Aaron Masino

Aaron Masino serves as Associate Professor of Computer Science at Clemson University's School of Computing within the College of Engineering, Computing and Applied Sciences. He also holds the Dr. Gary Spitzer Endowed Distinguished Professor of Genomics position at Clemson's Center for Human Genetics. His interdisciplinary work bridges computational methods with biomedical applications.

• PhD in Applied Mathematics, University of Central Florida (2004)
• MEng in Aerospace Engineering, University of Colorado, Colorado Springs (1999)
• BA in Mathematics, Rutgers University (1997)

Masino's research focuses on developing artificial intelligence, data science, and biomedical informatics methods for advancing healthcare. His primary interests include integrating structured ontology and multiomic data in deep learning models to study rare genetic disorders, with applications in phenotype discovery, variant pathogenicity prediction, and clinical diagnostic planning. He also develops clinical decision support systems for neonatal sepsis recognition and remote patient monitoring. His work emphasizes explainable AI interfaces for clinician adoption and addresses challenges in EHR data bias and demographic disparities.

Analysis of his recent publications (2022-2025) reveals strong thematic trends in rare disease genomics, neonatal sepsis AI systems, and mental health digital tools. His work consistently applies machine learning to multi-modal biomedical data (EHR, genomic, physiological, and behavioral) while addressing critical implementation challenges like algorithmic bias, clinical workflow integration, and explainability. Key application areas include autism spectrum disorder monitoring, long COVID sub-phenotyping, and hereditary cancer counseling.

Through the Masino Lab, he leads collaborative projects including a COBRE in Human Genetics Research Project developing deep learning methods for rare genetic disease diagnosis. His lab maintains active partnerships with the Children's Hospital of Philadelphia Research Institute on neonatal sepsis decision support systems and emphasizes open science principles with public code and data sharing.

Masino directs a highly interdisciplinary research environment involving collaborations across computer science, genetics, epidemiology, and clinical medicine. His lab actively recruits students and researchers interested in AI applications for biomedical science, with strong emphasis on reproducible research and translational impact.