Lehrstuhl für Systeme der Informationsverwaltung

Proseminar: Foundations of Recent Research in Information Systems


The focus of our research group is on scalable techniques for data management and analytics. Our work targets at the synthesis of conceptual research results with prototypical deployments in different domains. In this seminar we focus on actual research topics including
representation of expert knowledge, applications for cloud monitoring and energy systems, machine learning (ML) techniques for scientific theory building and for solving differential equations, ML models and classification, feature extraction, sample-efficient reinforcement learning, and combining domain knowledge with ML.


  • Combining Domain Knowledge with Machine Learning
  • Representation of Expert Knowledge
  • Applications for Cloud Monitoring and Energy Systems
  • ML Methods for Solving Differential and Difference Equations
  • Supervised Uncoupled Feature Extraction
  • Unsupervised Uncoupled Feature Extraction
  • Discovering governing equations from data
  • Bayesian Neural Networks:  Grundlagen & Anwendung im Sample-Efficient Reinforcement Learning
  • Main Challenges in Using ML for Scientific Theory Building
  • Describing Intermediate Results of Supervised ML Models
  • Towards a Unified Formalism of Risks in ML Classification
  • Applications of Grounding ML Models in Log