CyberTraining: Implementation: Medium: Machine Learning Training and Curriculum Development for Earth Science Studies

Earth system science discoveries are increasingly affected by data management, analysis, and inference using powerful machine learning (ML) techniques. Yet, the skills required to perform these tasks, and training in cutting-edge, open-source technologies to build ML models and pipelines, big data, and cloud computing, are not covered by the traditional graduate curriculum in the geosciences.

To fill these gaps, this project will develop the GeoScience MAchine Learning Resources and Training (GeoSMART) framework that will build a foundation in open-source scientific ecosystems and general ML theory, toolkits, and deployment on Cloud computing platforms.

This project will include a team of geoscience and ML educators to create a novel ML curriculum with focus on seismology, cryosphere and hydrology applications. The training materials will be included in an enhanced curriculum that will broaden impact on emerging ML communities. The project’s implementation plan will provide training in open-source ML toolkits and data science skills.

Further, the project will cultivate the development of discipline-specific ML libraries, workflows, and communities of practice to sustain future growth of ML cybertraining opportunities. By building tools using open-source and cloud-accessible platforms, and by partnering with colleges and institutions that lack computing resources for ML workflows, the project will increase access to cybertraining materials and help to solve geoscience challenges.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.