Collaborative Research: CAIG: Using statistical neural networks to learn ocean microbial e — NSF Award to University of Washington
This project will combine large flow-cytometry datasets with novel machine learning models to reveal the geographical distribution of phytoplankton and show how the environment shapes these patterns. Neural network methods for flow-cytometry data analysis will be applied to data from over 100 cruises across the Pacific
| Award title | Collaborative Research: CAIG: Using statistical neural networks to learn ocean microbial e |
|---|---|
| Award ID | 2530448 |
| Awardee | University of Washington |
| City | SEATTLE |
| State | WA |
| Amount obligated | $554,004 |
| Principal investigator | François Ribalet |
| Program | GEO CI - GEO Cyberinfrastrctre |
| Start date | 10/01/2025 |
| Abstract | This project will combine large flow-cytometry datasets with novel machine learning models to reveal the geographical distribution of phytoplankton and show how the environment shapes these patterns. Neural network methods for flow-cytometry data analysis will be applied to data from over 100 cruises across the Pacific and Atlantic Oceans. The project will develop computationally efficient mixture of neural network models, a generative model framework for changepoint detection, and spatially dep |
| Source | NSF Awards |
$799/mo
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