The U.S. Department of Energy’s Office of Science, under the Advanced Scientific Computing Research (ASCR) program, released a funding opportunity announcement, DE-FOA-0003266, for $15 million to support the advancement of data reduction for science.
This research will explore potentially high-impact approaches to develop and use data reduction techniques and algorithms to facilitate more efficient analysis and use of massive data sets produced by observations, experiments, and simulation. These different types of sources are producing data at rates beyond current capacity to store, analyze, stream, and archive the data in raw form.
As a result, many research groups have begun reducing the size of their data sets via techniques such as compression, reduced order models, experiment-specific triggers, filtering, and feature extraction.
ASCR seeks to continue to increase the level of mathematical rigor in scientific data reduction to ensure that scientifically relevant constraints on quantities of interest are satisfied, methods can be integrated into scientific workflows, and methods are implemented in a manner that inspires trust that the desired information is preserved.
Data are ubiquitous in every scientific discipline, and they are foundational to the recent, current, and future advancements in scientific machine learning and artificial intelligence. Machine learning is particularly ripe for data reduction advances, as data reduction can improve the efficiency of learning, and machine learning techniques can be used to reduce data.
Pre-application Due Date: March 19, 2024, 5 p.m. Eastern time
Pre-application Response Date: April 2, 2024
Application Due Date: May 7, 2024, 11:59 p.m. Eastern time
Pre-applications are required and must be submitted by 5 p.m. Eastern time Tuesday, March 19, 2024.
More details on eligibility, pre-application requirements, and full application requirements are contained within the relevant funding announcement DE-FOA-0003266 and on the ASCR funding opportunities page.
# # #This work was supported by the U.S. Department of Energy’s Office of Science, through the Biological and Environmental Research program as part of the Atmospheric System Research program.