Profile

Information on teaching

In teaching, the Chair is dedicated to a wide range of issues relating to business analytics, data science, machine learning and artificial intelligence (AI). In introductory modules, we teach the basics of programming, data analysis and algorithmic solution development in lectures and (computer-based) exercises. Building on this, we offer the opportunity to apply the methodological and conceptual knowledge acquired in advanced in-depth courses in practical project seminars. Here, for example, students work in small groups on real problems from practice or application-oriented research projects. The topics for these projects come from current research initiatives of the Chair and collaborations with practice partners as well as from the participating students' own research and practical projects.

Core research areas

• Design of Intelligent Systems: Socio-technical design of intelligent information systems based on modern data science technologies (e.g. deep learning, generative AI, process mining, natural language processing, computer vision)
• Explainable Artificial Intelligence: Development of algorithmic innovations in the field of intrinsically interpretable AI and machine learning models (e.g. Generalized Additive Models, Optimal Decision Trees) as well as post-hoc analytical explanation methods for black-box models (e.g. based on SHAP or LIME)
• Applied Machine Learning: Application and benchmarking of modern machine learning algorithms in various application domains (e.g. healthcare, production, finance)
• Human-AI Interaction: Empirical investigation of human perception and use of AI systems through user-centered experimental studies (e.g. to investigate algorithm aversion vs. algorithm appreciation)