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Machine Learning Technologies and Their Applications for Science and Engineering Domains Workshop -- Summary ReportThe fields of machine learning and big data analytics have made significant advances in recent years, which has created an environment where cross-fertilization of methods and collaborations can achieve previously unattainable outcomes. The Comprehensive Digital Transformation (CDT) Machine Learning and Big Data Analytics team planned a workshop at NASA Langley in August 2016 to unite leading experts the field of machine learning and NASA scientists and engineers. The primary goal for this workshop was to assess the state-of-the-art in this field, introduce these leading experts to the aerospace and science subject matter experts, and develop opportunities for collaboration. The workshop was held over a three day-period with lectures from 15 leading experts followed by significant interactive discussions. This report provides an overview of the 15 invited lectures and a summary of the key discussion topics that arose during both formal and informal discussion sections. Four key workshop themes were identified after the closure of the workshop and are also highlighted in the report. Furthermore, several workshop attendees provided their feedback on how they are already utilizing machine learning algorithms to advance their research, new methods they learned about during the workshop, and collaboration opportunities they identified during the workshop.
Document ID
Acquisition Source
Langley Research Center
Document Type
Technical Memorandum (TM)
Ambur, Manjula
(NASA Langley Research Center Hampton, VA, United States)
Schwartz, Katherine G.
(Georgia Inst. of Tech. Atlanta, GA, United States)
Mavris, Dimitri N.
(Georgia Inst. of Tech. Atlanta, GA, United States)
Date Acquired
January 20, 2017
Publication Date
December 1, 2016
Subject Category
Documentation And Information Science
Report/Patent Number
Funding Number(s)
WBS: WBS 736466.
Distribution Limits
Public Use Permitted.
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