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dc.contributor.authorGebraeel, Nagi
dc.date.accessioned2016-10-28T17:02:14Z
dc.date.available2016-10-28T17:02:14Z
dc.date.issued2016-09-06
dc.identifier.urihttp://hdl.handle.net/1853/55983
dc.descriptionPresented on September 6, 2016 at the Klaus Advanced Computing Building, Room 1116W, Georgia Institute of Technology.en_US
dc.descriptionSouth Big Data Innovation Hub ; Applications of Analytics and Machine Learning in Energy Industry-Academia Workshopen_US
dc.descriptionNagi Gebraeel is a Georgia Power Associate Professor in the Stewart School of Industrial & Systems Engineering at Georgia Tech. Dr. Gebraeel's research interests are in (1) equipment prognostics and diagnostics for improving reliability, maintainability, and availability by leveraging degradation-based sensor data streams, and (2) the integration of these results in subsequent maintenance, operational and logistical decision making. His specific focus is on tackling these problems in Big Data settings involving massive amounts of data streams and large equipment fleets. From the standpoint of application domains, Dr. Gebraeel has a general interest in the energy industry with a focus on power generation, and the manufacturing industry with a focus on discrete and continuous manufacturing. Dr. Gebraeel currently serves as an associate director at Georgia Tech's Strategic Energy Institute with the responsibility of identifying and promoting research activities and thought leadership at the intersection of Data Science and Energy. He is also the director of the Analytics and Prognostics Systems laboratory at Georgia Tech's Manufacturing Institute. He is a member of the Institute of Industrial Engineers (IIE), Institute for Operations Research and the Management Sciences (INFORMS), and The American Institute of Aeronautics and Astronautics (AIAA). He was the former president of the the IIE's Quality Control and Reliability Engineering Division. He received his MS and PhD from Purdue University in 1998 and 2003, respectively.en_US
dc.descriptionRuntime: 32:23 minutesen_US
dc.format.extent32:23 minutes
dc.language.isoen_USen_US
dc.publisherGeorgia Institute of Technologyen_US
dc.relation.ispartofseriesSouth Big Data Innovation Hub ; Applications of Analytics and Machine Learning in Energy Industry-Academia Workshopen_US
dc.subjectBig dataen_US
dc.subjectEnergyen_US
dc.titleBig Data Analytics in Energyen_US
dc.typePresentationen_US
dc.contributor.corporatenameGeorgia Institute of Technology. Institute for Data Engineering and Scienceen_US
dc.contributor.corporatenameUniversity of North Carolina at Chapel Hillen_US
dc.contributor.corporatenameGeorgia Institute of Technology. School of Industrial and Systems Engineeringen_US


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