Data and Knowledge (D&K)

Topic Cluster Chair: Univ.-Prof. Dipl.-Ing. Dr. -Ing. Moritz Grosse-Wentrup


Research Agenda

The research cluster Data and Knowledge 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.

Research Seminar D&K (summer term 2024)

  • 500501 SE Doctoral Research Seminar - Data and Knowledge