Our group combines expertise in different aspects of computer science (data mining, machine learning, optimization, artificial intelligence), statistics (extreme value theory, Bayesian inference, multivariate analysis), and bioinformatics (analysis of biological networks and large-scale biomedical data).
“Data Science” is an increasingly expanding new field that focuses on theory and practice of learning from data. We can interpret the name “data science” in two ways:
- The science of data. This would be a scientific field that explores how to manage, analyse, or use data (or information), which could be seen as a subset of computer science/informatics and translates literally to “datalogi” in Danish (although “datalogi” means computer science and thus also includes other aspects that are not of particular interest in “data science” such as, e.g., theoretical computer science or operating systems).
- Science from data. This interpretation would relate to the process of learning, to the methods used to create knowledge from data, or to the methodology of deriving valid insights from data. In this way it could be seen as a variant of statistics, but it also relates to theory of science and to theory of learning (as studied in machine learning or more general in artificial intelligence). However, this interpretation also aligns with the so-called “4th paradigm”, describing the transformation in many academic fields that is leading to sciences being more strongly based on the (semi-) automated analysis of (big) data (examples are bioinformatics, computational biomedicine, cheminformatics) or new ways of doing research in other disciplines (e.g., digital humanities, computational history).
In our group, we connect between computer science and statistics and subscribe to both interpretations of “data science”. In our research in data science we develop and evaluate methods for data analysis (data mining, machine learning, statistics, operations research, analytics), we strive to improve our way of understanding data and of gaining insights from data (visualization techniques, optimization), and we connect to various areas to apply learning from data in practice as well as to gain insights and to create knowledge and value from data in collaboration with partners in other academic fields, in companies, or in the public sector.
Topics of research include
- data mining
- machine learning
- deep learning
- optimization
- (explainable) artificial intelligence
- reinforcement learning
- computer vision
- signal processing
- extreme value theory
- Bayesian inference
- causal inference
- time series
- spatial-temporal data
- mobility data
- big data management and analytics
- multivariate data analysis
- high-dimensional statistics
- medical statistics
- bioinformatics and computational biology
- visualization
- visual analytics
- digital humanities
Group members
- Arthur Zimek
- Birgit Debrabant
- Hans Christian Petersen
- Jing Qin
- Marco Chiarandini
- Melih Kandemir
- Panagiotis Tampakis
- Peter Schneider-Kamp
- Ricardo Jose Gabrielli Barreto Campello
- Richard Röttger
- Stefan Jänicke
- Tariq Yousef
- Vaidotas Characiejus
- Yuri Goegebeur
Contact person
Arthur Zimek
Website: imada.sdu.dk/~zimek
E-mail: zimek@imada.sdu.dk
Group website: https://dss.sdu.dk