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

空间天气(SWX)是指空间环境中从太阳到地球的瞬变。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。近十年来，解决了解和预报剧烈太阳喷发及其对地面影响的艰巨任务已成为国家战略重点。网络基础设施(CI)是SWX研究的一个极其重要的部分，因为每天从不同的来源产生数TB的数据。这个由新泽西理工学院(NJIT)和蒙特克莱尔州立大学(MSU)合作的项目建立在国家科学基金会资助的CI平台的基础上，该平台用于共享启用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。该项目将从国家科学基金会资助的传播与信息平台的开发中学到的技能和经验纳入课程课程。通过将研究结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启用方法，以了解、监控和预测SWX。NJIT和密歇根州立大学都是为少数族裔服务的机构，拥有充足的资源来支持代表性不足的学生。这个项目通过(1)为新的计算机科学研究生课程开发学习模块，(2)为学生提供在实施针对SWX问题的ML解决方案方面的实践经验的机会，(3)让学生接触到机器学习即服务、可操作的近实时SWX预测系统以及使用Binder Enabled Zenodo存档的开源ML工具的预测智能，以及(4)使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，并提高启用CI的ML工具的可持续性。SWX对地球系统产生了深远的影响。建立SWX战备状态需要在几个方面做出大量努力，包括研究、预测和缓解计划。这门新课程培养研究生，使他们成为能够对SWX监测和预测分析做出贡献的CI专业人员。该项目为工作人员提供SWX研究方面的培训，这在空间计划、无线电通信和电网安全等许多领域都至关重要。该项目产生的知识在其他科学领域也有更广泛的应用。该计划虽然规模较小，但可以帮助解决新泽西州对CI专业人员的需求。该奖项由NSF高级网络基础设施办公室颁发，由NSF数学和物理科学局(MPS)内的天文科学部和NSF地球科学局(GEO)内的研究、创新、协同和教育部(RISE)联合支持。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.