Bohan Chen

Bohan Chen is a Postdoctoral Scholar Research Associate in the Department of Computing and Mathematical Sciences. Their work focuses on advancing graph-based machine learning techniques and their applications in environmental science, remote sensing, and AI-driven analysis.

Research interests include graph neural networks, active learning strategies, hyperspectral image analysis, and knowledge graph integration. Notable contributions include the development of the GLL layer for neural networks, the CUSP permafrost dataset, and hybrid models for multispectral image processing.

Key research trends span theoretical advancements in graph-based learning and practical applications such as environmental monitoring, SAR data analysis, and pandemic modeling. Their work bridges computational methodologies with real-world environmental and societal challenges.

Advising and grants: No student advisees listed. Contributions include foundational research without explicit grant mentions in provided text.

Labs/teams: No specific lab or team affiliations mentioned in the text.