About
From model to impact
Many Data Science and AI projects don't fail because of the models – they fail in implementation. That's exactly what I work on: from statistical foundations and model development through Machine Learning and AI to productive use in processes and decisions.
What matters to me is not the individual model, but its robustness, interpretability, and applicability in its specific context.
Skills
Substantive focus and methodological scope
Data preparation, exploratory analysis, statistical modeling, and inferential methods. Solid methodological foundations for reliable results.
Supervised and unsupervised learning, ensemble methods, deep learning. From model development through evaluation to production deployment.
Interpretable and explainable AI models. Methods for revealing model assumptions, limitations, and decision rationales.
AI-based methods designed to be explainable, reliable, and responsibly deployed. Assessment and design within specific contexts.
Translating models into products and operational systems. Scalability, data quality, and integration into existing processes.
Building data- and AI-driven analytical capabilities. Developing business models with a focus on sustainable impact.
Fields of Work
Three perspectives, one common focus
As a Professor of Data Science, I don't just teach methods – I enable reflective use of data-driven models. My research investigates how AI-based methods can be designed to be explainable and responsible.
In startups, I translate models into products and operational systems. This is where you learn which approaches scale, where data quality becomes a bottleneck, and how to integrate DS meaningfully.
In consulting projects, I support organizations in building data- and AI-driven analytical and decision capabilities, as well as in developing corresponding business models – with a focus on sustainable impact.
References
Companies and organizations I have worked with
Contact
Interested in collaborating? I look forward to your message.