# CAREER: Integrating Conformal Prediction into Machine Learning for Provably Safe Deployment

> **NSF 01002829DB NSF RESEARCH & RELATED ACTIVIT** · Washington State University (WA) · $600,000

## Abstract

Advances in machine learning (ML) have opened up new possibilities for accurate predictions in various fields. To ensure the reliability of these predictions, especially in high-stakes decision-making scenarios, theory and tools need to be developed that provide confidence intervals. This research project aims to create innovative methods for quantifying uncertainty in complex systems, allowing for more informed and confident decision-making. One key aspect of this work is designing efficient collaboration between humans and machines, where artificial intelligence (AI) produces a set of possible solutions and human experts select the best option. For instance, in medical diagnosis, these tools will enable doctors to identify potential health risks by generating a short list of likely diagnoses, with the correct answer guaranteed to be included among them. Through a five-year program combining cutting-edge research, education, and community engagement, the researcher will develop novel algorithmic and theoretical frameworks to improve predictive accuracy and trustworthiness across various applications. To share the results broadly, the researcher will publish research papers, present at conferences, and provide tutorials on uncertainty quantification and safe ML deployment. 

This project aims to create a new framework that seamlessly integrates conformal prediction principles into ML model training procedures. This integrated approach will enable the production of small unc

## Key facts

- **NSF award ID:** 2443828
- **Awardee organization:** Washington State University (WA)
- **SAM.gov UEI:** XRJSGX384TD6
- **PI:** Yan Yan
- **Primary program:** 01002829DB NSF RESEARCH & RELATED ACTIVIT
- **All programs:** CAREER-Faculty Erly Career Dev, ROBUST INTELLIGENCE
- **Estimated total:** $600,000
- **Funds obligated:** $317,381
- **Transaction type:** Continuing Grant
- **Period:** 06/15/2025 → 05/31/2030

## Primary source

NSF Award Search: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2443828

## Citation

> US National Science Foundation, Award 2443828, CAREER: Integrating Conformal Prediction into Machine Learning for Provably Safe Deployment. Retrieved via AI Analytics 2026-06-07 from https://api.ai-analytics.org/grant/nsf/2443828. Licensed CC0.

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