Skip directly to content

Minimize RSR Award Detail

Research Spending & Results

Award Detail

Awardee:UNIVERSITY OF WISCONSIN SYSTEM
Doing Business As Name:University of Wisconsin-Madison
PD/PI:
  • Aws Albarghouthi
  • (608) 262-3822
  • aws@cs.wisc.edu
Co-PD(s)/co-PI(s):
  • Frederic Sala
Award Date:05/12/2021
Estimated Total Award Amount: $ 900,000
Funds Obligated to Date: $ 900,000
  • FY 2021=$900,000
Start Date:07/01/2021
End Date:06/30/2025
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:SHF: Medium: Program Synthesis for Weak Supervision
Federal Award ID Number:2106707
DUNS ID:161202122
Parent DUNS ID:041188822
Program:Software & Hardware Foundation
Program Officer:
  • Nina Amla
  • (703) 292-7991
  • namla@nsf.gov

Awardee Location

Street:21 North Park Street
City:MADISON
State:WI
ZIP:53715-1218
County:Madison
Country:US
Awardee Cong. District:02

Primary Place of Performance

Organization Name:University of Wisconsin-Madison
Street:1210 W. Dayton St
City:Madison
State:WI
ZIP:53706-1613
County:Madison
Country:US
Cong. District:02

Abstract at Time of Award

Artificial intelligence, in the form of machine learning, has been transformative in automating difficult tasks and extracting insights in numerous problem domains, including language, vision, and recommendations, and offers many further possibilities. For instance, machine learning holds the promise of automatically analyzing medical images, a task previously reserved for human specialists. However, machine-learning algorithms typically require learning from large amounts of hand-labeled data. Manually labeling data is an expensive and human-intensive process. This project seeks to radically minimize the amount of labeled data needed for machine learning, with the goal of enabling cheap and rapid application of machine learning to new and important domains. The project leverages program synthesis and weak supervision technology to minimize the amount of labeled data needed to build performant models. Weak supervision replaces hand labels with a number of imprecise sources providing a rough signal for supervised training. Such sources are expressed by labeling functions: small, rough programs that encode knowledge about the task at hand. The goal of this project is to have labeling functions be generated automatically using program synthesis, eliminating the manual writing of labeling functions and the need for programming expertise. The project develops a generic language of labeling functions, and explores efficient re-use of synthesized functions and richer means of user interaction to further reduce label requirements. The project is training a diverse group of students. Additionally, the PIs are designing a novel course on machine learning with less labeled data. 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.