Šlapak Eugen

Šlapak Eugen is an Assistant Professor at the Technical University of Košice. His research focuses on autonomous driving systems, edge computing, and network optimization, with a particular emphasis on applying neural networks and blockchain technologies in vehicular and 5G networks.

Eugen Šlapak holds a PhD in [specific field not explicitly stated, likely Engineering/Computer Science] and an Ing. (engineer) degree. His academic background combines technical expertise in telecommunications and computer science.

His research interests span autonomous driving technologies, including simulation and control systems, as well as edge computing and metaverse integration. He also explores blockchain applications in vehicular networks, resource allocation in 5G and beyond, and the use of graph neural networks for network optimization. His work intersects machine learning, robotics, and telecommunications to address challenges in modern communication systems and intelligent transportation.

Recent publications highlight advancements in neural radiance fields for industrial robotics, distributed edge video compression for autonomous driving, and blockchain-based resource allocation in connected vehicles. Earlier work includes optimization of UAV-assisted networks and HetNet topology design using machine learning clustering methods.

While no formal awards are listed, his contributions to vehicular networks, edge computing, and AI-driven network design reflect significant scholarly impact. Advising details are not documented here, but his research collaborations likely involve cross-disciplinary teams focusing on autonomous systems and 5G infrastructure.

No lab affiliations or teams are explicitly mentioned, though his teaching role in the course Stochastické modelovanie a analýza dát (SMaAD) suggests involvement in data analysis and stochastic modeling initiatives.