Research activities

Arne Andersson

See also the publication list.

In my research, I aim at studying fundamental problems in computer science. One of my goals is to work towards bridging the gap between theory and practice. The focus has been mainly on algorithms and data structures. In addition, I am beginning to broaden my scope to do active research also in other areas, like E-commerce and distributed negotiations and machine learning.

Evidently, it is important to ensure that scientific research is relevant. Some aspects on relevancy, which I try to apply to my research, are listed below.

Algorithm theory

The bounds on fundamental computational problems, like sorting and searching, is critical for most areas within algorithm theory, such as computational geometry, graph algorithms, bioinformatics, and many more.

Some of my major research efforts have been made within this area

Simple and practical methods for sorting and searching

Apart from the theoretical achievements mentioned above, I have spent much effort on simple and practical methods.

String processing

String processing is becoming more and more important with the extended use of free test searching and data mining in large data bases and with the increased interest in bioinformatics.


Uppsala University is currently putting much effort on new research aresa, one important such area is bioinformatics. The effort is centered around the Linnaeus Centre for Bioinformatics, where I am a board member. Bioinformatics is a broad and rapidly evolving discipline where much algorithmic research and competence is needed. See also the section on string processing above.


E-commerce and Combinatorial auctions

This project is based on a clear vision of improving state-of-the art in the theory and practice of algorithms for electronic negotiations, and also contribute to the research in economy and game theory. Altogether, we hope to not only provide new scientific insights, but also assist the society in taking the step from simple transactions to fully developed electronic negotiation, so that partners in business and government networks can achieve greater flexibility, more efficient use of resources, and increased economic welfare.

Some selected references:

Machine learning

I also have some interest in machine learning. One of my standpoints is some skepticism about some of the claims made about neural networks. In my opinion, the quality of a generalization algorithm (or some other learning algorithm) should depend on the properties of the produced output, not on how the algorithm works itself. A discussion of this can be found in

A. Andersson, P. Davidsson, and J. Linden, Model selection using measure functions
Proc. of the ECML'98 Workshop on Upgrading Learning to the Meta-Level: Model Selection and Data Transformation, pages 54--65, 1998.

I have also some preliminary results on two well-studied learning problems, in both cases we produce solutions which are both simpler and better than neural networks. (Why take a detour over some complicated solution when you can solve it better directly?) The two problems are (ongoing, unpublished work):

More on this is to be presented.