Faculty of Computer Science

Research areas

Scientific research conducted at the Software Department is focused on several main areas:

  • knowledge discovery in databases (KDD) and Data Mining: techniques based on artificial neural networks, evolutionary algorithms, Bayesian networks and fuzzy sets;
  • parallel and distributed computing: use of GPU acceleration, computer cluster and grid computing technologies, especially applied in data mining and biomedical informatics;
  • applications of computer science in biomedicine (bioinformatics, medical informatics): methods of image analysis and modelling, medical decision support systems;
  • software engineering;
  • digital image processing and analysis (including biometrics).

The topics and scope of research of individual staff members include:

  • data mining, in particular: classification, regression, feature selection based on CPL-type criterion function minimization techniques;
  • data mining (including genomics) and Big Data using, using various tools e.g., evolutionary algorithms and decision trees;
  • statistical, biostatistical and bioinformatic data analysis with a focus on genomic data mining resulting from Next Generation Sequencing experiments;
  • methods of analysis (both classical, statistical and exploratory) and data visualization;
  • applications of artificial intelligence in medicine, with particular emphasis on probabilistic graphical models;
  • construction of probabilistic models in medicine based on data and expert knowledge;
  • probabilistic graphical models; data analysis; discovering causality from data;
  • Big data analysis; flat pattern detection; knowledge extraction algorithms;
  • knowledge engineering;
  • decision support systems based on decision theory methods;
  • medical imaging;
  • classification of medical images based on textural feature analysis;
  • biomedical informatics; nuclear magnetic resonance; computer modeling; computer simulation; computer fluid mechanics;
  • biometric systems (automatic verification of human identity, techniques for automatic recognition of human identity), in particular methods of identification and verification of persons on the basis of behavioral characteristics;
  • parallel and distributed computing;
  • clustering algorithms; evolutionary algorithms;
  • software reuse, software production lines; identification of reusable components, similarity analysis in software families;
  • software clustering algorithms;
  • mobile applications supporting patient monitoring and psychotherapy;
  • hardware and software virtualization; technical and security aspects of cloud computing service;
  • geospatial data mining, including airborne laser scanning data (LiDAR) for environmental information.

The research is focused on applied mathematics. The following areas are dominating:

  • mathematical control theory
    • systems on time scales
    • systems of fractional order
    • controllability and observability of control systems
    • positive systems
    • systems with delays
  • calculus of variations and optimization
  • multi-agent systems with discrete and continuous time
  • machine learning
  • partial differential equations
  • mathematical models in biology and economics
  • the theory of non-commutative rings
  • abstract algebra

There are several separate research areas of our activity:

  • genetic algorithms and artificial intelligence methods in solving specific practical problems,
  • verification and specification of the properties of multi-agent systems,
  • the use of IT tools and information theory in study of capital markets, economics and finance,
  • data analysis methods and tools in social sciences,
  • noncommutative algebra and graph theory.

The research scopes of individual members of the Department cover the following issues:

  • route optimization of unmanned aerial vehicles in autonomous marine navigation systems,
  • application of artificial intelligence methods in the issues of survival data analysis,
  • application of artificial intelligence methods in transport issues,
  • graph algorithms in the optimization of vehicle fleet management,
  • satisfiability checking of strategic logic formulas applied to the verification and specification of the properties of multi-agent systems,
  • use of IT tools in capital market research, with emphasis on the market microstructure,
  • data analysis methods and tools in social sciences,
  • application of information theory in economics and finance,
  • negotiation theory,
  • behavioral aspects of decision making,
  • multi-criteria decision support,
  • application of comparative analysis methods in economics,
  • description of some classes of associative rings, including rings with differentiations,
  • description of the properties of rings in terms of ideals,
  • selected topics of the theory of semigroups,
  • Hopf algebras,
  • applications of finite groups in geometry,
  • group algebras of finite groups,
  • selected topics of algebraic graph theory,
  • analytical geometry in practice, offset curves and surfaces, approximation of offsets.

The main area of research and teaching activities carried out by the employees of the Department are issues correlated with biometrics, signal processing and analysis, in particular sound and image, using traditional methods as well as machine learning and artificial intelligence tools.

Another important area is the work related to digital technology and embedded systems, which are key in the Internet of Things industry. The whole is completed by the issues of human-computer communication, modern NUI interfaces (Natural User Interface) and Augmented Reality. The above-mentioned issues are very important elements of the Industry of the Future, also known as Industry 4.0.

Research topics conducted at the Department of Digital Media and Computer Graphics focuses on several main trends:

  • Biometrics; Recognition of people based on the methods of analyzing biometric data, body parts and signals collected by various devices;
  • Analysis of medical images (including the detection of lesions at various stages). Artificial intelligence and machine learning techniques in image classification;
  • Digital signal processing and analysis; audio signal processing techniques; object detection and classification in image processing;
  • Speech processing, with particular emphasis on the processing and coding of the speech signal; Signal decomposition techniques and block transforms;
  • Use of machine learning tools in the processing of audio and image signals.

The topics and scope of research of individual employees include:

  • Expert systems. Knowledge representation; Processing uncertain knowledge. Inference methods under uncertainty;
  • Embedded systems on programmable circuits, finite state machine synthesis, design of control systems, high-level design of digital systems;
  • Robotics; Identification of objects and surroundings; Topological navigation algorithms based on an integrated data collection system;
  • Applications of image processing and recognition techniques, incl. automatic recognition of biomarker concentration (SPRi), recognition of dangerous objects in X-ray scanner images, recognition of objects on the road of the car;
  • The issues relate, inter alia, to classification of objects using texture features, R-CNN network, key point detection and segmentation algorithms.
  • Signal processing with the use of quaternions algebra and deep learning with particular emphasis on the series of time sensors of motion;
  • Application of machine learning algorithms, including deep learning, to automatically characterize spatial audio recordings;
  • Speech signal analysis using distorted transforms and signal subspace methods; Single-channel speech signal conditioning systems; Estimating noise in acoustic signals; Use of psychoacoustic phenomena in the processing of the speech signal;
  • Use of fuzzy random forests based on cluster models – fuzzy decision trees; Gesture recognition and issues related to drones and image analysis;
  • Designing control systems, in particular sequence systems based on programmable CPLD / FPGA systems;
  • Methods of logical synthesis, optimization of the footprint, speed of operation and consumed power;
  • Machine learning tools, cloud technologies and Cognitive Services in the process of codifying organizational knowledge and knowledge management;
  • Identification of dynamic systems: continuous, discrete, linear and nonlinear. Selection of optimal and friendly control signals in the task of identifying dynamic systems;
  • Technical information technology and telecommunications, with particular emphasis on the design of embedded systems and the design of digital circuits based on programmable CPLD / FPGA circuits;
  • “Information technology and telecommunications”, with particular emphasis on behavioral biometrics and the use of IMU (Inertial Measurement Unit) measuring sensors.

The scientific work conducted in the Department of Information Systems and Computer Networks includes:

  • development of artificial intelligence methods, including:
    • recommender systems,
    • conversation systems,
    • generation of music with specific emotions,
    • granular computing and Polish School of Artificial Intelligence – methods of rough sets.
  • processing methods of uncertain and incomplete information,
  • data mining and analysis,
  • artificial immune systems and their use in security of computer systems and networks, and data classification,
  • an application of transparent tests for RAM in the BIST schemes,
  • using FPGAs for data mining.
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