Joseph D. Romano, PhD, MPhil, MA
Assistant Professor of Informatics
Dr. Romano’s research focuses on using biomedical data science and artificial intelligence to make critical discoveries about toxicology and environmental health. His lab leads the development of new data infrastructures that integrate diverse biomedical knowledge from public data sources into knowledge graphs, as well as data-driven analyses on those knowledge graphs that both predict and explain outcomes of toxic exposures. He also conducts methods development research in graph machine learning, genetic programming, and nonparametric statistics for multi-omics data.
He currently leads the development of ComptoxAI and VenomKB, which provide richly structured data and machine learning models for studying environmental toxicology and venom-based drug discovery. He also led construction of the knowledge graph underlying the Alzheimer’s Knowledge Base (AlzKB) – a major new resource for discovering drugs to treat Alzheimer’s Disease. His work is currently supported by a K99/R00 “Pathway to Independence” grant awarded by the National Library of Medicine, and has been recognized by the National Institute of Environmental Health Sciences and the National Cancer Institute. In 2022, Dr. Romano joined the Environmental Health Language Collaborative – an NIH-led effort to develop a standardized language for environmental health science research.
Dr. Romano joined the University of Pennsylvania faculty in 2023. In addition to the Department of Biostatistics, Epidemiology and Informatics, he is also affiliated with the Penn Institute for Biomedical Informatics (IBI) and the Center of Excellence in Environmental Toxicology (CEET). His teaching activities include coursework in biomedical informatics, computer programming, and computational toxicology, as well as related fields.
For more information, see his lab website.
Content Area Specialties
Biomedical informatics, translational bioinformatics, computational toxicology, systems pharmacology, toxinology, biomedical ontologies, biomedical knowledge graphs, environmental health science, electronic health records
Machine learning, artificial intelligence, knowledge representation, multimodal data integration, genetic programming, omics methods