The focus of our research group is on scalable techniques for data management and data science. Our work targets at the synthesis of conceptual research results with prototypical deployments in different domains.

In this proseminar, we explore fundamental concepts and methods in data science and information systems. Topics include, but are not limited to: data management, data integration and feature extraction, information systems for distributed data processing, data platforms for large-scale data management, e.g., data lakes and lakehouses, applications of such information systems in specific domains, e.g., cloud monitoring and energy systems, methods to address different kinds of bias in data, fairness in machine learning (ML) and automated decision-making, the interpretation and explanation of ML model results, the representation of expert knowledge and approaches for combining domain knowledge with ML.