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.
Veronika Eyring, German Aerospace Center, Institute of Atmospheric Physic/University of Bremen, Germany
Earth system modeling and process-oriented model evaluation and analysis.
Jianwei Ma, Peking University, China
Deep learning, seismic exploration, inverse problems, geophysics, data assimilation.
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.
Devis Tuia, Ecole Polytechnique Fédérale de Lausanne, Switzerland
Earth observation, machine learning, ecology.
Laure Zanna, NYU, USA
Climate and ocean dynamics.
Editorial Board
Viviana Acquaviva, CUNY/Columbia University, USA
Artificial intelligence, risk and reliability, data science, uncertainty quantification.
Hamed Alemohammad, Clark University, USA
Remote sensing, artificial intelligence, geospatial analytics, land use land cover mapping.
Chengping Chai, Oakridge National Laboratory, USA
Machine learning, geophysical inversion, tomography, seismic and acoustic monitoring.
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.
Meng Gao, Hong Kong Baptist University, Hong Kong SAR, China
Machine Learning in atmospheric sciences.
Danfeng Hong, Chinese Academy of Sciences, China
Artificial intelligence, multimodal intelligent perception, foundation models, earth observation, earth science.
Karim Malik, University of Windsor, Canada
Deep learning, computer vision, GIScience, remote sensing, landcover modelling, change detection, snow cover in a changing climate.
Gianmarco Mengaldo, National University of Singapore
AI, explainable AI, dynamical and complex systems, computational mathematics, scientific computing, weather & climate, engineering.
Carlos Messina, University of Florida, USA
Agriculture, biotechnology, predictive breeding, dynamical systems, probabilistic programming.
Maria Molina, University of Maryland, USA
Machine learning, extremes, climate variability, climate change, signal extraction.
Ioannis Papoutsis, National Technical University of Athens, Greece
Earth system deep learning, earth observation, disaster management and spatio-temporal forecasting.
Maike Sonnewald, University of California Davis, USA
Oceanography, machine learning.
Ren Wang, Nanjing University of Information Science and Technology, China
Land-atmosphere interactions, hydroclimatology, global change, extreme climate, remote sensing.
Yang Zhao, Ocean University of China, China
Artificial intelligence, atmospheric water cycle (atmospheric rivers), atmospheric dynamics.
Zhonghua Zheng, The University of Manchester, UK
Urban Climate, Air Quality, Atmospheric Aerosols, Data Science, Data Engineering.
Journal Team
Ashley Gasque, Publisher
Ashley Gasque is Publisher of Machine Learning: Health, Machine Learning: Earth, and Machine Learning: Engineering at IOP Publishing. She brings nearly 20 years of experience in scholarly publishing, having led publishing programs in optical sciences, electromagnetics, radar, and engineering at Taylor & Francis/CRC Press and IOP Publishing. Throughout her career, she has overseen the publication of more than 300 scientific and technical books and now focuses on developing journals that advance machine learning research. In her current role, she works closely with the journals’ editorial boards to shape publishing strategy, support content development and ensure the journals reflect the evolving needs of the machine learning research communities they serve.
Freddie Taylor, Editor
As Editor, Freddie Taylor manages the peer review process for Machine Learning: Earth, working closely with the Editorial Board to ensure that all submissions receive a fair, rigorous, and timely assessment. He oversees editorial decision-making based on recommendations from the Editorial Board and independent expert reviewers, in accordance with the journal’s research integrity and peer review policies. Freddie has editorial experience across a range of environmental science journals at IOP Publishing, including the flagship journal Environmental Research Letters. He holds a Master’s degree in Chemistry.