Competence

Volume and complexity of data that is used in day-to-day life is increasing rapidly and both private companies and public institutions have to tackle the challenge of using the data in effective and beneficial manner. This area is well aligned with the field of research of Institute of Computer Science. The role of Laboratory of Applied Informatics is to provide the platform that brings together state-of-the-art research and practical needs of applied sector. The laboratory is dedicated to real-world testing of new scientific results that belong to selected fields of computer science and collected know-how and experience is used to transfer the knowledge into applications. Working with real-world data is the next step for application of new algorithms and methods. This usually means that a number of issues has to be solved that are not present in previous basic research. Close collaboration with partners from the applied sector is required to move scientific results closer to applications that respect particular needs and technical constraints given by application domain.

The key areas of competence of the laboratory include:

• analysis and modelling of complex systems using methodology for both linear and non-linear analyses,
• selected applications of large-scale computing, including parallel processing on HPC (high performance computing) clusters,
• methods of artificial intelligence – machine learning in particular (including artificial neural networks) computed using heterogeneous multiprocessor systems and GPU architectures with possibility to use processing on cloud platforms
• statistical analyses of complex data, statistical modelling, development of inference procedures, statistical experiment design.

Application domains

There is universal demand that goes across application domains for better data processing that is able to extract vital information and that helps to understand the data. This leads to very wide spectrum of past and present collaborations – from commercial application of analyses of brain activity captured by neuroimaging technique to application of machine learning in cybersecurity. However, the fields of application that are the most significant, with repeated applications of scientific results and with long-term collaborations are

• energy – for example statistical modelling of natural gas consumption aimed to accounting applications, or application of models, and satellite and meteorological data for estimation of energy production from solar photovoltaics,
• environment – for example application of heat comfort models in high spatial resolution for urban planning or data analyses for air quality observation network,
• transportation – for example processing of traffic data from toll gates for free parking capacity estimation.

End users

Our know-how and experience is interesting to any companies and institutions that have to process large volumes of data and usual and easy-to-access methods are not sufficient. Typical problems in data processing include – date volume, data complexity, and also lack of in-house knowledge of more sophisticated state-of-the-art methods (that fall into domain of mathematical statistics, or artificial intelligence). The wide portfolio of our partners thus include technological companies, companies and institutions active in various fields of research and development, global corporations that want to stay in touch with recent advances and know-how of various fields of computer science, and also government and municipal institutions.