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Research

Latest Research Achievements

Inter-Departmental Research Foci at the FIDS

Information security

Departments involved in the FIDS

Research topics in the field of information security

Research work in the field of information security is highly relevant to society and at FIDS includes

  • Issues relating to the use of machine learning methods in information security and IT forensics,
  • Issues relating to the protection of AI systems against targeted attacks,
  • Issues relating to distributed, fiduciary systems,
  • Issues relating to the collection and exchange of cyber threat intelligence and
  • Issues relating to cyber ranges and the simulation of attacks and their defence.

In addition, related research topics such as the implementation of the right to privacy and informational self-determination (e.g. issues relating to user acceptance and individual data protection behaviour as well as the value of privacy mechanisms) are also dealt with at FIDS. The implementation of privacy and data protection rights requires the development of new, extended anonymisation and pseudonymisation approaches. To this end, modern cryptographic methods such as post-quantum cryptography and their application are being researched at FIDS.

Explainable AI (XAI)

Departments involved in the FIDS

Research topics in the field of Explainable AI

In the field of Explainable AI (XAI), all departments of FIDS combine various specialist areas and complementary methodological approaches in order to investigate issues from a theoretical and applied perspective.

Within the theoretical perspective, mathematics, symbolics, statistics and computer science foundations and methods are researched to explain how artificial intelligence works and to interpret statistical data. This includes, for example, symbolic methods that enable formal justifications/interpretations of the outputs of AI systems. Furthermore, the field of differential privacy enables AI methods to be used while safeguarding the privacy of users.

Building on these theoretical foundations, Explainable AI approaches are being developed, investigated and evaluated in various application areas (e.g., immunology, oncology, image processing or operational value creation). To this end, the interaction between humans and AI systems is also being researched for a better understanding of the perception, acceptance and behavioural influence of Explainable AI. Overall, the faculty is thus making an important contribution to research into the central quality properties of machine learning methods, which are essential requirements for the responsible use of artificial intelligence.

Computational Methods in the Natural Sciences

Participating departments of the FIDS

Research topics in the area of computational methods in the natural sciences

The "Computational Methods in the Natural Sciences" focus area develops new computer-aided approaches for applications in the fields of biology, medicine, physics and chemistry. Close collaboration with researchers from the natural sciences and medical faculties is at the forefront of this work.

Large parts of today's natural sciences and medical research rely heavily on computer informatics methods, e.g. methods of data engineering, machine learning and high-performance computing. In all fields, the analysis, visualisation and integration of large amounts of data is of central importance. Modern measurement methods such as high-throughput sequencing, mass spectrometry or high-resolution microscopy as well as screening procedures or population-wide studies generate large, complex and heterogeneous data sets. The methods developed at the FIDS include statistical and machine learning procedures, with which quantitative information is obtained from the often indirect and unstructured raw data, with which different data sources are linked and statistical correlations are uncovered. A particular focus is also on uncertainties to be estimated due to measurement inaccuracies as well as systematic errors and missing values. In many areas of the natural sciences, the understanding of scientific processes is represented by complex models from which predictions can only be derived using computer simulations. This applies to the description of fundamental interactions between quantum chemistry particles as well as the modelling of genetic interactions with applications in cancer research. New simulation algorithms are being developed at FIDS, in particular those that incorporate machine learning techniques to make the problems solvable. Furthermore, research into general artificial intelligence is producing completely new optimisation methods in order to develop automated processes. As part of the research focus, reinforcement learning techniques are used, which are applied in the natural sciences in the control of experiments with feedback or in the design of experimental setups.

Human-centred AI

Departments involved in the FIDS

Research topics in the field of human-centred AI

People use information and information systems to solve tasks in analogue and/or digital contexts. This raises a variety of questions:

  • How do people use digital information systems in everyday situations?
  • What influence do different interaction options have on the success of using an information system or on the fulfilment of the task in question?
  • Can information systems influence people's opinions and decisions?

These questions guide the research focus "Human-Centered AI" at the FIDS. Answers to these questions can help us to better understand how, when and why people choose a certain action from a set of options. As a result, human-centred AI is always supported by the "human in the loop" paradigm, complementing AI focal points in the field of autonomous systems. Through this focus area, FIDS makes direct contributions to the key areas of "Digital Transformations" and "Integrated Sciences in Life, Health, and Disease" at the University of Regensburg.

Solutions developed in this research focus area are always interdisciplinary in nature: the fact that people as users of digital technology are the central object of investigation requires close collaboration with psychology. On the other hand, information systems always have a distinct technical component that can be used to observe the environment and users as well as for interactive behaviour. This requires technical expertise and competence in the fields of machine learning and data science, which are also available at the FIDS. The application scenarios at FIDS are extremely diverse and originate, for example, from management information systems (e.g. Internet of Things, socio-technical processes, human-robot interaction), the life sciences (e.g. prevention in medicine, counselling systems for patients, medical decision support systems, recommender systems) and information science in the narrower sense (e.g. fake news detection, information behavior in social media, credibility of information offerings on the Internet, influence of information on people's attitudes, smart environments). Finally, an essential aspect of the research focus "Human-Centered AI" at the FIDS is the ethical and legal requirements and consequences of AI procedures in information processes.

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