Prediction and inference for heterogeneous network data — NSF Award to Regents of the University of Michigan - Ann Arbor (MI, $325
Network data, which captures relationships and interactions among entities, is central to many modern AI and machine learning applications in areas such as neuroscience, social science, economics, and biomedicine. Examples include brain connectivity networks, social interaction graphs, and recommendation systems. This
| Award title | Prediction and inference for heterogeneous network data |
|---|---|
| Award ID | 2610168 |
| Awardee | Regents of the University of Michigan - Ann Arbor |
| City | ANN ARBOR |
| State | MI |
| Amount obligated | $325,000 |
| Principal investigator | Elizaveta Levina |
| Program | STATISTICS |
| Start date | 07/01/2026 |
| Abstract | Network data, which captures relationships and interactions among entities, is central to many modern AI and machine learning applications in areas such as neuroscience, social science, economics, and biomedicine. Examples include brain connectivity networks, social interaction graphs, and recommendation systems. This project develops new machine learning and statistical methods for analyzing complex network data, with a focus on prediction, representation learning, and comparing populations of |
| Source | NSF Awards |
$799/mo
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