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Licheng LIU
University of Minnesota Twin CitiesMeeting roles in:
Knowledge-Guided Graph Machine Learning Enables High-Resolution Nitrogen Transport Modeling in the Upper Mississippi River Basin
A Hybrid Modeling and Digital Twin Framework for Field-Scale Water and Nutrient Dynamics of Tile Drainage Systems
PyKGML: A Python Library for Efficient Development and Benchmark of Knowledge-Guided Machine Learning for Greenhouse Gas Modeling
Next-Generation Modeling of Global Natural Methane Fluxes: Integrating Multi-scale Observations with Knowledge-Guided Machine Learning
UAV4DCrop: An Ultra-High Resolution, Multi-Angle Benchmark UAV Dataset for Time-Series 3D Crop Reconstruction and Modeling
Bridging Spatial Gaps in Crop Yield Mapping to Support Climate-Resilient Agriculture via Knowledge-Guided Graph Neural Networks
Warming-Induced Emissions: Integrating Models and Observations to Advance Understanding of Greenhouse Gas Fluxes of Natural Systems and Climate Feedbacks I Poster
Minimal sampling, maximum insight: A deep learning BRIDGE framework for regional carbon assessment
Warming-Induced Emissions: Integrating Models and Observations to Advance Understanding of Greenhouse Gas Fluxes of Natural Systems and Climate Feedbacks II Oral
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