Aaron J. Masino

Dr. Masino is an Associate Professor in the School of Computing at Clemson University and holds the Clemson University Center for Human Genetics Dr. Gary Spitzer Endowed Distinguished Professorship in Genomics. Prior to joining Clemson University, Dr. Masino served as Vice President of Clinical Data Science at AiCure, LLC from 2021-2023 where he led research aimed at using Artificial Intelligence (AI) methods to characterize central nervous system disorders. He was previously an Assistant Professor of Informatics in the Department of Anesthesiology and Critical Care at the Perelman School of Medicine at the University of Pennsylvania from 2019-2021 and a biomedical informatics scientist in the Department of Biomedical and Health Informatics at the Children’s Hospital of Philadelphia from 2011-2021 where his research group focused on the application and development of AI based clinical decision support tools for pediatric medicine including machine learning model development for sepsis prediction, utilization of wearable device data to recognize stress in individuals with Autism, and deep learning methods to detect latent concepts in electronic health record (EHR) data. From 2004-2011, Dr. Masino served as a senior scientist at MZA Associates Corporation and SAIC where he developed adaptive optics control algorithms. He served in the U.S. Air Force from 1997-2002 primarily as a launch crew commander for the Boeing Delta space launch program. He received his PhD in Applied Mathematics from the University of Central Florida in 2004. He also holds a MEng in Aerospace Engineering from the University of Colorado in Colorado Springs and a BA in Mathematics from Rutgers University.

Dr. Masino’s research interests include the development and application of artificial intelligence, data science, bioinformatics, and biomedical informatics methods for biomedical research. His current research focuses on the integration of structured ontology and multiomic data in deep learning models to increase knowledge of rare genetic disorders through phenotype discovery and in-silico variant pathogenicity prediction, clinical diagnostic planning with large language model interfaces, and early sepsis recognition clinical decision support systems.