CAREER: Foundations of Resource Efficient Machine Learning — NSF Award to Regents of the University of Michigan - Ann Arbor (MI, $
Contemporary machine learning techniques tend to be resource-intensive, often requiring good quality datasets, expensive hardware, or significant computing power. In a wide array of application domains, ranging from healthcare to mobile computing, these critical resources are lacking. Novel methodologies that enable th
| Award title | CAREER: Foundations of Resource Efficient Machine Learning |
|---|---|
| Award ID | 2550179 |
| Awardee | Regents of the University of Michigan - Ann Arbor |
| City | ANN ARBOR |
| State | MI |
| Amount obligated | $324,789 |
| Principal investigator | Samet Oymak |
| Program | Comm & Information Foundations |
| Start date | 10/01/2025 |
| Abstract | Contemporary machine learning techniques tend to be resource-intensive, often requiring good quality datasets, expensive hardware, or significant computing power. In a wide array of application domains, ranging from healthcare to mobile computing, these critical resources are lacking. Novel methodologies that enable the optimal utilization of resources can help unlock the full potential of the data science revolution for these domains. Towards this aim, this project will develop theoretically-gr |
| Source | NSF Awards |
$799/mo
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