UCSB Researchers Secure $1 Million NSF Grant

The funding supports a new project focused on developing advanced AI frameworks to model complex scientific interactions.

Updated on Oct. 21, 2026 in Mathematics

UCSB Researchers Secure $1 Million NSF Grant

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Four UC Santa Barbara researchers have received a $1 million grant from the National Science Foundation. The project aims to develop AI frameworks capable of modeling multi-component scientific interactions.

Why it matters

Current artificial intelligence models are often restricted to analyzing pairwise interactions, which limits their ability to understand complex systems. This research seeks to provide robust, provable AI methods for scientific discovery.

The grant supports the study of Foundations for Discovering Higher-Order Scientific Laws with Deep Learning. The research utilizes mathematical frameworks such as topological deep learning and Kolmogorov-Arnold networks.

The players

Nina Miolane

She is one of the four lead researchers on the UC Santa Barbara project.

Haewon Jeong

She is a member of the research team at the Robert Mehrabian College of Engineering.

Sanjukta Krishnagopal

She is a researcher contributing to the new AI modeling project.

Yao Qin

She is one of the four female researchers awarded the federal grant.

National Science Foundation

This is a federal agency that supports fundamental research and education in all the non-medical fields of science and engineering.

The details

Affiliated with the Robert Mehrabian College of Engineering, the team will test their frameworks using data from neuroscience, molecular biology, and cosmology. The project represents the first federal funding secured by the Real AI initiative at the university.

The Real AI initiative launched earlier this year at UC Santa Barbara.
The National Science Foundation grant was reported on October 21, 2026.

The Big Picture

This project represents a significant step forward for the Robert Mehrabian College of Engineering's Real AI initiative, marking its first federal funding. The work addresses a critical gap in existing AI models by prioritizing mathematical robustness in scientific discovery.

By creating robust tools for scientific modeling, the team hopes to accelerate discoveries in fields like molecular biology and neuroscience. The commitment to release these tools as open source software could eventually lower barriers for researchers using AI to process complex data.

The takeaway

This project highlights a shift toward prioritizing provable mathematical foundations in artificial intelligence to improve scientific accuracy. Researchers can look forward to the future release of these open source tools to aid in their own data modeling efforts.