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Multidisciplinary Education on Big Data + HPC + Atmospheric Sciences

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dc.contributor.authorWang, Jianwu
dc.contributor.authorGobbert, Matthias K.
dc.contributor.authorZhang, Zhibo
dc.contributor.authorGangopadhyay, Aryya
dc.contributor.authorPage, Glenn G.
dc.date.accessioned2018-09-19T20:03:38Z
dc.date.available2018-09-19T20:03:38Z
dc.date.issued2017-11-01
dc.description.abstractWe present a new initiative to create a training program or graduate-level course (cybertraining.umbc.edu) in big data applied to atmospheric sciences as application area and using high-performance computing as indispensable tool. The training consists of instruction in all three areas of "Big Data + HPC + Atmospheric Sciences" supported by teaching assistants and 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 and have the opportunity for significant career impact. The paper discusses the challenges, proposed solutions, practical issues of the initiative, and how to integrate high-quality developmental program evaluation into the improvement of the initiative from the start to aid in ongoing development of the program.en_US
dc.description.sponsorshipThis work is supported in part by the NSF Grant #1730250: CyberTraining: DSE: Cross-Training of Researchers in Computing, Applied Mathematics and Atmospheric Sciences using Advanced Cyberinfrastructure Resources. For co-author Matthias Gobbert, this material is based upon work supported while serving at the National Science Foundation. Any opinion, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.en_US
dc.description.urihttps://par.nsf.gov/biblio/10067778en_US
dc.format.extent8 pagesen_US
dc.genreconference papers and proceedings en_US
dc.genrepreprints
dc.identifierdoi:10.13016/M2KS6J78R
dc.identifier.citationWang Jianwu, Gobbert K. Matthias, Zhang Zhibo, Gangopadhyay Aryya, Page Glenn, Multidisciplinary Education on Big Data + HPC + Atmospheric Sciences, Proceedings of the Workshop on Education for High-Performance Computing (EduHPC-17) Proceedings of the Workshop on Education for High-Performance Computing (EduHPC-17), 2017en_US
dc.identifier.urihttp://hdl.handle.net/11603/11318
dc.language.isoen_USen_US
dc.publisherNational Science Foundationen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Physics Department
dc.relation.ispartofUMBC Faculty Collection
dc.relation.ispartofUMBC Student Collection
dc.relation.ispartofUMBC Mathematics and Statistics Department
dc.relation.ispartofUMBC Information Systems Department
dc.relation.ispartofUMBC Center for Accelerated Real Time Analysis
dc.relation.ispartofUMBC Computer Science and Electrical Engineering Department
dc.relation.ispartofUMBC Data Science
dc.relation.ispartofUMBC Joint Center for Earth Systems Technology (JCET)
dc.relation.ispartofUMBC Center for Real-time Distributed Sensing and Autonomy
dc.rightsThis item may be protected under Title 17 of the U.S. Copyright Law. It is made available by UMBC for non-commercial research and education. For permission to publish or reproduce, please contact the author.
dc.subjectBig Dataen_US
dc.subjectHigh-Performance Computingen_US
dc.subjectAtmospheric Sciencesen_US
dc.subjectMultidisciplinary Educationen_US
dc.subjectDevelopmental Evaluationen_US
dc.subjectUMBC High Performance Computing Facility (HPCF)en_US
dc.titleMultidisciplinary Education on Big Data + HPC + Atmospheric Sciencesen_US
dc.typeTexten_US

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