Joseph Daniel Romano, PhD, MPhil, MA, FAMIA
Assistant Professor of Biostatistics and Epidemiology
CEET Investigator, Center of Excellence in Environmental Toxicology
Faculty member, Institute for Translational Medicine and Therapeutics
Senior Fellow, Penn Institute for Biomedical Informatics
Associate Director, Biomedical Informatics Educational Programs, Penn Institute for Biomedical Informatics
Chair, Community Affairs and Professional Development Committee, Department of Biostatistics, Epidemiology and Informatics
Associate Director, Environmental Health Informatics Core, Center of Excellence in Environmental Toxicology, University of Pennsylvania
Department: Biostatistics and Epidemiology
Contact information
3600 Civic Center Blvd
5E 300-A
Philadelphia, PA 19104
5E 300-A
Philadelphia, PA 19104
Office: 215-573-5571
Graduate Group Affiliations
Publications
Education
B.S. (Molecular Genetics)
University of Vermont, 2014.
M.A. (Biomedical Informatics)
Columbia University, 2016.
MPhil (Biomedical Informatics)
Columbia University, 2018.
PhD (Biomedical Informatics)
Columbia University, 2019.
B.S. (Molecular Genetics)
University of Vermont, 2014.
M.A. (Biomedical Informatics)
Columbia University, 2016.
MPhil (Biomedical Informatics)
Columbia University, 2018.
PhD (Biomedical Informatics)
Columbia University, 2019.
Links
Search PubMed for articles
The Romano Lab at the University of Pennsylvania
Joseph D. Romano - Personal Website
Joseph D. Romano - Google Scholar profile
Permanent linkSearch PubMed for articles
The Romano Lab at the University of Pennsylvania
Joseph D. Romano - Personal Website
Joseph D. Romano - Google Scholar profile
Description of Research Expertise
Joseph D. Romano is an Assistant Professor of Informatics and Pharmacology at the University of Pennsylvania. He earned a BS degree in Molecular Genetics from the University of Vermont, followed by MA, MPhil, and PhD degrees in Biomedical Informatics from Columbia University.The Romano Lab conducts original research at the interface of clinical/translational informatics and environmental health, with particular focus on developing new artificial intelligence (AI) models that explain the biology underlying environmental risk factors and mechanisms of disease. Other areas of research interest include autoimmune-related adverse events, maternal environmental health, and natural products drug discovery. Dr. Romano leads the development of several major biomedical knowledge bases, including ComptoxAI, VenomKB, and the Alzheimer’s Knowledge Base.
Selected Publications
Cleaves T, Semmel E, Marsolo K, & Romano JD: Scaling a National OMOP Registry: Informatics Lessons from the ASH RC Sickle Cell Disease Data Hub. American Medical Informatics Association 2026 Annual Symposium Nov 2026.Nguyen TA, Su W, Rajagopalan A, Abdurezak N, Hewryk OSI, & Romano JD: Clinical Knowledge Representation in Data Science. Annual Review of Biomedical Data Science 9: 239-263, Aug 2026.
Albi G, Dagliati A, Bellazzi R, & Romano JD: Semantic Knowledge Improves Molecular Machine Learning for Chemical Toxicity Prediction. iScience 29(8): 116937, Aug 2026.
Hewryk OSI & Romano JD: A Phyto-Ontology for Integrative Therapeutic Discovery. Lecture Notes in Artificial Intelligence. P. Andreev et al. (eds.). Springer Nature Switzerland, 16749(2): 68-72, July 2026 Notes: (accepted, in-press).
Aldeia GSI, Romano JD, de Franca FO, Herman DS, & La Cava WG: Towards symbolic regression for interpretable clinical decision scores. Philosophical Transactions A 384(2317): 20240588, April 2026.
Abdurezak N, Pan IT, Sacksith K, Apostolidis S, & Romano JD: Defining an Electronic Health Record-Based Phenotyping Algorithm for Immune-Related Adverse Events. 2026 Penn Colton Center for Autoimmunity Symposium, Philadelphia, PA April 2026.
Rajagopalan A, Nguyen TA, Guare LA, Garao Rico AL, Venkatesh R, Caruth L, Regeneron Genetics Center, Penn Medicine BioBank, Verma A, Ritchie MD, Hall MA, *Setia-Verma S, *Romano JD: DRIVE-KG: Enhancing variant-phenotype association discovery in understudied complex diseases using heterogeneous knowledge graphs. 2026 Pacific Symposium on Biocomputing 31: 830-848, Jan 2026 Notes: doi: 10.1142/9789819824755_0060; *co-corresponding/co-senior authors on publication.
Kumar R, Romano JD, & Ritchie MD: Learnable Protein Representations in Computational Biology for Predicting Drug-Target Affinity. Journal of Cheminformatics 18(1): 25, Jan 2026 Notes: doi: 10.1186/s13321-025-01145-7.
Paris CF, Ledyard R, Just AC, South EC, Nguemeni Tiako MJ, Canelon SP, Burris HH, & Romano JD: The Relative Importance of Neighborhood Environment Features in Explaining Preeclampsia Risk using Machine Learning. Pregnancy 2026 Notes: (accepted; in-press).
Andemariam B, Torres K, & Romano JD: Explainable AI-based prediction of chronic kidney disease as a long-term outcome of sickle cell disease in a large, multi-site observational data cohort. American Society of Hematology 2025 Annual Meeting Dec 2025.