Convergence of Deep Learning and High Performance Computing: A Paradigm Shift for Multi-Messenger Astrophysics

Huerta Escudero, Eliu Antonio

Title:

Convergence of Deep Learning and High Performance Computing: A Paradigm Shift for Multi-Messenger Astrophysics

dc.contributor.author	Huerta Escudero, Eliu Antonio
dc.contributor.corporatename	Georgia Institute of Technology. Institute for Data Engineering and Science	en_US
dc.contributor.corporatename	University of Illinois at Urbana-Champaign	en_US
dc.contributor.corporatename	National Center for Supercomputing Applications	en_US
dc.date.accessioned	2019-06-26T21:00:45Z
dc.date.available	2019-06-26T21:00:45Z
dc.date.issued	2019-06-11
dc.description	Presented on June 11, 2019 at 12:15 p.m. in the Georgia Tech Hotel and Conference Center, Georgia Institute of Technology.	en_US
dc.description	The 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.description	PLENARY TALK - Eliu Antonio Huerta Escudero is the head of the Gravity Group at the National Center for Supercomputing Applications (NCSA) at the University of Illinois at Urbana-Champaign. NCSA provides supercomputing and advanced digital resources for the nation’s science enterprise. At NCSA, University of Illinois faculty, staff, students, and collaborators from around the globe use advanced digital resources to address research grand challenges for the benefit of science and society. NCSA has been advancing one-third of the Fortune 50 for more than 30 years by bringing industry, researchers, and students together to solve grand challenges at rapid speed and scale. Huerta’s expertise lies at the interface of analytical and numerical general relativity, boosted with innovative applications of machine learning and deep learning, and has as a unifying thread in the use of innovative hardware architectures and advanced cyber-infrastructure facilities, in particular, the Blue Waters supercomputer, to create scenarios for multimessenger astrophysics.	en_US
dc.description	Runtime: 43:48 minutes	en_US
dc.format.extent	43:48 minutes
dc.identifier.uri	http://hdl.handle.net/1853/61460
dc.language.iso	en_US	en_US
dc.publisher	Georgia Institute of Technology	en_US
dc.relation.ispartofseries	IDEaS Conferences
dc.relation.ispartofseries	Machine Learning in Science and Engineering
dc.subject	Cyber-infrastructure	en_US
dc.subject	Deep learning	en_US
dc.subject	High performance computing	en_US
dc.subject	Machine learning	en_US
dc.title	Convergence of Deep Learning and High Performance Computing: A Paradigm Shift for Multi-Messenger Astrophysics	en_US
dc.title.alternative	Machine Learning in Science and Engineering Conference - Convergence of Deep Learning and High Performance Computing: A Paradigm Shift for Multi-Messenger Astrophysics	en_US
dc.type	Moving Image
dc.type.genre	Presentation
dspace.entity.type	Publication
local.contributor.corporatename	Institute for Data Engineering and Science
local.relation.ispartofseries	IDEaS Conferences
relation.isOrgUnitOfPublication	2c237926-6861-4bfb-95dd-03ba605f1f3b
relation.isSeriesOfPublication	e73bf74b-8831-41fd-b64b-82ce60a15c9f