This $422,711 Project Grant from the National Science Foundation's Engineering program (CFDA 47.041) will support the development of new frameworks, algorithms, and applications for optimization under distributional distortions at Georgia Tech Research Corporation from October 1, 2022 to September 30, 2026. The Principal Investigator will investigate methods to enhance data-driven decision making when distributions are distorted by outliers or data quality issues. The research will establish theoretical and algorithmic foundations for distributionally favorable optimization and explore applications in operations engineering. Specific work will include developing decomposition techniques for solving large-scale models efficiently, exploiting problem structures like submodularity, and designing learning frameworks to address endogenous uncertainty. In addition to furthering research, the grant incorporates educational components such as new course modules, a summer camp for high school girls, and collaboration with a science museum to broaden participation in STEM fields.
Mod # | Description | Reason For Modification | Federal Obligation (Click to sort descending) | Date (Click to sort ascending) |
|---|---|---|---|---|
| Not listed | $422.7k | 11/2/22 |