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Hands-On Research and Training in High Performance Data Sciences, Data Analytics, and Machine Learning for Emerging Environments

  • Kwai Wong
  • Stanimire TomovEmail author
  • Jack Dongarra
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11887)

Abstract

This paper describes a hands-on Research Experiences for Computational Science, Engineering, and Mathematics (RECSEM) program in high-performance data sciences, data analytics, and machine learning on emerging computer architectures. RECSEM is a Research Experiences for Undergraduates (REU) site program supported by the USA National Science Foundation. This site program at the University of Tennessee (UTK) directs a group of ten undergraduate students to explore, as well as contribute to the emergent interdisciplinary computational science models and state-of-the-art HPC techniques via a number of cohesive compute and data intensive applications in which numerical linear algebra is the fundamental building block.

Keywords

Computational science Educational outreach Research Experiences for Undergraduates Data analytics Machine learning (ML) Hands-on experiences and education HPC 

Notes

Acknowledgments

This work was conducted at the Joint Institute for Computational Sciences (JICS), sponsored by the National Science Foundation (NSF), through NSF REU Award #1262937 and #1659502, with additional Support from the University of Tennessee, Knoxville (UTK), and the National Institute for Computational Sciences (NICS). This work used the Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by National Science Foundation grant number ACI-1548562. Computational Resources are available through a XSEDE education allocation award TG-ASC170031.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.University of TennesseeKnoxvilleUSA
  2. 2.Oak Ridge National LaboratoryOak RidgeUSA

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