Our approach towards our group's objectives follows three main research themes combining expertise in different research areas:
Data Management & Engineering
are the cornerstone of modern Artificial Intelligence and data-driven systems. We focus on Data Engineering, and research ways of preparing data for its deployment or usage in a complex AI/data-driven system. In Scalable Data Management, the focus is on how to cope with the ever-increasing demand for storage and processing power by scaling data operations.
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.
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.
In WIS we focus on two core aspects of modern Data Management: a) Data Engineering, and b) Scalable Data Management.
We focus on areas instrumental in developing the next generation of AI systems: (1) Human-in-the-loop AI, (2) Human-AI interaction, (3) User Modeling and Explainability.
We focus on core Information Retrieval topics such as conversational search, collaborative search, search as learning and data-hungry ranking models.