Multidisciplinary Research and Education on Big Data + High-Performance Computing + Atmospheric Sciences

Part of NSF Initiative on Workforce Development for Cyberinfrastructure (CyberTraining)

This website is about a new NSF-funded initiative in big data applied to atmospheric sciences and using high-performance computing as a vital tool. The training consists of instruction in the areas of data, computing, and atmospheric sciences supported by teaching assistants, followed by faculty-guided project research in a multidisciplinary team of participants from each area. Participating graduate students, post-docs, and junior faculty from around the nation will be exposed to multidisciplinary research experiences and have the opportunity for significant career growth.

News

2019/10, our 2020 online training, the last round from the project, is now open for application! Please check out its submission form at https://forms.gle/HELSLx1hsi25cHt16 and its flyer. Application is due on 1/1/2020. More info is at http://cybertraining.umbc.edu/2020.html.

2019/9, the extended version of CyberTraining 2018 team 4's technical report is accepted by Frontiers in Earth Science, Frontiers, 2019. Hybrid Causality Analysis of ENSO's Global Impacts on Climate Variables based on Data-driven Analytics and Climate Model Simulation.

2019/8, the extended version of CyberTraining 2018 team 3’s technical report has published: Peichang Shi, Qianqian Song, Janita Patwardhan, Zhibo Zhang, Jianwu Wang, and Aryya Gangopadhyay. A hybrid algorithm for mineral dust detection using satellite data. Proceedings of the 15th IEEE International Conference on e-Science (e-Science2019).

2019/5, congratulations to Pei Guo on receiving fellowship from UMBC Joint Center for Earth Systems Technology on causality analytics. More info is at JCET website.



Acknowledgement


Citation

Please cite our training program: Jianwu Wang, Matthias Gobbert, Zhibo Zhang, Aryya Gangopadhyay, Glenn Page. Multidisciplinary Education on Big Data + HPC + Atmospheric Sciences, In Proceedings of the Workshop on Education for High-Performance Computing (EduHPC-17) at SC'2017.


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