# BIO-AI: STAR: Machine Learning for Robust Demographic Inference Under Biologically Realistic Conditions

> **NSF 01002627DB NSF RESEARCH & RELATED ACTIVIT** · Mississippi State University (MS) · $399,999

## Abstract

Across the tree of life, populations diverge upon isolation by geographic barriers, exchange migrants upon secondary contact, and adapt to environmental pressures. These processes leave signatures in species’ genomes, which can be used to understand the factors shaping biodiversity. However, popular methods for disentangling these signatures are limited both in terms of efficiency and accuracy, and Artificial Intelligence (specifically, machine learning) offers a powerful alternative. Despite recent advances, machine learning approaches have yet to reach their potential in this field and remain limited in the processes they can consider, their applicability across organisms, and their accessibility to researchers with varying levels of technical expertise. The proposed work will develop robust, user-friendly machine learning tools for investigators studying the drivers of diversification. Furthermore, the proposed work will use these tools to illuminate the evolutionary histories of several empirical systems, including fruit flies, mosquitoes, plants, snails, and slugs.  By creating well-documented, user-friendly tools, this work will provide a valuable resource to the broader community of evolutionary biologists. Furthermore, the work will support NSF’s desired societal outcome of the development of a globally competitive workforce by hosting workshops (both virtual and in-person), and training a postdoctoral researcher, a graduate student, several undergraduates, and high s

## Key facts

- **NSF award ID:** 2552066
- **Awardee organization:** Mississippi State University (MS)
- **SAM.gov UEI:** NTXJM52SHKS7
- **PI:** Megan L Smith
- **Primary program:** 01002627DB NSF RESEARCH & RELATED ACTIVIT
- **All programs:** Artificial Intelligence (AI), BIO-AI, EXP PROG TO STIM COMP RES
- **Estimated total:** $399,999
- **Funds obligated:** $399,999
- **Transaction type:** Standard Grant
- **Period:** 08/01/2026 → 07/31/2029

## Primary source

NSF Award Search: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2552066

## Citation

> US National Science Foundation, Award 2552066, BIO-AI: STAR: Machine Learning for Robust Demographic Inference Under Biologically Realistic Conditions. Retrieved via AI Analytics 2026-06-07 from https://api.ai-analytics.org/grant/nsf/2552066. Licensed CC0.

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