Peter Gnip

Peter Gnip is an Assistant Professor at the Technical University of Košice. He teaches courses such as Formal Languages (FJ) and conducts laboratory exercises in computer science-related subjects. His research focuses on bankruptcy prediction and imbalanced data classification, with a strong emphasis on machine learning methodologies. He has published extensively on ensemble learning, imbalanced datasets, and financial risk analysis. Notable projects include studies on TabNet vs. XGBoost for imbalanced data and genetic algorithm-optimized autoencoders for bankruptcy prediction. No scientific awards are explicitly listed, but his work contributes significantly to financial and computational research fields. He advises no listed students and has undefined grant activities. His teaching and research are centered at TUKE, with office PK6 Room 011-1.