Prof. Dr. Christoph Weisser, MLitt (St Andrews), MSc (Oxford)

Bridging AI research and enterprise impact.

Professor of Mathematics, in particular Business Data Science Former Technical Lead Analytics & AI

I build production-grade AI, publish peer-reviewed research, and advise a select group of organizations on turning artificial intelligence into measurable value.

About

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.

Prof. Dr. Christoph Weisser

Research

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.

Tabular Deep Learning

Most business data is tabular. I research how deep learning can be applied to it.

Natural Language Processing

LLM-based classification, information extraction, and ensemble methods for text at scale.

Agentic AI Systems

LLM agents that plan, use tools, and act autonomously — and how to make them reliable in production.

Explainable & Trustworthy AI

Making model behaviour interpretable so that AI can be trusted in high-stakes decisions.

Predictive Analytics

Time-series and predictive models that turn historical data into reliable, forward-looking decisions.

Statistics

Distributional regression and empirical methods that quantify uncertainty, not just point estimates.

Selected Publications

  1. Probabilistic Topic Modelling with Transformer Representations

    A. Reuter, A. Thielmann, C. Weisser, B. Säfken, T. Kneib · IEEE Transactions on Neural Networks and Learning Systems · 2025

  2. A Machine Learning and Explainable AI Framework Tailored for Unbalanced Experimental Catalyst Discovery

    P. Semnani, M. Bogojeski, F. Bley, Z. Zhang, Q. Wu, T. Kneib, J. Herrmann, C. Weisser, F. Patcas, K.-R. Müller · The Journal of Physical Chemistry C · 2024 · ACS Editors’ Choice

  3. STREAM: Simplified Topic Retrieval, Exploration, and Analysis Module

    A. Thielmann, A. Reuter, C. Weisser, G. Kant, M. Kumar, B. Säfken · Annual Meeting of the Association for Computational Linguistics (ACL) · 2024

  4. Mapping ex ante risks of COVID-19 in Indonesia using a Bayesian geostatistical model on airport network data

    J. D. Seufert, A. Python, C. Weisser, E. Cisneros, K. Kis-Katos, T. Kneib · Journal of the Royal Statistical Society: Series A · 2022

View full publication list on Google Scholar →

Open Source

LabelFusion

Multi-class and multi-label text classification with LLMs and classical models, combined through advanced ensembling.

View on GitHub →
See all projects on GitHub →

Speaking

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.

  • The state of enterprise AI: from generative models to autonomous agents
  • Building trustworthy, production-grade AI systems
  • From AI strategy to measurable business value

Keynotes · Guest lectures · Executive workshops · Panels

Invite me to speak →

Summer Schools

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.

Themes

Core ML concepts, methods & techniques, hands-on data work in Python, and the societal impact of AI.

Recent editions

Obertauern 2026 · Banz Abbey 2025 · Ljubljana 2024 · Koppelsberg 2021 · Cambridge (St John's College) 2019.

Visit bridgingaiandsociety.org →

Advisory

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.

01

Strategy

Where AI creates real value

Identifying high-impact opportunities and turning them into a credible roadmap.

  • AI Strategy
  • Use-case Discovery
  • Governance
  • Adoption
02

Build

Hands-on, production-grade systems

Designing architectures and building AI systems that deliver measurable impact.

  • Generative AI
  • RAG
  • AI Agents
  • ML Platforms
03

Enable

Teams that sustain the work

Upskilling people and embedding the practices that keep AI working after launch.

  • Training
  • Best Practices
  • MLOps
  • Mentoring

Contact

For advisory enquiries, speaking invitations, research collaboration, or media, send a short message.