Aaron Masino
Associate Professor, School of Computing
Email: amasino@clemson.edu
Biosketch
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.
Research
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.
Selected Publications
Kark SM, Worthington MA, Christie RH, Masino AJ: Opportunities for Digital Health Technology: Identifying Unmet Needs for Bipolar Misdiagnosis and Depression Care Management. Frontiers in Digital Health 2023; 5:1221754. doi: 10.3389/fdgth.2023.1221754
Epifano JR, Ramachandran RP, Masino AJ, Rasool G: Revisiting the Fragility of Influence Functions. Neural Networks 162: 581-588, 2023
Campbell EA, Maltenfort MG, Shults J, Forrest CB, Masino AJ: Characterizing clinical pediatric obesity subtypes using electronic health record data. PLOS Digital Health 1.8 e0000073, 2022
Huang BH, Wang R, Masino AJ, Obstfeld O: Aiding Clinical Assessment of Neonatal Sepsis Using Hematological Analyzer Data with Machine Learning Techniques. International Journal of Laboratory Hematology May, 2021.
Bose S, Kenyon CC, Masino AJ: Personalized prediction of early childhood asthma persistence: a machine learning approach. PLOS One 16(3): e0247784, March 2021.
Folweiler KA, Sandsmark DK, Diaz-Arrastia R, Cohen AS, Masino AJ: Unsupervised machine learning reveals novel traumatic brain injury patient phenotypes with distinct acute injury profiles and long-term outcomes. Journal of Neurotrauma 37(12): 1431-1444, June 2020.
Masino AJ, Forsyth D, Nuske H, Herrington J, Pennington J, Kushleyeva Y, Bonafide CP: m-Health and Autism: Recognizing Stress and Anxiety with Machine Learning and Wearables Data. Proceedings of 2019 IEEE 32nd International Symposium on Computer-Based Medical Systems Page: 714-719, 2019.
Masino AJ, Harris MC, Forsyth D, Ostapenko S, Srinivasan L, Bonafide CP, Balamuth F, Schmatz M, Grundmeier, RW: Machine learning models for early sepsis recognition in the neonatal intensive care unit using readily available electronic health record data. PLoS One 14(2), February 2019.
Masino AJ, Forsyth D, Fiks AG: Detecting Adverse Drug Reactions on Twitter with Convolutional Neural Networks and Word Embedding Features. Journal of Healthcare Informatics Research 2: 25-43, June 2018 Notes: https://doi.org/10.1007/s41666-018-0018-9
Cocos A, Qian T, Callison-Burch C, Masino AJ: Crowd control: Effectively utilizing unscreened crowd workers for biomedical data annotation. Journal of Biomedical Informatics 69: 86-92, 2017.
Cocos A, Fiks AG, Masino AJ: Deep learning for pharmacovigilance: recurrent neural network architectures for labeling adverse drug reactions in Twitter posts. Journal of the American Medical Informatics Association 24(4): 813-821, 2017
Masino AJ, Dechene ET, Dulik M, Spinner NB, Krantz ID, Wilkens A, Pennington JW, White PS: Clinical gene-phenotype variant prioritization: A semantic similarity approach using the Human Phenotype Ontology. BMC Bioinformatics 15: 248, 2014.