Licheng Liu, U Minnesota
Meet: https://meet.google.com/niy-gtpk-sro
YouTube Stream: https://youtube.com/live/61MK8csN_jE
Join group to receive calendar invite: https://groups.google.com/a/modelingtalks.org/g/talks
Abstract:
Ecosystems are central to the global carbon and nitrogen cycles, yet their complexity and heterogeneity pose major challenges to modeling efforts. While process-based models offer theoretical rigor grounded in biophysical and biochemical principles, they often face limitations in computational scalability, data assimilation, and structural uncertainty. Conversely, purely data-driven models can leverage rich sensing data streams but tend to lack generalizability and scientific interpretability. Knowledge-Guided Machine Learning (KGML) bridges these paradigms by embedding domain knowledge into machine learning frameworks to produce models that are both accurate and mechanistically informed. In this talk, I will introduce a series of applications that demonstrate how KGML improves simulations of carbon and nitrogen fluxes across agricultural and natural ecosystems. Use cases include modeling carbon budgets from croplands, advancing digital twin frameworks for sustainable agriculture, and predicting methane dynamics in natural ecosystems. By blending scientific priors with data-driven flexibility, KGML facilitates ecosystem modeling that is explainable, scalable, and climate-action-relevant. Ultimately, this approach supports more robust decision-making in environmental management and provides a pathway for developing AI systems that are physically consistent, data-efficient, and applicable to complex Earth system processes.
Bio:
Dr. Licheng Liu is a research scientist at the University of Minnesota and the lead of the KGML division in the NSF AI-LEAF Institute, and the AI for Nature Methane working group. His research integrates process-based modeling, machine learning, in-situ sensing, and remote sensing to understand biogeochemical dynamics in agricultural and natural ecosystems, with a focus on greenhouse gas emissions and climate feedbacks. His work spans carbon-nitrogen-water cycle modeling, AI-enhanced crop and soil simulations, and the development of open-source KGML frameworks for ecosystem prediction. Starting January 2026, Dr. Liu will join the University of Wisconsin–Madison as an Assistant Professor in the Department of Biological Systems Engineering.