Our approach towards our group's objectives follows three main research themes combining expertise in different research areas:

• Data Management & Engineering

  is fundamental to modern computing, enabling efficient storage, processing, and retrieval of vast amounts of structured and unstructured data on various types of infrastructures (distributed, centralized, quantum, etc.) We focus mainly on three subtopics of Data Management: A. Scalable Data Management: Research in this area focuses on designing scalable, efficient, and secure data management systems that support AI, analytics, and real-time decision-making. B. Metadata Management: Research in this area focuses on the elicitation and processing of metadata in applied domains, as e.g., Plant Sciences or Digital Libraries. This covers also the human aspects of metadata management. C. Quantum Data Management: Explores how data is stored, represented, and processed in hybrid classical-quantum environments. Key research areas include classical simulation, where classical data represents and simulates quantum states and operations, and hybrid quantum-classical computing, which optimizes data workflows for preprocessing, postprocessing for quantum applications such as quantum error correction and quantum machine learning.

• Crowd Computing & Human-Centered AI

  represent core areas which are instrumental in developing the next generation of data-driven AI systems, which reply on Human-in-the-loop computing, Human-AI interaction, User Modeling and Explainability. These areas consider the computational role of humans for data-driven AI, i.e., AI augmented or supported by humans, and the interactional role of humans with AI systems, i.e., AI for humans.

• Information Retrieval

  is concerned with the development of algorithms and interface elements to enable people to satisfy their information needs by retrieving and presenting results from large collections of mostly unstructured documents. Besides this system-oriented view, this research field also has a user-oriented focus that explores how & why people search and reach decisiond based on information accessed.

• User Modeling & Learning Analytics

  use datafication for building the basis for collecting traces about learning and teaching activities and making use of them in Learning Analytics and Artificial Intelligence for learning support.