Large scale data analysis capability has become the key factor that is driving rapid progress in all fields of science. This project will significantly expand the high-performance computing resources at Brandeis University, by acquiring state of the art GPU computing machines that enable big data driven research. The new capability will support integrated research projects across a variety of disciplines, including the areas of Biology, Biochemistry, Chemistry, Computer Science, Math, Physics, and Psychology. It will also provide a platform at Brandeis University for training the next generation workforce to develop and apply deep learning techniques, thus accelerating discoveries in basic research and technological innovations. The project will enable Brandeis researchers and instructors to utilize modern computing techniques to broaden participation in science, technology, engineering, and mathematics (STEM) fields.

Specifically, the new resources will include 16 GPU nodes and 1 storage node on a 10Gbit network fabric to allow rapid internode communications. This project will enable Brandeis researchers to conduct big data driven convergence research that enhances our understanding of the Rules of Life in areas ranging from neuroscience to virus assembly, and addresses the fundamental problems underlying societal needs (e.g., green energy). The emerging interdisciplinary research activities will also create an inclusive environment for developing novel and more powerful big data driven techniques. The project will enable courses and workshops that train faculty, postdocs, graduate students, and undergraduate students to effectively use state of the art GPU computing and deep learning techniques. It will allow Brandeis to further integrate its education and research via a current NSF funded REU site at Brandeis Materials Research Science and Engineering Research Center. In addition, it will enhance Brandeis' ability to broaden participation in STEM, especially by women and underrepresented minorities, through several existing programs, including the NSF funded REU site, the Transitional Year Program for economically disadvantaged students, the local Society for Advancement of Chicanos/Hispanics and Native Americans in Science chapter at Brandeis, the Brandeis Science Posse Program, and the Brandeis Scientists in the Classroom Workshop.

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.

Agency
National Science Foundation (NSF)
Institute
Division of Advanced CyberInfrastructure (ACI)
Type
Standard Grant (Standard)
Application #
1920147
Program Officer
Alejandro Suarez
Project Start
Project End
Budget Start
2019-10-01
Budget End
2022-09-30
Support Year
Fiscal Year
2019
Total Cost
$349,983
Indirect Cost
Name
Brandeis University
Department
Type
DUNS #
City
Waltham
State
MA
Country
United States
Zip Code
02453