The main research themes of the Algodan CoE are:

• Sequence analysis
• Learning from and mining structured and heterogeneous data
• Discovery of hidden structure in high-dimensional data
• Foundations of algorithmic data analysis

There is considerable overlap between the themes: certain algorithmic and probabilistic techniques occur in many themes. In the same way, several themes can be used for a single application.

Sequence analysis considers the algorithmic techniques for sequential data. The key methods in the theme are string algorithms, pattern discovery techniques, dynamic programming, and probabilistic modelling. Examples of the algorithmic tasks in the area are approximate string matching, episode discovery, and finding motifs and orders from data. The techniques of sequence analysis have numerous applications in, for example, gene mapping, finding regulatory regions in genomes, telecommunications, linguistics, and paleontology.

Most applications have multiple types of data objects, many different types of data, etc., instead of the classical situation of a single table with observations and variables. Learning from and mining structured and heterogeneous data looks for techniques for data analysis tasks involving such data sets. The methods studied are pattern discovery, prediction of structured objects, the analysis of flows, etc. The applications include biological data analysis, information retrieval, telecommunications, and environmental studies. Algorithmic techniques for probabilistic modelling are crucial in this theme.

The high dimensionality of many datasets causes interesting modelling problems and leads to extremely challenging algorithmic questions. The third theme, discovery of hidden structure in high-dimensional data, looks at how to find latent structure in high-dimensional data sets. The latent structure can be in the form of components, as in independent component analysis, or cluster-like structures, or it can be a parsimonious model giving weight only to a small fraction of the observed variables. The techniques in this theme are based on probabilistic modelling, with a strong algorithmic component.

The theme on foundations of algorithmic data analysis looks at the frameworks of algorithmic data analysis. What can be said about the limitations of pattern discovery? What are the fundamental bounds on the efficiency of string algorithms? What is the computational complexity of fitting probabilistic models of a certain type? Questions such as these abound in algorithmic data analysis, and they are fascinating problems in core computer science.