Editorial board
Scientific leadership of Machine Learning: Earth is provided by the Editor-in-Chief and will be supported by an Editorial Board with broad scientific and geographical distribution.
Machine Learning: Earth is currently making appointments to the Editorial Board and this page will be updated accordingly in due course. Through the process of Board Member appointment we strive for scientific, gender, geographic, and ethnic diversity, and welcome nominations from the community. For further information please contact our Publishing team: mlearth@ioppublishing.org
Editor-in-Chief
Pierre Gentine, Columbia University, USA
Pierre Gentine is a Professor in the department of Earth and Environmental Engineering and in the department of Earth and Environmental Sciences. He is director of the National Science Foundation Science and Technology Center “Learning the Earth with Artificial intelligence and Physics” and a director of the Graduate Program in Earth and Environmental Engineering. Dr. Gentine and his group investigate the multiscale nature of the continental hydrologic and carbon cycle, with observations (remote sensing and in situ), models and machine learning
Executive Editorial Board
William Collins, Lawrence Berkeley National Laboratory and The University of California, Berkeley, USA
Application of machine learning emulators to climate extremes, fast and slow feedbacks in the climate system.
Jianwei Ma, Peking University, China
Deep learning, seismic exploration, inverse problems, geophysics, data assimilation.
Laure Zanna, NYU, USA
Climate and ocean dynamics.
Chaopeng Shen, Pennsylvania State University, USA
Deep learning-based water resources prediction, flood/soil moisture forecasting, water temperature modeling, plant/ecosystem modeling, water quality prediction, physics-informed machine learning, differentiable hydrology, scientific machine learning.
Editorial Board
Chengping Chai, Oakridge National Laboratory, USA
Machine learning, geophysical inversion, tomography, seismic and acoustic monitoring.
Danfeng Hong, Chinese Academy of Sciences, China
Artificial intelligence, multimodal intelligent perception, foundation models, earth observation, earth science.
Viviana Acquaviva, CUNY/Columbia University, USA
Artificial intelligence, risk and reliability, data science, uncertainty quantification.
Carlos Messina, University of Florida, USA
Agriculture, biotechnology, predictive breeding, dynamical systems, probabilistic programming.
Dan Fu, Texas A&M University, USA
Deep learning/AI in weather and climate applications, high-resolution global and regional climate modeling, seasonal-to-decadal climate predications.
Yang Zhao, Ocean University of China, China
Artificial intelligence, atmospheric water cycle (atmospheric rivers), atmospheric dynamics.
Maike Sonnewald, University of California Davis, USA
Oceanography, machine learning.
Zhonghua Zheng, The University of Manchester, UK
Urban Climate, Air Quality, Atmospheric Aerosols, Data Science, Data Engineering.