Research in the Field of Applied Computer Science
The research activities in Applied Computer Science primarily address the use of Computer Science in innovative applications particularly related to the humanities, as well as the IT-based methods and principles essential to this field.
Applied Computer Science in the Cultural Sciences
The research projects deal with matters pertaining to Computing in the Cultural Sciences, i.e., computer science in the humanities. The research group develops, for example, information systems for architectural heritage conservation, human geography or communication studies.
Semantic information processing systems, which are continually being refined, represent the foundation of this work. The focus of this research is placed on the following technological fields:
- Geoinformation Systems and Services
- Digital Libraries and Archives
- Mobile Assistance Systems
- Computer-mediated Communication
Explainable Machine Learning
The chair's research focuses on the development of robust, data-efficient machine learning methods with versatile applications in industry and especially in healthcare. It is guided by the idea of making a positive contribution to society and patients.
- Basics: Further development of Neural Networks (Deep Learning) with a focus on robustness and interpretability.
- Application: Quantitative image analysis based on classification, segmentation and localisation including uncertainty estimation.
- Impact: Translation and integration of research results in close collaboration with partners from industry and the healthcare sector
Information visualization is a mediator between humans and machines – it makes machine data readable for humans and provides an interface to use the machine. In this role, the discipline integrates various computer science techniques, in addition to its own contributions, and makes them available. The research group carries out basic research developing new visualization techniques and investigates interactive visual analysis systems for various disciplines. Good usability and comprehensibility of the visualizations as well as transparency of data processing are always taken into account. The research of the group splits into the following areas:
- Expressive Visual Encodings: Meaningful visualization of complex and dynamic processes and behavior patterns.
- Explanatory Visual Reporting: Explanatory combinations of text or speech and visualization, where both media merge through seamless integration.
- Enabling Visual Analytics: Analysis methods that enable conclusions to be drawn through comparative presentation as well as interactive editing and abstraction.
The field of Cognitive Systems in Applied Computer Science deals first and foremost with learning. The aim of machine learning is to develop software that enables computers to acquire new knowledge or more general capabilities from particular and exemplary experience.
Machine learning research is focused on improving existing learning algorithms or developing new ones, with the aim of creating programs that replicate human learning processes.
Machine learning makes it possible to utilize in computer programs knowledge and capabilities for which no explicit model can be specified. This can be applied, for example, to computer-aided diagnostics, the detection of patterns in large volumes of data (data mining) or to the development of user-adaptive systems.
The focal areas of Media Informatics research are context-related information retrieval, content-based retrieval in distributed environments, search engines for internet and intranet applications, content-based image retrieval, blended learning and e-learning applications, and infrastructures and applications for digital humanities.
The three vital developments of human-computer interaction (HCI) play a major role in research: interactive systems (with graphic interfaces and windows-based user interaction); cooperative systems (for computer-aided communication and cooperation); and ubiquitous systems (for natural user interaction with the help of sensors and actuators of finger movements, speech input, etc.). Research work is conducted primarily in the following fields:
- The fundamentals: methodological, conceptual and technological foundations for the development of these systems
- Contextual support: sensors for the capture of information; information presentation indicators; information and context modeling
- Innovative user interfaces: the conceptual design and evaluation of mobile and web-based user interfaces, as well as ambient interfaces which utilize users’ physical surroundings to display digital information.
Research in the area of Smart Environments contributes to the university-wide research focus on human-centred artificial intelligence (AI) by exploring AI techniques for interactive and context-sensitive applications. The processing of knowledge about space and time plays a central role (knowledge representation, qualitative spatial and temporal reasoning). It is investigated how intelligent systems can acquire knowledge about their environment through learning and perception, how conclusions can be drawn efficiently from this knowledge, how it can be communicated with humans, and how such techniques can be integrated in a technical system.