| NSF Org: |
OAC Office of Advanced Cyberinfrastructure (OAC) |
| Recipient: |
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| Initial Amendment Date: | January 11, 2023 |
| Latest Amendment Date: | December 1, 2023 |
| Award Number: | 2244271 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Sharmistha Bagchi-Sen
shabagch@nsf.gov (703)292-8104 OAC Office of Advanced Cyberinfrastructure (OAC) CSE Directorate for Computer and Information Science and Engineering |
| Start Date: | April 1, 2023 |
| End Date: | March 31, 2026 (Estimated) |
| Total Intended Award Amount: | $443,764.00 |
| Total Awarded Amount to Date: | $458,164.00 |
| Funds Obligated to Date: |
FY 2024 = $14,400.00 |
| History of Investigator: |
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| Recipient Sponsored Research Office: |
MAIN CAMPUS WASHINGTON DC US 20057 (202)625-0100 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
MAIN CAMPUS WASHINGTON DC US 20057-0001 |
| Primary Place of
Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): | RSCH EXPER FOR UNDERGRAD SITES |
| Primary Program Source: |
01002425DB NSF RESEARCH & RELATED ACTIVIT |
| Program Reference Code(s): |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.070 |
ABSTRACT
When attempting to tackle societal scale issues, computer science students have limited opportunities to use algorithms, methods, and the tools they are taught in class with large-scale real world data sets. It is even more unusual to get the opportunity to understand how to build bridges that connect these algorithms and methods to policy development and decision making. This program aims not only to teach students about computer science research, but to also help them understand how researchers in other disciplines use computer science algorithms and analytic tools to generate evidence for developing public policy. The Georgetown REU site connects formal computer science education to real world data science research to public policy decision making. Students in the program work on improving mining and learning algorithms for different data science algorithms. They then connect the outputs of the methods they develop to social science and public policy questions, improving their understanding of how those outside of computer science use algorithms and their specifications to generate scientific evidence for developing public policy.
The core research that the REU students conduct is in data-centric computing. Their goals are to advance the state-of-the-art methods for emotion detection across languages (cohort 1), emerging misinformation detection (cohort 2), and opinion modeling of public policies (cohort 3). All three of these problems have existing solutions. Each year students extend the existing state of the art methods to address one specific constraint. Cohort 1 employs multi-lingual language models to tackle the language constraint. Cohort 2 generates novel weakly labeled data to address the temporal (emerging) constraint. Cohort 3 focuses on blending auxiliary information to address the limited training data constraint. For all three tasks, students also consider unsupervised, semi-supervised, and supervised models and conduct sensitivity analyses that identify the biases associated with different learning techniques, extending their understanding of the tradeoffs between interpretability, scalability, and accuracy. Finally, students use the ?best? models to conduct a data science analysis that informs public policy research with respect to migration movement, misinformation intervention strategies, and gun culture. The Georgetown REU Site gives students the opportunity to advance computer science research and understand how computer science research connects to other disciplines of research.
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.
Please report errors in award information by writing to: awardsearch@nsf.gov.
