# Collaborative Research: SaTC 2.0: RES: Achieving Reliable, Interpretable, and Efficient At-scale Machine Unlearning on Generative AI Models

**Budget Signal Score:** 55/100

| Field | Value |
|---|---|
| Institution | University of Tulsa |
| Location | TULSA, OK |
| Project | Collaborative Research: SaTC 2.0: RES: Achieving Reliable, Interpretable, and Efficient At-scale Machine Unlearning on Generative AI Models |
| Award amount | $500,787 |
| Obligated so far | $500,787 |
| Awarded | 2026-07-19 |
| Period | 2026-10-01 to 2029-09-30 |
| Program | Secure &Trustworthy Cyberspace |
| Directorate / division | CSE / CPIS |
| Award ID | 2555319 |

## Why this score
- Award of $300K or more.
- Equipment- and compute-heavy field.

Source: U.S. National Science Foundation Awards API (https://www.nsf.gov/awardsearch/showAward?AWD_ID=2555319). Last refreshed: 2026-11-03T18:10:42.804Z.
Award data comes from the NSF Awards API and may be amended by NSF. Institution-level only; investigator contact details are not included. Informational only.