معرفی
Jakub Nowosad is a researcher affiliated with Adam Mickiewicz University in Poznań and currently holding a prestigious Marie Skłodowska-Curie Actions Postdoctoral Fellowship at the University of Münster's Remote Sensing and Spatial Modeling group (August 2024-August 2026). His work bridges geography, computer science, and environmental science with a focus on spatial data analysis and machine learning applications.
Nowosad's research interests center on spatial association methods, landscape metrics, information theory applications in geography, and spatial machine learning techniques. He has made significant contributions to developing and implementing computational methods for analyzing spatial patterns, particularly through R programming packages that address spatial autocorrelation challenges in machine learning. His work spans environmental monitoring, landscape ecology, and geospatial analysis with practical applications in understanding climate change impacts, forest fragmentation, and permafrost degradation.
His publication record demonstrates a strong focus on methodological development in spatial data science, with numerous recent publications on spatial machine learning frameworks, computational landscape ecology, and specialized software tools. Nowosad's work shows a clear trajectory toward integrating advanced machine learning techniques with traditional spatial analysis methods to overcome limitations in spatial prediction and pattern recognition.
- Marie Skłodowska-Curie Actions Postdoctoral Fellowship (MSCA-PF) for the PRISM project (PReservation and RecognItion of Spatial patterns using Machine learning)
Nowosad actively contributes to the open-source geospatial community through software development, including packages like spatialRF and contributions to spatial machine learning frameworks. His collaborative work spans multiple institutions including the University of Cincinnati, International Institute for Applied Systems Analysis, and various European research groups. He appears to be developing methodologies that will significantly impact how spatial patterns are recognized and preserved in environmental datasets, with applications ranging from Arctic landscape monitoring to forest conservation planning.
