Department of Artificial Intelligence is preparing developers of advanced machine intelligence and other technologies that embody science fiction in the reality. Its mission is to produce students with a sound understanding of the fundamentals of the theory and practice of Artificial Intelligence and Machine Learning. The mission is also to enable students to become leaders in the industry and academia nationally and internationally. The curriculum for the Department of Artificial Intelligence is designed to reach the following goals.

  • Acquire the knowledge and skills for developing various intelligent information systems through a basic grasp of computer science and information processing technology.

  • Develop a novel technique of intelligent information processing in which computers collaborate with human beings, by learning various technologies in intelligent information processing.

  • Master the fundamentals of mathematics and natural science.



Anatoliy Melnyk, Professor, PhD, D.Sc., Head of the Department of Artificial Intelligence.

Field of interest: Computer Architecture, All-Purpose and Specialized Computer Systems Design, Cyber-Physical Systems and IoT, Self-Configurable and Self-Improvable Computer Systems, High-Level Automatic Design Tools.

Current research: Application of machine learning to computer architecture design and analysis. Self-configurable and self-improvable computer systems architecture based on the Artificial Intelligence technologies and tools for specialized computer system automatic design. Methods and computer tools for signal processing and image analysis in cyber-physical systems and the Internet of Things.

International cooperation: Institute of Computer Technologies, Automation and Metrology, Lviv Polytechnic National University, Ukraine.

e-mail: aomelnyk [at]

Ryszard Kozera, PhD, D.Sc.

Fields of interest: Computer Vision and Image Processing, Computer Graphics, Interpolation, Numerical Analysis, Optimization, Artificial Intelligence.

Current research: Fitting Reduced Data with Cubic Splines, Surface Reconstruction from Photometric Stereo, Noise Removal with Different Optimization Schemes, Classification of Bacteria from Microscope Images.

International cooperation: University of Western Australia and The University of Adelaide (Australia)

e-mail: ryszard.kozera [at]

Michał Dolecki, PhD

Fields of interest: artificial intelligence, machine learning, cryptography, programming.

Current research: Analysis and the application of artificial neural networks and genetic algorithms. Using machine learning methods to support and improve the work of developers. Cryptographic key exchange using The Tree Parity Machine artificial neural networks.

e-mail: michal.dolecki [at]

Magdalena Wilkołazka, MSc

Fields of interest: Computer Networks, Mathematics, Optimization.

Current research: Modelling curves via reduced data interpolation, exploration of the convergence order for trajectory and length of curves. Practical application for this type interpolation.

e-mail: magda8310 [at]

Sara Jurczyk-Zielińska, MSc.

Fields of interest: Programming, Theoretical Physics.

Current research: The 3-source photometric stereo.

e-mail: sarajurczyk [at]

Joanna Wasiura-Maślany, MSc

Fields of interest: artificial intelligence, machine learning, deep learning, programming, databases, probability
Current research: Almost sure random limit theorems. Machine learning methods in data mining processes.
e-mail: wasiuraj [at]

Zofia Marek, MSc

Fields of interest:  Numerical Analysis Algorithms, Modeling and Computer Simulations.

e-mail: zmarek [at]