Skip directly to content

Minimize RSR Award Detail

Research Spending & Results

Award Detail

Awardee:UNIVERSITY OF CONNECTICUT
Doing Business As Name:University of Connecticut
PD/PI:
  • Bing Wang
  • (860) 486-0582
  • bing@uconn.edu
Co-PD(s)/co-PI(s):
  • Sanguthevar Rajasekaran
  • Chuanrong Zhang
  • Wei Wei
  • Suining He
Award Date:09/13/2021
Estimated Total Award Amount: $ 299,362
Funds Obligated to Date: $ 299,362
  • FY 2021=$299,362
Start Date:10/01/2021
End Date:09/30/2023
Transaction Type:Grant
Agency:NSF
Awarding Agency Code:4900
Funding Agency Code:4900
CFDA Number:47.070
Primary Program Source:040100 NSF RESEARCH & RELATED ACTIVIT
Award Title or Description:CyberTraining: Pilot: Cyberinfrastructure Training in Computer Science and Geoscience
Federal Award ID Number:2118102
DUNS ID:614209054
Parent DUNS ID:004534830
Program:CyberTraining - Training-based
Program Officer:
  • Alan Sussman
  • (703) 292-7563
  • alasussm@nsf.gov

Awardee Location

Street:438 Whitney Road Ext.
City:Storrs
State:CT
ZIP:06269-1133
County:Storrs Mansfield
Country:US
Awardee Cong. District:02

Primary Place of Performance

Organization Name:University of Connecticut
Street:371 Fairfield Way Unit 4157
City:Storrs
State:CT
ZIP:06269-4157
County:Storrs Mansfield
Country:US
Cong. District:02

Abstract at Time of Award

Computation, data, and workforce development are critical components of cyberinfrastructure (CI). This project will help create future CI professionals from the Computer Science and Engineering (CSE) and Geography Departments who can use, develop, deploy, and maintain advanced CI. CSE students have little experience with spatio-temporal data, while Geography students have training in spatio-temporal data but are unfamiliar with advanced computing techniques and data analytics techniques. To fill these gaps in curricula, this project will develop a set of training materials covering the full workflow of data acquisition, transfer, synthesis, computation, and visualization of CI, with a focus on handling large-scale spatio-temporal data. To encompass the interdisciplinary nature of CI, this project will develop training materials by faculty with complementary expertise in both CSE and Geography, targeting undergraduate and graduate students, postdocs and researchers with these substantive interests. The training materials will be at three levels that are in increasing depth. The first level is a set of basic training modules on the main components and their interactions in a CI system. The second level is a set of open-ended course projects that uses CI to solve challenging research problems, while the third level is an intensive competition-based workshop that will provide the students with the experience of solving real-world problems using CI through interdisciplinary collaboration. The context of the training materials will be in urban computing, an interdisciplinary field that relies heavily on spatio-temporal data and has profound societal impacts. This project will provide students with improved understanding of CI systems, contribute to new knowledge and discoveries using CI, and expand the CI community of interdisciplinary collaborators. Training materials be made publicly available and will be broadly disseminated through various outreach programs. 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.

For specific questions or comments about this information including the NSF Project Outcomes Report, contact us.