Technology always attracted me, from software to hardware. In my research career, I’ve had the privilege to develop for some complex systems in critical environments, like:

• Processing and analyzing 160 GB of data per second from particle accelerator collisions for real-time monitoring,

• Building custom end-to-end pipelines to analyze big 3D ultramicroscopy images with high performance cluster computing,

• Designing and programming maximally parallel code to simulate molecular level NMR (nuclear magnetic resonance) for large-scale distributed MRI simulations,

• Planning and developing Machine Learning pipelines (from data preparation to training, evaluation and inference) for AI-support in medical diagnoses,

• Implementing Deep Learning algorithms for next-generation MRI scanners to accelerate and enhance imaging with AI,

• Rethinking, augmenting and automating how MRI scanners can be operated data-driven, using Generative AI and graph algorithms to improve clinical workflow,

• Innovating signal reception hardware for medical imaging with cutting-edge quantum sensor technology.

Along the way, I got to witness some impressive engineering works of art. A highlight for me was being 100 m underground, inside the ATLAS and CMS particle detectors of the Large Hadron Collider at CERN, during shutdown for upgrades.

*Don’t worry, the other sections are shorter.

With a passion for analytics and math, I enjoy revealing hidden insights and generating grounded decision support from real data.

Mathematical University lectures on stochastics and statistics taught me the foundation and over half a decade of biomedical research has built up experience. Structured or unstructured numerical, text, image, audio, or sensor signal data, as well as high-dimensional data analysis have all been among my research tasks.

In this age of digital transformation, IoT emergence, and sensor omnipresence, the endless possibilities for data sourcing are pretty exciting. What a time to be alive, right?

In 2017, I started using machine learning with classic methods of more explainable AI, like:

• Support Vector Machines, Decision Trees, and shallow Artificial Neural Networks for classification tasks on temporal signals and tabular, numeric and categorical data,

• Deep Learning and Convolutional Neural Networks for classification and feature extraction on medical images using Computer Vision.

In January 2023, I got into Generative AI and began researching and building experience in:

• Transformer architectures and multimodal models for text, image and temporal signals,

• Large Language Models and Diffusion Models, API-based and local, private use,

• Open-source models, platforms and libraries, Fine-tuning and cloud hosting,

• Retrieval Augmented Generation (RAG), knowledge embedding, vector databases,

• Advanced knowledge representations, knowledge graphs, higher-order retrieval.

I see a lot of potential in combining Gen-AI with software-based automation to link normally disconnected systems for workforce support, like collecting data from different sources, analyzing events, and preparing or suggesting actions (see “What I Do“ for details).

Studying Physics has shown me what the mind is capable of. I am amazed by what has been achieved by observing nature and describing it mathematically with fitting formalisms.

From Lagrangian, Hamiltonian and Quantum Mechanics, Special and General Relativity to the Standard Model of particle physics and Quantum Field Theory, it has been such a captivating journey, which has taught me that unimaginable insights and predictability can be gained from suitable formulations to describe a problem, sometimes even just very abstractly.

In that light, I like to see AI architectures as a form of modeling abstraction.

With broad interests and a passion for understanding, I have always loved learning and trying new things, and increasingly so with age.

Gladly autodidactic, I enjoy deciphering new concepts and grasping paradigms. Working in multidisciplinary fields with diverse people and backgrounds has always turned out to be extremely interesting and rewarding.

Experience has shown me that bringing methods and ways of thinking from other fields into new areas can unexpectedly yield creative, effective solutions to different problems.

I lecture about advanced AI concepts and technical implementations at a progressive German state-certified online school to prepare professionals for future job market demands.

The following certifications enable me to offer professional consulting services:

• Information Security Foundation based on ISO/IEC 27001 by EXIN,

• DevOps Foundation Certification by the DevOps Institute,

• Generative AI Professional Certification by CertiProf,

• AI Consultant Certification by A-Leecon GmbH.

Scroll down to find out more on What I Do…

My family grounds me with the sweetest joys in life, especially my son. Having always loved nature, it’s a whole new experience to rediscover everything together.

We enjoy walking our dog, always ready to get excited about random findings. This reminds me that it really is about the simple things in life and I am very grateful for that.