Principles and methods of Computer Science nowadays not only affect nearly all scientific fields but their applications also have an impact on society and all areas of life. As a result, Computer Science is developing from a previously more technical-engineering discipline to a discipline with broad implications and connections which also extends into e.g. the social sciences or the arts and humanities.

The Faculty of Computer Science at the University of Vienna covers a great variety of subjects in the areas of Core Computer Science and Applied Computer Science. One objective is to strengthen the disciplinary core of the Faculty of Computer Science in order to successfully develop interdisciplinary connections and to make valuable contributions to current and future challenges in research and teaching. This can be achieved by taking advantage of the wide range of disciplines at the University of Vienna. By interacting and establishing networks with many different disciplines, the Faculty of Computer Science develops a unique profile. Interdisciplinary links already exist in the following areas:

  • in Data Science and Scientific Computing with mathematics, chemistry, biology as well as business and economics;
  • in Bioinformatics with mathematics, chemistry, biology;
  • in Computational Science with pharmacology and pharmaco-informatics, mathematics, chemistry, biology, physics, astronomy, earth sciences;
  • in Business Informatics with business and economics;
  • to the Faculty of Philological and Cultural Studies;
  • in Didactics of Computer Science with the Centre for Teacher Education;
  • with psychology;
  • with law.

These interdisciplinary links will be further extended.
Additional links to the following are being established and deepened:

  • Neuro- / cognitive sciences
  • Faculty of Social Sciences
  • Faculty of Philosophy and Education
  • Medical University of Vienna

The research strategy of the Faculty follows an international approach and active cooperation structures have been established with other universities and research institutions at national and international levels. The goal is to maintain a balance between basic research and applied research. Technology transfer activities will contribute to the sustainable effects of the research activities.

Key Research Areas

Graphs and Networks

The research focus Graphs and Networks deals with questions about networks which can often be modelled as graphs. These networks not only include communication networks which form the backbone of our digital society, but also other types of networks such as social networks. For the use of and communication through such networks, new algorithms are necessary that fulfil high efficiency and scalability requirements: networks are currently growing rapidly in many areas, which results in an increased energy consumption. Due to the immense popularity of data-centric applications (in the areas health, business, social networking, etc.), data traffic to and from data centres virtually explodes and consequently wide-area networks could soon reach their capacity limits. Studies also predict that data centres will account for approximately 5% of global energy consumption by 2025. Energy efficiency and sustainability are, therefore, an important research focus.

Many questions in the area of such large networks require the solution of algorithmic problems on graphs. Efficient graph algorithms are developed, theoretically analysed, and also empirically evaluated. Research activities also include dynamic, distributed and parallel algorithms. Examples of application areas for these algorithms are new types of communication technologies such as Software-Defined Networks, programmable Data Planes, reconfigurable optical networks, or “Self-* Networks” which optimise and repair themselves autonomously, which makes them more efficient, secure and reliable.

Research activities closely related to this, which form strong links to the research focus Data Science at the Faculty, deal with algorithms for understanding neural networks and algorithms for gaining knowledge from social networks. Graph-based abstractions also serve as the basis for algorithms for and programming of future computer architectures which are not only highly parallel, but also increasingly heterogenous for reasons of energy efficiency. Task-based runtime systems make it possible to represent complex scalable and adaptive algorithms, the basis for computationally and data intensive applications, as dynamic graphs. They play an important role in the development of a new generation of parallel programming models. Networks are also of central importance for Cloud Data centres and supercomputers, which now include millions of processors within a system.

Data Science

Data Science deals with the acquisition of knowledge from data. Due to the digital transformation, nearly all scientific fields now require Data Science methods. Data-driven research is of central significance in numerous scientific fields, e.g. in medicine, life science, pharmacy, chemistry and astrophysics, but alsoin humanities and social sciences, where new insight is increasingly based on Data Science methods. At the same time, problems from other scientific fields inspire the development of new Data Science methods. Data Science is, in its broadest interpretation, an interdisciplinary field of research and requires intensive collaboration between method developers and users. The University of Vienna conducts interdisciplinary research activities in this field. The Faculty of Computer Science conducts research in pivotal methodological components of Data Science, plays a leading role in these interdisciplinary research activities and contributes Computer Science expertise. Especially because of its inherent bridging function, Data Science is an important research focus at the Faculty of Computer Science.

Since datasets are rapidly increasing, methods for knowledge discovery from data are essential pillars in Data Science research. Research questions look at the entire process of knowledge discovery from data: methods from database research for efficient storage, representation, organisation and similarity search in very large datasets, Data Mining methods for finding trends and patterns, Machine Learning methods for predicting correlations (of particular interest are interpretable machine learning and robust machine learning) and visualisation methods for understanding data and models. In this area, there are connections to algorithmic-methodical components of Computational Science, where classical ab-initio models are increasingly supplemented by data-based models and, therefore, the use of machine learning methods has also become very important.

Data Science is an emerging field of research because more and more data can be acquired and collected in nearly all scientific fields and because computing infrastructure has developed rapidly in recent decades. However, the continuous development and diversity of the computing infrastructure will also require permanent advancement of algorithms, runtime systems as well as tools and libraries in order to achieve the ambitious objectives of Data Science. Hence additional research activities at the Faculty of Computer Science in the areas of, for example, robustness and scalability of numerical algorithms, methods of analysing neural data, text mining, or software and middleware, are important components.

Intelligent, Distributed, and Secure Systems

This research focus is based on the observation that the real and digital world will increasingly converge in the future. In this context, our focus will primarily be on intelligent systems which are required in this transformation process.
One challenge here is to research and develop methods and procedures about knowledge in and about intelligent systems while taking new approaches into account.
This leads among others to the following research questions: How can systems be designed and modelled according to a “design-oriented approach” so that new architectures emerge in a disruptive environment (sustainability)? How can domain-specific knowledge be formalised and how can a representation of it be made “machine-understandable” (operationalizable, intelligent)? How can the behaviour of these intelligent systems be made comprehensible (explainability)? How can security and privacy be guaranteed in this context (secure systems)? How can we deal with the challenges of the ever-increasing distribution of information systems (distributed systems)?

The complexity and diversity of digitisation topics is not only addressed by appropriately positioned research approaches, but also by the design-oriented approach that takes disruptive technologies into account. The research questions mentioned above are of central importance for core Computer Science and are also an integral part of the modern, system- and design-oriented Business Informatics, which is an integral part of the Faculty of Computer Science.

This research focus includes research on as well as the development of approaches, methods and tools for the areas cloud computing, flexible and distributed processes, parallel computing, conceptual modelling, intelligent and agile agents, DevOps, semantic technologies, Internet of the Future, service-oriented systems, cooperative systems, IT infrastructure for the industry 4.0, cyber-physical systems (CPS), Internet of Things, and blockchain systems.
The semantics of specific application domains are particularly relevant so that new technologies can be used appropriately. Conceptual modelling serves as the basis for this. By merging different basic technologies and those still in development, feasibility and solution approaches can be illustrated and made verifiable within the framework of prototype systems.

As part of design-oriented research, prototype implementations are designed, executed and validated using emergent technologies. By using specific Use Cases, it is possible to evaluate the developed models in an “experimental environment”. This will also give the opportunity to make the symbiosis of the virtual and real world, which happened through digitisation, more accessible.

Human-Centered Computing

Human-Centered Computing focuses on the people and their diverse needs and aspirations and includes both theoretical and experimental developments of human-computer systems, interfaces, models, and interaction processes. Human-Centered Computing is an inherently interdisciplinary area between Computer Science and psychology, human-, neuro- and social sciences, business and economics, law, political science, translation and communication studies, technical philosophy, ethics, arts, etc. Our research focus centres on the Computer Science aspects and the resulting connections to other disciplines mentioned above.

At the centre of the research activities is the vision to reduce the digital gap and to contribute to increasing quality of life, social inclusion, effectiveness as well as personal fulfilment and purpose for the individual and for society. Members of the Faculty investigate human-centred design of human-computer interfaces, assistive communication devices, brain-computer interfaces, technologies and systems to include and enable people with special needs. They work on the improvement of user experience to increase the acceptance of applications as well as questions about value-based use and the sustainable development of ICT.

In addition to focusing on the design of the interfaces between humans and computers, members of the Faculty investigate in several initiatives, some of which are interdisciplinary, the technology that will support people in learning, decision-making, working and improving the quality of life in the digital change. This is achieved, for example, through empirical research of influence factors on the use of information technologies, through the expansion and improvement of human learning processes through digitally-supported access, through the comprehensible explanation and comprehensible visualisation of AI models as well as through digital technologies to support communication and cooperation.
At the Faculty of Computer Science, there are important connections between the research foci Human-Centered Computing and Data Science in the area of technology enhanced learning and a visualisation of data that is understandable for humans. The research focus Intelligent, Distributed, and Secure Systems is inherently connected with Human-Centered Computing through their research approach in design thinking, where human-centred systems and interfaces are developed. Furthermore, security and the protection of privacy are essential characteristics of Human-Centered Computing and are, therefore, closely linked to the security concerns in Intelligent, Distributed, and Secure Systems.

There is also an important link to the research area of Computer Science Education (Didactics of Computer Science), especially through physical computing, gaming education and technology-enhanced learning.