Argonne is now accepting proposals for the ALCF Data Science Program (ADSP). The program, which currently supports eight projects, allocates computing time and supporting resources to research teams focused on using the ALCF's leadership-class systems and infrastructure to explore, demonstrate, and improve a wide range of data and learning techniques. These techniques include uncertainty quantification, statistics, machine learning, deep learning, databases, pattern recognition, image processing, graph analytics, data mining, real-time data analysis, and complex and interactive workflows. ADSP proposals undergo a review process to evaluate potential impact, data-scale readiness, diversity of science domains and algorithms, and other criteria. ADSP projects are two-year awards.

The Argonne Leadership Computing Facility (ALCF), a U.S. Department of Energy (DOE) Office of Science User Facility, is now accepting proposals for the ALCF Data Science Program (ADSP). Launched in 2016, the ADSP is targeted at "big data" science problems that require the scale and performance of leadership computing resources, such as the ALCF's two petascale supercomputers: Mira, an IBM Blue Gene/Q system, and Theta, an Intel-Cray system. From April 27 to June 20, 2018, the ADSP open call provides an opportunity for researchers to submit proposals for projects that will employ advanced data science and machine learning techniques to gain insights into very large datasets produced by experimental, simulation, or observational methods. The program, which currently supports eight projects, allocates computing time and supporting resources to research teams focused on using the ALCF's leadership-class systems and infrastructure to explore, demonstrate, and improve a wide range of data and learning techniques. These techniques include uncertainty quantification, statistics, machine learning, deep learning, databases, pattern recognition, image processing, graph analytics, data mining, real-time data analysis, and complex and interactive workflows.