Federal Grant Award Summary

The National Science Foundation's Division of Mathematical Sciences awarded UCLA a $139,995 Project Grant (CFDA 47.049 - Mathematical and Physical Sciences) effective June 1, 2026 through May 31, 2029 to develop non-parametric estimation methods and algorithms for analyzing multimodal datasets. The research deliverables focus on creating robust, scalable, and statistically principled approaches for integrating and analyzing heterogeneous data sources such as medical imaging, clinical records, and genetic information, with particular emphasis on uncertainty quantification. The project employs deep learning techniques to model auxiliary structures and kernel-based methods to provide rigorous statistical uncertainty quantification, enabling more reliable inference and reducing the risk of misleading conclusions in high-stakes applications such as clinical decision-making and health policy.

In addition to core research outputs, the award incorporates substantial education and workforce development components. UCLA will integrate research findings into undergraduate and graduate coursework, develop openly accessible online materials, engage undergraduate, graduate, and postdoctoral students in research and dissemination activities, and conduct workshops and outreach activities designed to broaden participation in data science. The project is performed at UCLA's Los Angeles, California location under the Office of Research Administration.