Collaborative Research: CyberTraining: Pilot: Cyberinfrastructure-Enabled Machine Learning for Understanding and Forecasting Space Weather

空间天气（SWx）是指从太阳到地球的空间环境瞬变现象。SWx影响着人类的生活，包括通信、交通、电力供应、国防、太空旅行等等。近十年来，理解和预测太阳猛烈喷发（SWx的来源）及其对地球的影响已成为国家的战略重点。网络基础设施（CI）是SWx研究中极其重要的一部分，因为每天都会从不同的来源生成许多tb级的数据。新泽西理工学院（NJIT）和蒙特克莱尔州立大学（MSU）之间的合作项目建立在国家科学基金会资助的CI平台上，用于共享基于CI的机器学习（ML）方法、工具和资源，用于SWx数据探索和事件预测。该项目将从美国国家科学基金会资助的CI平台开发中获得的技能和经验教训纳入课程课程。通过将研究成果和发现转化为教学模块，该项目培训潜在的ML专业人员开发先进的CI支持方法来理解、监测和预测SWx。新泽西理工大学和密歇根州立大学都是少数族裔服务机构，有充足的资源来支持代表性不足的学生。经验丰富的项目领导监督项目开发的多样性、公平性和包容性。该项目通过以下方式对CI培训做出了贡献：(1)为新的计算机科学研究生课程开发学习模块，(2)为学生提供机会，获得针对SWx问题实施机器学习解决方案的经验，(3)让学生了解机器学习作为服务的进展，可操作的近实时SWx预测系统，以及使用Binder支持的Zenodo存档开源ML工具的预测智能。(4)运用形成性和总结性方法评估教学和指导方法。主要研究人员与本科生合作开发CI资源，并提高支持CI的ML工具的可持续性。SWx对地球系统有着深远的影响。建立SWx的准备工作需要在几个方面做出大量努力，包括研究、预测和缓解计划。新课程培养研究生，使他们成为能够为SWx监测和预测分析做出贡献的CI专业人员。该项目为SWx研究人员提供培训，这在空间计划、无线电通信和电网安全等许多领域至关重要。该项目产生的知识在其他科学领域也有更广泛的应用。虽然这个项目规模很小，而且是试点项目，但它可以帮助解决新泽西州CI专业人员的需求。该奖项由美国国家科学基金会高级网络基础设施办公室颁发，由美国国家科学基金会数学和物理科学理事会（MPS）的天文科学部和美国国家科学基金会地球科学理事会（GEO）的研究、创新、协同和教育部门（RISE）共同支持。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Space weather (SWx) refers to the transients in the space environment traveling from the Sun to Earth. SWx affects the life of human beings, including communication, transportation, power supplies, national defense, space travel, and more. In the recent decade, tackling the difficult task of understanding and forecasting violent solar eruptions, which are sources of SWx, and their terrestrial impacts has become a strategic national priority. Cyberinfrastructure (CI) is an extremely important part of SWx research, as many terabytes of data are generated daily from different sources. This collaborative project between New Jersey Institute of Technology (NJIT) and Montclair State University (MSU) builds upon a National Science Foundation funded CI platform for sharing CI enabled machine learning (ML) methods, tools, and resources for SWx data exploration and event prediction. The project incorporates the skills and lessons learned from the development of the NSF funded CI platform into a course curriculum. By transforming research results and findings into teaching modules, the project trains potential ML professionals to develop advanced CI enabled methods for understanding, monitoring, and forecasting SWx. Both NJIT and MSU are minority serving institutions with ample resources to support underrepresented students. Experienced project leaders oversee diversity, equity, and inclusion efforts for the project development.This project makes contributions to CI training by (1) developing learning modules for a new computer science graduate course, (2) providing students with opportunities to gain hands on experience in implementing ML solutions for SWx problems, (3) exposing students to advances in machine learning as a service, operational near real time SWx forecasting systems, and predictive intelligence with Binder enabled Zenodo archived open source ML tools, and (4) assessing the teaching and mentoring methods using formative and summative approaches. The principal investigators work with undergraduate students to develop CI resources and improve the sustainability of CI enabled ML tools. SWx has a profound impact on the Earth system. Building the SWx readiness merits substantial efforts on several fronts, including research, forecast, and mitigation plan. The new course nourishes graduate students, preparing them to become CI professionals capable of contributing to SWx monitoring and predictive analytics in general. The project provides training of the workforce in SWx research, which is critically important in many areas such as safety of space programs, radio communications and power grids. Knowledge generated from the project also has broader applications in other areas of science. The program, while small and pilot, can help address the need of CI professionals in New Jersey.This award by the NSF Office of Advanced Cyberinfrastructure is jointly supported by the Division of Astronomical Sciences within the NSF Directorate for Math and Physical Sciences (MPS) and the Division of Research, Innovation, Synergies, and Education (RISE) within the NSF Directorate for Geosciences (GEO).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.