.
Bio
I was born in eastern Germany, Saxony-Anhalt, in 1985, the year in which Marty McFly and Emmet Brown started their journey Back to the Future. I grew up in a small village with about 2000 people living in it, nestled between hills, forrests and fields.
My journey into tech started early with a C64, and that fascination with computers and technology continues to this day. Like most young people in my area during the 90s, I spent my time watching TV (way too much), experiencing all the shows you’d remember from that era, getting my first real PC, taking it apart, upgrading it as best I could, and playing video games (extensively). I was captivated by the internet, although it took some time since we got more than 56k, spending countless night hours online and participating in LAN parties. But I also started learning guitar and discovered my love for making music, a passion that remains with me today.
In 2005, I started my IT studies at the University of Applied Sciences in Kiel, and graduated with honors with a Master’s in IT in 2012.
After finishing my studies, I spent about 4 years working full-time as a researcher and laboratory engineer at the University of Applied Sciences in Kiel, diving deep into underwater computer vision and marine organism detection. My work resulted in several publications on the topic and taught me valuable skills in scientific research and in teaching students in the field of programming (C, C++, Python).
Learning, teaching, and working at the university first taught me the foundations of computer vision and machine learning. I experienced firsthand the shift from those traditional methods to the deep learning approaches we see today. Because of this, I started to experiment and try out those new methods from the very beginnings of this new age of AI. I started implementing and refactoring countless lines of C++ and later switched to Python using all the popular libraries known to the community.
I set up complete training and evaluation pipelines, configured and administered several GPU servers and workstations, and handled data curation, annotation, and data analysis. I trained all kinds of detection and segmentation models and implemented tracking algorithms. I was supervising teams of students in their project work and thesis projects in our research group for machine learning and pattern recognition. I participated in writing several research project proposals to acquire funding, wrote technical and project reports, and gave talks for our stakeholders.
Until now this was one of the most influential times for me and led me to pursue a career in the field of computer vision and machine learning. I met many bright people from all kinds of nations and really enjoyed the mix of cultures, making several friends I hold dear to this day.
After university, I transitioned to bbe Moldaenke where I spent three years developing specialized software for scientific instrumentation. I built C++ GUI applications for microscope-based digital holography systems to detect and quantify algae in water samples, created analysis tools for Raman spectroscopy measurements to identify microplastic particles, and reimplemented legacy C applications into modern Python GUI interfaces for configuring measuring instruments. This industrial R&D experience bridged my academic background with practical software development skills.
After my time in industry, I returned to university work for another 4 years at my former alma mater, continuing my previous projects in ecological informatics. During this period, I focused on developing Python software for the automatic detection and size determination of underwater organisms. I conducted scientific investigations of various methods for detection, segmentation, and tracking of objects, which resulted in several publications at scientific conferences and in professional journals. I again set up and administered multiple GPU servers for conducting machine learning experiments, further expanding my technical infrastructure expertise.
Since 2024, I am working at IBAK, a leading manufacturer of sewer inspection and rehabilitation systems with over 75 years of industry expertise, where I develop AI solutions for camera-based sewer inspection systems. I am part of a fresh AI team with great people all around. I’ve learned many new skills that I didn’t touch before, especially in the fields of CI/CD and production systems. Nonetheless, I can bring my previous experience in developing and evaluating computer vision and deep learning models. It’s a great position where I can make industrial-focused research work, which is very rare to have, and I’m glad I have the opportunity.
Got a question, idea, or project in mind?
Feel free to reach out using one of my socials below.