{"id":535,"date":"2024-09-06T10:30:36","date_gmt":"2024-09-06T08:30:36","guid":{"rendered":"https:\/\/site.uit.no\/ccgrid2025\/?page_id=535"},"modified":"2024-09-06T10:40:33","modified_gmt":"2024-09-06T08:40:33","slug":"professor-anne-c-elster","status":"publish","type":"page","link":"https:\/\/site.uit.no\/ccgrid2025\/professor-anne-c-elster\/","title":{"rendered":"Professor Anne C. Elster"},"content":{"rendered":"\n
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Professor<\/strong><\/h2>\n\n\n\n

Anne C. Elster<\/strong><\/h2>\n\n\n\n
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Personal Page<\/a><\/h6>\n<\/div>\n\n\n\n
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HPC and Geophysical Forecasting<\/strong><\/h2>\n\n\n\n

Abstract<\/h4>\n\n\n\n

Geophysical forecasting offers the opportunity to leveraging some of the cutting-edge technologies from the oil and gas sector to improve, for instance, geohazard monitoring and forecasting sudden events along roads and railways. This also includes the use new methods for monitoring and mapping life and geophysical events at sea and near the seabed. Modern seismic sensors and DAS (Distributed Acoustic Sensing)  systems also generate vast datasets we will need both AI and HPC techniques to fully make use of. These tasks thus offer many interesting research challenges related to parallel and distributed computing the next several years<\/em>.<\/p>\n\n\n\n

This talk will highlight some of the ongoing work my group is involved in at The Center for Geophysical Forecasting at NTNU. This includes discussing some of our work related to utilizing AI and HPC techniques such as autotuning and GPU-assisted compression, and how these can impact HPC applications.<\/em><\/p>\n\n\n\n

Bio<\/h4>\n\n\n\n

Anne C. Elster<\/strong> is a Professor and the Director of Heterogeneous and Parallel Computing Lab (HPC-Lab) at the Dept. of Computer Science, Norwegian Univ. of Science and Technology (NTNU), a HPC Leader at the Center for Geophysical Forecasting at NTNU, and a Senior Research Fellow \/ Visitor at The Oden Institute. Anne currently also serves as a faculty representative on both the NTNU Board and NTNU\u00b4s College (Faculty) of Information Technology and Electrical Engineering Board.<\/p>\n\n\n\n

She received her Bachelor of Computer Systems Engineering from UMass Amherst, and her Master and PhD degrees in Electrical Engineering from Cornell. Before joining NTNU in 2001 she worked at Schlumberger Austin Research as well as an Adjunct faculty member at UT Austin where she taught courses in Algorithms and Operating Systems and helped with a graduate course in Partial Differential Equations.<\/p>\n\n\n\n

Anne\u00b4s current research includes developing methods and tools for parallelizing, optimizing and auto-tuning codes targeting heterogeneous computing systems. She and her research group are especially known for work on GPU accelerations dating back to 2006. <\/p>\n\n\n\n

Anne has advised over 100 master students as well as several PhDs and Postdocs in the area parallel and GPU computing, as well as served on PhD evaluation committees in Czech Republic, Denmark, Finland, Italy, Saudi Arabia (KAUST), and Spain. She is also credited for her Linear Bit-reversal algorithm and served on the original MPI Standards Committee. She is an Associate Editor of IEEE CiSE and her served on numerous program committees, including the Sid Fernbach and Test-of-Time Awards committees. Anne is an IEEE Computer Society Distinguished Contributor Charter Member and a Distinguished speaker for IEEE CS (2019-2022).<\/p>\n\n\n\n

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Google Scholar Page<\/a><\/div>\n<\/div>\n<\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

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