Complex Systems Computation Research Group – CoSCo
The CoSCo research group studies computational problems in complex systems, especially regarding prediction and modelling. Its research areas include stochastic modelling and data analysis, Bayesian networks and related probabilistic model families – such as finite mixture models, Bayesian multi-nets and discrete main component analysis, information-theoretical approaches to inference (MDL) and stochastic optimisation algorithms like simulated annealing and genetic algorithms.
The work has a strong basic research component, being at the intersection of computer science, information theory and mathematical statistics. In addition, the research works towards a strong application component. The theoretical results on methods have been applied in many fields, such as social sciences, criminology, ecology, medicine, historical studies and industrial applications.
Recent research has recently focused on such areas as personalisation of the internet, election engines, next-generation search engine techniques and location-aware services. The members of the group possess varied abilities, from theoretical research to excellent programming skills. To name one concrete example of the group's broad field of expertise, we can mention the unique B-Course data analysis server (http://b-course.hiit.fi) developed and maintained by the group. It applies the latest research results from the field of probability modelling. During its three years of existence, the server has been accessed by over 15,000 users world-wide, and the results from the analysis service have been used e.g. for the development of a vaccine against HIV, analysing birdsong and studying gene data.
In 2006, next-generation search engines were still one of the most important focuses of the group's work (please see http://cosco.hiit.fi/search ). CoSCo looks to become an important international operator in the development of open-source code in this field, and is the co-ordinator of a large EU project in this area (Alvis, please see http://cosco.hiit.fi/search/alvis.html ). The group members also established and organised a meeting on the subject, "International Workshop on Intelligent Information Access" (IIIA-2006), which brought a great number of experts in the field to Helsinki in July 2007 ( http://cosco.hiit.fi/search/IIIA2006/ ).
The more theoretical aspects of the research group are represented by the www.mdl-research.org portal that the group continued to maintain in 2006. This website endeavours to collect the main results of research on the Minimum Description Length (MDL) theory developed by Jorma Rissanen in one place. Rissanen also co-operates actively with the group. He was awarded the prestigious Kolmogorov medal in 2006 for his work on the MDL theory.
Contact person: Professor Petri Myllymäki.
Website: http://cosco.hiit.fi/
Projects
Probabilistic Methods for Microchip-data Analysis (PMMA)
Image-signal denoising methods based on MDL theory (KUKOT)
Scalable Probabilistic Methods for Next Generation Internet Search Engines (Prose)
Superpeer Semantic Search Engine (Alvis)
Search-Ina-Box (SIB)
Cognitively Inspired Visual Interfaces for Representing Multidimensional Information (CIVI)
Publications
Roos, T. & Grünwald, P. & Myllymäki, P. & Tirri, H: Generalization to Unseen Cases. Pp. 1129-1136 in Advances in Neural Information Processing Systems 18 (NIPS 05), edited by Y. Weiss, B.Schölkopf and J. Platt. MIT Press, Cambridge , MA , 2006
Silander, T. & Myllymäki, P: A Simple Approach for Finding the Globally Optimal Bayesian Network Structure. Pp. 445-452 in Proceedings of the 22nd Conference on Uncertainty in Artificial Intelligence (UAI-2006), edited by R. Dechter and T. Richardson . AUAI Press, 2006.
Buntine, W. & Jakulin, A: Discrete Components Analysis. Pp. 1-33 in Subspace, Latent Structure and Feature Selection Techniques, edited by C. Saunders, M. Grobelnik, S. Gunn and J. Shawe-Taylor. Springer-Verlag 2006.
Roos, T. & Heikkilä, T. & Myllymäki, P: A Compression-Based Method for Stemmatic Analysis. Pp. 805-806 in Proceedings of the 17th European Conference on Artificial Intelligence (ECAI 2006), edited by G. Brewka, S. Coradeschi, A. Perini and P. Traverso. IOS Press, 2006.
Jaeger, M. & Nielsen, J. & Silander, T: Learning probabilistic decision graphs. International Journal of Approximate Reasoning, 42 (2006), 84-100.