Research on high performance computing was initiated by
the first Director of the Institute of Computer Science

Professor Jacek Mościński in early 80-ties.



Nowadays, our activity is concentrated on:

Further reading on our work


Pattern Recognition and Intelligient Data Exploration

Since the works of Hollerith in 1890s, machines have been used to aid humans in processing of vast amounts of data. Nowadays, as we explore natural, technological and business processes for which analytical models are not yet uncovered, the focus is shifted towards intelligent data recognition, analysis and understanding techniques.

At the Department of Computer Science, we have gained proficiency in developing and applying pattern recognition solutions. Our expertise includes methods for automatic classification and clustering, by using supervised and unsupervised learning coupled with advanced noise removal techniques. Our tools for data visualisation and dimensionality reduction provide intuitive support for data analysts. We also excel in automatic information extraction with image processing algorithms and interaction network analysis.

Main application schemes of our methods include intelligent system control, exploratory data analysis and decision support. Our clustering techniques have been used for surveillance and safe-guarding of IBR2 pulsed nuclear reactor and in supervising high-performance computer simulations. Ensemble classification methods of ours have been employed in an industrial drug discovery support system. Our algorithms have also proven successful in handling pattern recognition problems emerging from life sciences, e.g. genomic and proteomic data exploration, as well as cancer detection.

Our activities have been funded by grants from Polish State Agencies: KBN in 1993-95, MNiI in 2004-06 and MEiN in 2005-07, as well as funds from the University of Minnesota Digital Technology Center in 2003-04.

We collaborate on extending, customising and applying our algorithms with leading foreign and national research and industry partners, including:

  • Minnesota Supercomputing Institute, – computer simulation results analysis,
  • University of Minnesota Medical School, USA – drug discovery, cancer patterns analysis,
  • University of Southern California, USA – earthquake patterns exploration,
  • Joint Institute for Nuclear Research, Russia – nuclear reactor surveillance,
  • Fujitsu Group, Poland – drug discovery support,
  • Collegium Medicum of the Jagiellonian University, Poland – cancer detection.

Research team: Witold Dzwinel, Krzysztof Boryczko, Tomasz Arodź, Marcin Kurdziel



Ontological Representation of Grid Resources

Modern Grid solutions provide now variety of middleware solutions that are supposed to help manage resources available in the Grid environment, but most of these solutions are based on specific formats for each aspect of the Grid, thus making inconvenient integration and making complete virtualization of resources difficult. Our response to needs for a more uniform way of defining metadata in the Grid environment is using ontologies as a formalism for unified representation of various kinds of metadata in the Grid. In the context of automatic workflow composition from semantically annotated Grid services, an extensible and distributed framework for management of semantic metadata in Grid environment called Grid Organizational Memory (GOM) was developed.


The objective was to define knowledge representation paying attention to the distributed nature of a working environment and allowing collaborative development of ontologies within any application domain. The result was the separation schema for the GOM ontologies. GOM contains the prototype versions of generic ontologies, domain specific ontologies and supports management of ontological registries. The framework architecture also reflects separation of Grid on Virtual Organizations (VO). GOM prototype is available in terms of public license. It is being used for development of ontology similarity for VO development and semi-automatic construction of workflow-type grid applications.

The scientific research in the framework of EU IST-2002-511385 K-WfGrid project and KBN Grant is being performed in tight cooperation with:

  • Fraunhofer Institute for Computer Architecture and Software Technology, Berlin
  • Institute for Computer Science, University of Innsbruck
  • Institute of Informatics of the Slovak Academy of Sciences, Bratislava


Researchers: R. Słota, M. Majewska, M. Kuta, S. Polak, J, Kitowski with ACK Cyfronet AGH collaborators: B. Kryza, Ł. Dutka.


Usage of Grid Organizational Memory for workflow composition


Modeling Natural Phenomena for Computer Graphics and Animation

Modeling of natural phenomena and processes such as behavior of water surface with splashes, forming of ocean waves, smoke and clouds is an intriguing and challenging task. In order to visualize them in realistic way in real time we need to employ simplified physical models, as well as non-physical approach. Special algorithms utilizing capabilities of modern Graphics Processing Units has to be developed to achieve desirable speed of both simulation and animation. We have implemented algorithms for visualization of fluid flow, based on continuous models in which simplified Navier-Stokes equations are solved. We also use particle methods as well as cellular automata for evolution and visualization of clouds. Non-physical models have been adopted for ocean waves simulation.

Turbulent flow through the dam visualized in real time

Real time animation of ocean waves

Volumetric effects in clouds evolution

Researchers: W.Alda, K.Boryczko, W.Dzwinel, R. Górecki, J.Kitowski