Collaborative Research: SaTC: CORE: Small: Corruption-Robust Online Optimization: from The — NSF Award to University of Massachuse
From online advertising to content delivery networks and recommendation systems, many modern technologies rely on algorithms that must operate in real time without knowing what will happen next. Researchers in computer science, operations research, engineering, and other fields have developed powerful online optimizati
| Award title | Collaborative Research: SaTC: CORE: Small: Corruption-Robust Online Optimization: from The |
|---|---|
| Award ID | 2512128 |
| Awardee | University of Massachusetts Amherst |
| City | AMHERST |
| State | MA |
| Amount obligated | $249,998 |
| Principal investigator | Mohammadhassan Hajiesmaili |
| Program | Secure &Trustworthy Cyberspace |
| Start date | 10/01/2025 |
| Abstract | From online advertising to content delivery networks and recommendation systems, many modern technologies rely on algorithms that must operate in real time without knowing what will happen next. Researchers in computer science, operations research, engineering, and other fields have developed powerful online optimization and learning tools for making effective decisions in the face of uncertainty, by helping systems learn from past outcomes to improve performance over time. However, these method |
| Source | NSF Awards |
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