NIH Awards Morgan State $520,000 Grant to Advance Biomedical Artificial Intelligence Research

Four-Year ORBIL-AI Project Builds on Faculty Research Presented at Yale University to Explore How Artificial Intelligence Can Better Interpret Complex Biomedical Data

BALTIMORE — Morgan State University has received a four-year research award totaling approximately $520,000 from the National Institutes of Health (NIH), through the National Library of Medicine (NLM), to support foundational research aimed at advancing how artificial intelligence processes, integrates, and interprets complex biomedical information. The project, which began September 10, 2026, and runs through July 31, 2030, seeks to address a fundamental challenge in modern AI: determining how multiple sources of biomedical data can contribute meaningful, complementary information without introducing unnecessary duplication.

The project, titled ORBIL-AI: Orthogonality-Regularized Biomedical Information-theoretic Learning, is led by Timothy Oladunni, D.Sc., assistant professor in the Department of Computer Science within Morgan’s School of Computer, Mathematical and Natural Sciences (SCMNS). Supported through NIH’s Support for Research Excellence (SuRE) R16 program, the project received an initial award of $130,134, with approximately $520,000 anticipated over its four-year duration.

Building on Dr. Oladunni’s previously published research, “Complementary Feature Domains for Optimal ECG Multimodal Deep Learning Performance,” ORBIL-AI will investigate how artificial intelligence systems can more effectively distinguish between information shared across multiple biomedical observations and the unique insights each source provides. The research seeks to establish mathematical and computational principles that could improve the efficiency, interpretability and reliability of biomedical AI systems.

“Combining biomedical data from different sources can improve predictive accuracy, but understanding how these sources interact and contribute distinct information requires a stronger mathematical foundation,” said Dr. Oladunni, principal investigator. “Through this NIH-funded project, we are developing and testing principles that explain when combining information is beneficial and how those relationships affect model performance and reliability.”

When More Data Does Not Necessarily Mean Better Results

The scientific foundation for ORBIL-AI emerged from Dr. Oladunni’s earlier investigations into redundancy and complementarity in multimodal machine learning.

In April 2025, he presented his research at the Yale University More Isn't Always Better: Investigating Redundancy and Complementarity in Multimodal ECG Deep Learning Computer Science Colloquium. The presentation examined how incorporating additional feature domains into AI models can introduce overlapping information and increased computational demands without necessarily improving predictive performance. His research also introduced Complementary Feature Domains (CFD) theory, which explores the importance of identifying distinct, nonredundant information when integrating multiple data sources.

Building upon this work, ORBIL-AI advances the investigation from understanding informational complementarity to developing mathematical and computational approaches that enable AI systems to learn complementary information more effectively.

The NIH-funded research will examine orthogonality-regularized, information-theoretic learning, an approach designed to study how computational models represent shared and distinct information across multiple biomedical observations. The findings could provide new insights into how AI systems process complex data, with potential implications for improving model generalizability, interpretability and calibration.

Expanding Biomedical AI Research and Student Opportunities at Morgan

The award further strengthens Morgan State University’s commitment to advancing investigator-led research, expanding interdisciplinary collaboration and creating opportunities for students to participate in emerging fields of scientific discovery. By bringing together information theory, mathematical representation learning, artificial intelligence and biomedical signal analysis, ORBIL-AI contributes to a growing body of research examining how advanced computational methods can address complex scientific challenges. The initiative also builds upon Dr. Oladunni’s broader research portfolio in explainable machine learning, multimodal biomedical computing and the theoretical foundations of computational representations.

“This research reflects Morgan State University's commitment to developing new theories in computer science and applying them to important biomedical problems. It also creates opportunities for students to participate in fundamental research and contribute to the development of new scientific knowledge,” added Dr. Oladunni.

In 2025, Dr. Oladunni held a visiting faculty research fellowship at Yale University, where he participated in interdisciplinary research involving cardiovascular science and computational artificial intelligence in medicine. The new NIH award provides an opportunity to expand this research at Morgan State while supporting the development of emerging scholars working at the intersection of computer science and biomedical science. Through the NIH SuRE program, the project also aligns with broader efforts to strengthen research capacity and foster meaningful student engagement in biomedical discovery.

Advancing the Future of Artificial Intelligence

While ORBIL-AI focuses on biomedical signals, its underlying scientific questions extend beyond healthcare applications. As artificial intelligence becomes increasingly dependent on integrating information from multiple sources, researchers face a growing challenge: determining how each source contributes to a computational model’s understanding and whether additional information meaningfully improves its performance. By investigating the mathematical relationships between shared, redundant and complementary information, ORBIL-AI seeks to advance the theoretical foundations of multimodal machine learning. The research has the potential to inform the development of more efficient, reliable and interpretable AI systems, contributing to future innovations in biomedical research and other data-intensive scientific fields.

For Morgan State University, the NIH-supported initiative represents another step in expanding its contributions to foundational artificial intelligence research and strengthening its role in preparing the next generation of scientists and researchers to address emerging technological challenges.

About Morgan

Morgan State University, founded in 1867, is a Carnegie-classified high research (R2) institution offering more than 150 baccalaureate, master’s degree, doctorate and certificate programs. As Maryland’s Preeminent Public Urban Research University, and the only university to have its entire campus designated as a National Treasure by the National Trust for Historic Preservation, Morgan serves a multiethnic and multiracial student body and seeks to ensure that the doors of higher education are opened as wide as possible to as many as possible. For more information about Morgan State University, visit www.morgan.edu. 

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