# Graph-based semi-supervised techniques for machine learning tasks at low label rates

> **NSF 01002526DB NSF RESEARCH & RELATED ACTIVIT** · Michigan State University (MI) · $379,999

## Abstract

Machine learning is a type of artificial intelligence that enables a machine to learn from data. The success of machine learning is dependent upon a sufficient amount of labeled data samples. A key limitation of most machine learning methods is their reliance on large labeled sets. Labeled data is scarce for many applications. Obtaining enough labeled data is often difficult because it is time-consuming and expensive, especially when experts are required for the labeling task. This project develops novel strategies for machines to effectively learn in limited labeled data scenarios. The foundation of the new strategies lies in data analysis and algorithm development and the project involves the training of graduate and undergraduate students in these areas through mentoring and curriculum development.  User-friendly software packages will be made available to the community to ensure the results from the project can be used by other researchers who use machine learning. 
 
To address the challenge of data with limited labeled samples, and to develop computationally tractable methods for machine learning tasks such as data classification, the PI will incorporate a graph-based semi-supervised learning framework. Specifically, one of the main advantages of semi-supervised learning is its ability to make use of the important information from the vastly available unlabeled data without the additional cost of external interaction; moreover, the graph-based framework provides infor

## Key facts

- **NSF award ID:** 2512644
- **Awardee organization:** Michigan State University (MI)
- **SAM.gov UEI:** R28EKN92ZTZ9
- **PI:** Ekaterina Rapinchuk
- **Primary program:** 01002526DB NSF RESEARCH & RELATED ACTIVIT
- **All programs:** Artificial Intelligence (AI), Machine Learning Theory, COMPUTATIONAL SCIENCE & ENGING
- **Estimated total:** $379,999
- **Funds obligated:** $379,999
- **Transaction type:** Standard Grant
- **Period:** 09/01/2025 → 08/31/2028

## Primary source

NSF Award Search: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2512644

## Citation

> US National Science Foundation, Award 2512644, Graph-based semi-supervised techniques for machine learning tasks at low label rates. Retrieved via AI Analytics 2026-06-08 from https://api.ai-analytics.org/grant/nsf/2512644. Licensed CC0.

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