CAREER: Distribution-In-Distribution-Out Analytics: Modeling, Inference, and Applications in Data-Rich Science and Engineering 2031 Grant

Unknown funder

About this grant

This Faculty Early Career Development Program (CAREER) grant, awarded by NSF's Division of Civil, Mechanical, and Manufacturing Innovation, funds the development of new statistical methods for analyzing data that take the form of probability distributions rather than single numbers. In many modern scientific and engineering applications—including single cell biology, biotechnology, and advanced manufacturing—data are naturally represented by probability distributions such as histograms or probability functions that describe variability within measurements. Current analytical methods typically reduce this rich distributional information to simple summary statistics like averages, discarding valuable information and potentially limiting scientific insights. The project establishes the statistical foundations for a distribution-in-distribution-out (DIDO) analytical framework that enables regression modeling and inference when both predictors and responses may be scalar-valued or distribution-valued random variables. The research leverages differential geometry to define linear and nonlinear regression intrinsically in the metric space of probability distributions, moving beyond traditional approaches that operate in Euclidean space. Both the theory and statistical properties of these models will be developed, along with new methods for analyzing model uncertainties. The models developed will be validated through applications to single cell RNA sequencing, single cell proteomics, and perioperative medicine data, where preserving the full distributional information of protein and gene expression intensities is expected to yield more accurate and robust scientific discoveries. The project also aims to identify biomarkers for diagnosis and drug development, contributing to global competitiveness in data science and artificial intelligence. The educational components include training opportunities for undergraduate and high school students in engineering statistics and biotechnology, as well as preparation of graduate students for this emerging interdisciplinary field. The project will engage the broader community through open-source software, accessible educational materials, and data challenge competitions, broadening participation in science, technology, and engineering.

Eligibility

This is an already-awarded NSF CAREER grant to a named principal investigator (Xiaoyu Chen) at the State University of New York at Buffalo. The CAREER program is open to early-career faculty members who have not previously received a CAREER award and who are in tenure-track or equivalent positions at US-based academic institutions. This specific award is not an open solicitation.

How it's scored

NSF evaluates CAREER proposals using the Foundation's standard review criteria of Intellectual Merit and Broader Impacts. Intellectual Merit considers the potential to advance knowledge within and across fields. Broader Impacts considers the potential to benefit society and contribute to the development of a diverse, globally competitive STEM workforce. Proposals are reviewed by external peer reviewers and program officers.

Key requirements

• ✓Applicant must be an early-career faculty member (CAREER programme eligibility)
• ✓Research must advance statistical methods for distribution-valued data analysis
• ✓Applications should demonstrate relevance to data-rich science and engineering domains such as single-cell biology, biotechnology, or advanced manufacturing

Recent past awardees

Knowing who's won the last rounds tells you whether you're in the strike zone.

• THE RESEARCH FOUNDATION FOR THE STATE UNIVERSITY OF NEW YORK (SUNY at Buffalo) · 2026 · $500K

Funder contact

The notice names a programme contact you can speak to before applying.

Frequently asked questions

How much funding does Unknown funder offer?

CAREER: Distribution-In-Distribution-Out Analytics: Modeling, Inference, and Applications in Data-Rich Science and Engineering offers $500K – $500K.

When is the CAREER: Distribution-In-Distribution-Out Analytics: Modeling, Inference, and Applications in Data-Rich Science and Engineering deadline?

Applications close 30 Jun 2031.

Who can apply for CAREER: Distribution-In-Distribution-Out Analytics: Modeling, Inference, and Applications in Data-Rich Science and Engineering?

Eligibility covers organisations in US; university, research-institution. See the eligibility criteria above for the full conditions.

Relevant sectors

About this sector

Climate software and data funding supports emissions accounting, climate risk analytics, Earth observation, digital MRV and tools for planners. It is offered as innovation grants, public contracts for data services and support from technology programmes. Buyers generally look for data quality, interoperability and evidence that users rely on the tool.