Computational Information Technology

Computational information technology deals with problems where efficient numerical methods of computational engineering are combined with the problems of information processing. During recent years, computational approach in information processing has gained increasing importance, with progress in fields like computational intelligence and data mining. Computational intelligence comprises methods such as neural networks, probabilistic methods, fuzzy systems and evolutionary computing, where iterative numerical methods are used for solving complex problems, involving large amounts of measured data and only uncertain prior knowledge about the solutions or modeled phenomena. A central element is inference in novel situations, based on observed data, that is, inductive learning systems. Similar capabilities for knowledge discovery are needed in data mining, where useful pieces of information are sought from large, possibly unorganized, databases.

In the laboratory the research in computational information technology is focused in neural computing, Bayesian methods, and pattern recognition and modeling applications. A good example of fields in information processing, in which computational methods have large impact, is the Bayesian approach, where the estimation of a specific set of model parameters is replaced by constructing the probability distribution of the model parameters and integrating over it during the use of the model. This integration requires efficient numerical methods, such as Markov chain Monte Carlo methods. The numerical solution also makes it possible to avoid some standard assumptions (such as linearity and/or gaussianity), that may be bad in practice. For an overview of the approach and our results, see review paper [36].

In the following chapters we give short descriptions of the research projects in the computational information technology group.