Tabular Deep Learning
Most business data is tabular. I research how deep learning can be applied to it.
Prof. Dr. Christoph Weisser, MLitt (St Andrews), MSc (Oxford)
I build production-grade AI, publish peer-reviewed research, and advise a select group of organizations on turning artificial intelligence into measurable value.
I work at the intersection of rigorous research and real-world engineering. My focus is applied AI and Business Data Science with industrial impact — from tabular deep learning and natural language processing to agentic systems that connect frontier research with production.
As Technical Lead Analytics & AI at BASF, I led international AI initiatives from strategy through to production. I now hold a professorship in Business Data Science, contribute to popular open-source AI libraries, and publish peer-reviewed research — while advising a select group of organizations.
I completed the PhD Program Applied Statistics & Empirical Methods (summa cum laude) in Göttingen and studied at Oxford and St Andrews as a scholar of the Studienstiftung des deutschen Volkes.
My research turns frontier machine learning into methods that are practical, trustworthy, and usable in production. I work across deep learning for structured data, language, and the statistics of decision-making under uncertainty.
Most business data is tabular. I research how deep learning can be applied to it.
LLM-based classification, information extraction, and ensemble methods for text at scale.
LLM agents that plan, use tools, and act autonomously — and how to make them reliable in production.
Making model behaviour interpretable so that AI can be trusted in high-stakes decisions.
Time-series and predictive models that turn historical data into reliable, forward-looking decisions.
Distributional regression and empirical methods that quantify uncertainty, not just point estimates.
A library for tabular deep learning featuring Mamba (state-space), transformer, and ResNet-style models with a scikit-learn API.
View on GitHub →A hands-on, self-paced course that takes you from your first line of Python to shipping a real AI-powered automation.
View on GitHub →Multi-class and multi-label text classification with LLMs and classical models, combined through advanced ensembling.
View on GitHub →I speak and lecture on applied AI for academic, industry, and executive audiences — translating where the field is heading into what it means for research and business.
Keynotes · Guest lectures · Executive workshops · Panels
Invite me to speak →Together with Dr. Knut Zoch, Research Fellow at CERN, formerly at Harvard University, I lead Bridging AI & Society, an interdisciplinary summer school that combines the technical foundations of machine learning with the ethical, legal, and societal dimensions of artificial intelligence. Offered in collaboration with the Studienstiftung des deutschen Volkes, the programme brings together participants from diverse academic backgrounds to explore both the opportunities and challenges of AI.
Core ML concepts, methods & techniques, hands-on data work in Python, and the societal impact of AI.
Obertauern 2026 · Banz Abbey 2025 · Ljubljana 2024 · Koppelsberg 2021 · Cambridge (St John's College) 2019.
Alongside my academic work, I advise a select group of organizations. I work end-to-end — from spotting the right AI opportunities to building the systems and training the teams that sustain them.
Where AI creates real value
Identifying high-impact opportunities and turning them into a credible roadmap.
Hands-on, production-grade systems
Designing architectures and building AI systems that deliver measurable impact.
Teams that sustain the work
Upskilling people and embedding the practices that keep AI working after launch.
For advisory enquiries, speaking invitations, research collaboration, or media, send a short message.