Our Research Themes

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.

  • 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.

Data Management & Engineering

In WIS we focus on two core aspects of modern Data Management: a) Data Engineering, and b) Scalable Data Management.

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Crowd Computing & Human-Centered AI

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.

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Information Retrieval

We focus on core Information Retrieval topics such as conversational search, collaborative search, search as learning and data-hungry ranking models.

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User Modeling & Learning Analytics @ CEL

LDE-CEL has focused its research activities around the following core topics: data and AI enhanced learning, digital literacy, augmented and virtual reality.

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