Ming Fan is a research scientist in the Computational Sciences and Engineering Division at Oak Ridge National Laboratory (ORNL). His research focuses on the intersection of machine learning, surrogate modeling, inverse modeling, uncertainty quantification, and high-performance computing. Ming collaborates with interdisciplinary teams of Earth and computational scientists to develop novel AI/ML methodologies, advancing the field of Earth sciences.
Links
Publications
Apr, 2026
Conference Paper
Adaptive Graph Learning with Transformer for Multi-Reservoir Inflow Prediction
Mar, 2026
Conference Paper
ORBIT-2: Scaling Exascale Vision Foundation Models for Weather and Climate Downscaling
Mar, 2026
Conference Paper
HydroDCM: Hydrological Domain-Conditioned Modulation for Cross-Reservoir Inflow Prediction
News
December 10, 2024
4 MIN READ
Calculations on Frontier earns Gordon Bell Prize, ORNL major presence at SC24
November 13, 2024
7 MIN READ
Fine-tuning forecasts: ORBIT brings long-range weather prediction within reach