# Collaborative Research: Efficient Individualized Treatment Selection for Personalized Medicine

> **NSF 01002526DB NSF RESEARCH & RELATED ACTIVIT** · University of North Carolina at Chapel Hill (NC) · $180,000

## Abstract

Recent advances in data science, statistics, and machine learning have opened new possibilities in precision medicine, enabling clinicians to tailor treatments based on individual patient characteristics. This project focuses on developing a unified and efficient statistical framework to improve treatment decisions by leveraging rich demographic, socio-economic, and biomedical data. By advancing personalized decision-making, this research contributes to better health outcomes, more efficient healthcare delivery, and overall national well-being. The project also offers broad societal impact through its commitment to education, collaboration, and open science. The investigators will mentor graduate students and develop new coursework at the intersection of machine learning, statistics, and personalized medicine. In addition, all software tools developed will be released as open-source, supporting accessibility and reproducibility in scientific research. The interdisciplinary nature of the project encourages collaboration across statistics, medicine, and computer science, and prepares a next-generation workforce to tackle complex health data problems.

This project aims to develop an efficient learning framework for estimating optimal individualized treatment rules (ITRs) across a broad range of personalized medicine settings. The proposed methodology is based on semiparametric modeling and is designed to address complex relationships among covariates, treatments, and outcomes

## Key facts

- **NSF award ID:** 2515561
- **Awardee organization:** University of North Carolina at Chapel Hill (NC)
- **SAM.gov UEI:** D3LHU66KBLD5
- **PI:** Yufeng Liu
- **Primary program:** 01002526DB NSF RESEARCH & RELATED ACTIVIT
- **All programs:** Artificial Intelligence (AI), Machine Learning Theory, Biotechnology
- **Estimated total:** $180,000
- **Funds obligated:** $180,000
- **Transaction type:** Standard Grant
- **Period:** 09/15/2025 → 06/30/2026

## Primary source

NSF Award Search: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2515561

## Citation

> US National Science Foundation, Award 2515561, Collaborative Research: Efficient Individualized Treatment Selection for Personalized Medicine. Retrieved via AI Analytics 2026-06-08 from https://api.ai-analytics.org/grant/nsf/2515561. Licensed CC0.

---

*[NSF Awards dataset](/datasets/nsf-awards) · CC0 1.0*