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dc.contributor.authorThomson, Ross
dc.date.accessioned2019-06-27T15:40:50Z
dc.date.available2019-06-27T15:40:50Z
dc.date.issued2019-06-12
dc.identifier.urihttp://hdl.handle.net/1853/61463
dc.descriptionPresented on June 12, 2019 at 2:00 p.m. in the Georgia Tech Hotel and Conference Center, Georgia Institute of Technology.en_US
dc.descriptionThe second-annual Machine Learning in Science and Engineering (MLSE) Conference highlights advances in research that utilize methods of artificial intelligence, the development of new machine learning algorithms designed for science and engineering problems, and the ways these methods lead to innovations across various fields. Researchers from academia, government, and industry will gather to explore the future of research in science and engineering.en_US
dc.descriptionPLENARY TALK - Ross Thomson is trained as a computational physicist and has worked in a broad range of academic and industry fields, from micro-gravity fluid simulation for NASA to “computation advertising” at Google. Currently, he works as a solutions architect for Scientific Computing at Google Cloud Platform.en_US
dc.descriptionRuntime: 45:23 minutesen_US
dc.format.extent45:23 minutes
dc.language.isoen_USen_US
dc.publisherGeorgia Institute of Technologyen_US
dc.relation.ispartofseriesIDEaS Conferences ; Machine Learning in Science and Engineeringen_US
dc.subjectMachine learningen_US
dc.titleTools and Methods for Machine Learningen_US
dc.title.alternativeMachine Learning in Science and Engineering Conference - Tools and Methods for Machine Learningen_US
dc.typeLectureen_US
dc.typeVideoen_US
dc.contributor.corporatenameGeorgia Institute of Technology. Institute for Data Engineering and Scienceen_US
dc.contributor.corporatenameGoogle (Firm)en_US


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