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Research Spending & Results

Award Detail

Doing Business As Name:University of Maine
  • Roy M Turner
  • (207) 581-3909
  • Jean-Sebastien Senecal
  • Peter O Koons
  • Bruce E Segee
  • Huijie Xue
Award Date:09/17/2019
Estimated Total Award Amount: $ 350,000
Funds Obligated to Date: $ 350,000
  • FY 2019=$350,000
Start Date:10/01/2019
End Date:09/30/2022
Transaction Type:Grant
Awarding Agency Code:4900
Funding Agency Code:4900
CFDA Number:47.070
Primary Program Source:040100 NSF RESEARCH & RELATED ACTIVIT
Award Title or Description:MRI: Acquisition of a high-performance computing instrument to support deep learning, modeling/simulation, and visualization for STEM
Federal Award ID Number:1919478
DUNS ID:186875787
Parent DUNS ID:071750426
Program:Major Research Instrumentation
Program Officer:
  • Rita Rodriguez
  • (703) 292-8950

Awardee Location

Street:5717 Corbett Hall
Awardee Cong. District:02

Primary Place of Performance

Organization Name:University of Maine
Street:5717 Corbett Hall
Cong. District:02

Abstract at Time of Award

This project, acquiring an instrument for high performance computing, aims to support computational needs for deep learning, modeling, and visualization projects. The instrument is expected to drastically speed up projects that are now limited by available computational resources, as well as enable projects to be undertaken that currently cannot be done with existing resources in a reasonable amount of time, if at all. Deep learning training times can expect several orders of magnitude improvement, as can simulation and modeling, data mining, and preparation of visualizations and demonstrations. Deep learning models and scientific simulations that are too large to be run on current campus computing resources will be possible with the instrument. The following contributions are expected in this state, which is part of the Established Program to Stimulate Competitive Research (EPSCoR): - Scientific advances - for example, in understanding the influence of large-scale circulation patterns on physical structures and ecological habitats; the increased use of deep learning in STEM projects; - Emergence of new forms of understanding of data and models via emerging visualization techniques; - Outreach to K-16 education and the public through web-based dynamic visualizations; - Training of undergraduate and graduate students across science, technology, engineering, and mathematics (STEM) disciplines using the methodologies mentioned as well as the use of advanced computational tools; and - Enhanced access to computational resources in underserved rural communities throughout Maine. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

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