CyberTraining: Pilot: Machine Learning Foundations and Applications in the Earth Systems Sciences

The increasing use of machine learning techniques in Earth Systems Science (ESS) has positive impacts on science and engineering research, but ease of use coupled with the opaque nature of many commonly available tools can lead to trust without critical assessment of the tools' outputs. Education surrounding the theoretical underpinnings of machine learning tools requires additional, and often substantive, coursework in advanced math and programming. Cyberinfrastructure users without access to this background information may have difficulty building an appreciation and understanding of the utility and appropriateness of machine learning technology for their research and career goals, especially when they attend under-resourced institutions that cannot create relevant educational materials on their own. This project intends to help university-level learners build necessary cyberinfrastructure literacy and skills that will allow them to appropriately apply machine learning techniques to their ESS research without requiring prohibitive additional coursework. These future cyberinfrastructure users benefit from practicing appropriate and ethical usage of machine learning techniques even when the tools in use were developed by others. The goals of this project are to elucidate the conceptual mechanisms behind machine learning models for university-level ESS students and early-career professionals, and to bridge the gap between machine learning concepts and low-code, real-world applications in the Earth Systems Sciences. It will accomplish this by providing a series of three learning modules: (1) a self-paced conceptual introduction that uses a systems-thinking approach to understanding how machine learning works in ESS, (2) a self-paced, low-code module that enables learners to apply the conceptual frameworks to real world scenarios with relevant ESS data, and (3) a lab-based activity that promotes group discussion, justification of decision making, and critical analysis of machine learning techniques and outputs. This design lends itself well to a flipped classroom setting integrated with existing curricula, allowing learners to practice these skills without the need to take on additional coursework. Additionally, the program fosters critical judgment skills in ESS cyberinfrastructure users, furthering the broad, strategic, ethical, and appropriate usage of cyberinfrastructure and machine learning. This award by the Office of Advanced Cyberinfrastructure is jointly supported by the Directorate for Geosciences. 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.