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About

R. J. Debski is a computational scientist whose research centers on high-performance simulation-based optimization, trajectory planning, and real-time data processing for dynamic, real-world systems. His major contributions lie in developing efficient, adaptive algorithms that leverage heterogeneous and micro high-performance computing (HPC) systems to solve complex path and trajectory problems. Notably, his work on alpine ski racer trajectory optimization (2014, 12 citations) is recognized as one of the most general solutions published, demonstrating the power of simulation-based algorithms on heterogeneous computers. He has extended these principles to maritime applications, pioneering a multi-objective ship route optimization using an estimation of distribution algorithm (EDA) for non-stationary tidal environments (2024, 4 citations). Debski also addresses the challenge of streaming sensor data with real-time surrogate-assisted preprocessing (2022, 5 citations) and has explored optimal sailboat path search on micro HPC systems (2021, 6 citations). His work consistently bridges the gap between theoretical algorithm design and practical, computationally demanding applications in sports, navigation, and sensor analytics, showcasing a clear impact on both academic research and applied engineering.