Michał Kucharzak

Michał Kucharzak is an active researcher engaged in multiple interdisciplinary research teams including the Machine Learning Team, Computer Networks Team, Advanced Data Analysis Methods Team, and Metaheuristics Team. He contributes to significant research projects such as Dark-Box Optimization and the development of evolutionary methods for multi-criteria optimization. His work focuses on optimization techniques applied to complex systems, particularly in network design and decision-making models.

His research interests span a wide range of topics in computational optimization and network technologies. These include metaheuristics, linear and integer programming, constraint programming, modeling and simulation, and advanced data analysis methods. He also investigates computer network architectures such as P2P, EON, FA, FCA, and NB-IoT, with a focus on application-aware and multi-layer network optimization.

The trends in his research, though not supported by explicit publications in the provided text, indicate a strong emphasis on algorithmic innovation for solving high-dimensional, multi-objective problems in both theoretical and applied domains. His work bridges operations research, computer science, and engineering applications.

• Involved in the AEON research project
• Active contributor to interdisciplinary research initiatives

There is no available information on formal advising roles or grant funding in the provided text. However, his involvement in multiple research teams suggests collaborative engagement across projects. He maintains a scientific presence through profiles such as ORCID, indicating ongoing scholarly activity.

Michał Kucharzak is part of dynamic research groups focused on cutting-edge computational methods. These teams work on developing novel optimizers, improving classifier training via multi-criteria approaches, and advancing evolutionary computation techniques for real-world applications in networking and decision support systems.