CAREER: TenAI: Tensorizing Machine Learning to Leverage Multiway Structure — NSF Award to Tufts University (MA, $300,000)
Machine learning has achieved state-of-the-art performance in image recognition, accelerated large-scale computational simulations, and unleashed the potential of generative modeling. The proliferation of machine learning has brought with it significant demands on computational resources (e.g., storage and energy) and
| Award title | CAREER: TenAI: Tensorizing Machine Learning to Leverage Multiway Structure |
|---|---|
| Award ID | 2541280 |
| Awardee | Tufts University |
| City | MEDFORD |
| State | MA |
| Amount obligated | $300,000 |
| Principal investigator | Elizabeth Newman |
| Program | COMPUTATIONAL MATHEMATICS |
| Start date | 09/01/2026 |
| Abstract | Machine learning has achieved state-of-the-art performance in image recognition, accelerated large-scale computational simulations, and unleashed the potential of generative modeling. The proliferation of machine learning has brought with it significant demands on computational resources (e.g., storage and energy) and reliance on data-driven models that lack accuracy guarantees. This lack of transparency has made it difficult to trust machine learning tools for high consequence tasks, e.g., drug |
| Source | NSF Awards |
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