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Fangxin Fang is a Senior Research Fellow in the Department of Earth Science and Engineering at Imperial College London since 2017. His research focuses on predictive modeling, including machine learning, data assimilation methods, and optimal control strategies applied to geophysical models in environmental contexts such as ocean and atmospheric flows. He has worked on developing machine learning-based tools for real-time air pollution predictions and responses, investigating issues related to environmental hazards like flooding, and addressing renewable energy challenges. Fang has a robust background in postdoctoral research, having contributed to significant international projects and workshops in applied mathematics and climate change. He has presented plenary talks at international conferences and is actively involved in peer-review activities for numerous leading journals. Notably, his work has led to advancements in rapid numerical tools for urban air quality management and innovative modeling methodologies for nonlinear fluid dynamics.
Imperial College London • London, UK
Leading research efforts in predictive modeling and machine learning applications in environmental science.
Imperial College London • London, UK
Conducted research and developed methodologies in applied mathematics and data-driven modeling.
Laboratoire des Etudes Géophysiques et Océanographiques Spatiales • Toulouse, France
Engaged in advanced geophysical modeling research.
University of Auckland • Auckland, New Zealand
Assisted in research projects in civil and resource engineering.
Specialisms available in Materials for the Energy Transition or Theory and Simulation of Materials.