The continuing rapid growth of the volume of data generated by various sources implies the need for efficient methods of processing and acquiring knowledge. Data must be structured and stored in the right way; it must be possible to efficiently search through the data and analyze it to distil knowledge that could not be distilled without sophisticated methods and algorithms. In our research we use methods inspired by the living nature to process large collections of weakly structured data, extract knowledge from the data or, for example, reduce the dimension of high-dimensional data.
By studying natural phenomena, these methods (bio-inspired methods, soft-computing methods, etc.) allow processes and theoretical models to develop computational systems and algorithms capable of solving complex problems that cannot be solved (or with difficulties) by traditional techniques.
Our research also covers the area of formal methods with focus on knowledge-based approach to the creation of software systems. The key topics in this area include the development of methods for effective creation and verification of software systems, utilization of formal methods for the specification of software process and effective management of such process.
- Biomedical applications when measuring and processing large-volume data, e.g.:
- signal processing with primary emphasis on signals of biological origins
- modelling and verifying complex processes when planning radio-therapy
- Intelligent networks (Smart Grids)
- sustainable development – resource optimization and active management of energy flows in smart grids with emphasis on increasing the positive impact on the environment and social level of the population
- optimization of sophisticated control methods for technological systems based on the analysis of biological processes
- Massively parallel computing on various technological platforms.