Project Grant 2515171

Award Date 7/1/25
Completion Date 6/30/28
Dollars Obligated $175K
Federal Grant Program
47.049
Assistance Type
Project Grant
Place of Performance
Urbana, IL 61801, USA
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This $175,000 Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences Program (CFDA 47.049) aims to advance the theoretical foundations and practical applications of self-supervised representation learning, particularly in the context of biomedical research. The key products and services to be delivered under this 3-year award include:

  1. Developing new theoretical frameworks for understanding self-supervised representation learning on low-dimensional nonlinear models, which can capture the intrinsic structure of observed data. This work will clarify the role of pseudo-labels generated from unlabeled data in self-supervised learning.

  2. Integrating self-supervised representation learning techniques into biomedical applications, such as microbiome studies and omics-based longitudinal data analysis. The project will create new computational tools and software tailored to these biomedical research domains, enabling more effective use of large-scale unlabeled data.

  3. Educational efforts to engage students and the broader public with this growing area of self-supervised machine learning research and its potential impacts on biomedical studies.

This award reflects the NSF's mission to advance scientific knowledge and support innovative research that can address critical scientific questions and contribute to a deeper understanding of biological systems and human health.

Generated 6/24/25, 5:12 AM