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Xia Jiang, PhD
- Associate Professor of Biomedical Informatics
- Affiliated Faculty, School of Computing and Information
- Affiliated Faculty, CMU-Pitt PhD Program in Computational Biology
Department of Biomedical Informatics (DBMI) | University of Pittsburgh School of Medicine
Biography
Dr. Jiang is an Associate Professor in the Department of Biomedical Informatics at the University of Pittsburgh School of Medicine, with affiliated appointments in the School of Computing and Information and the CMU-Pitt PhD Program in Computational Biology. Her research centers on artificial intelligence and machine learning methods — including Bayesian networks, causal modeling, and deep learning — for biomedical data analysis, disease risk prediction, and clinical decision support. She has made significant contributions to breast cancer outcome prediction, causal discovery, and the application of large language models in biomedical informatics.
Computational & Clinical Expertise
Dr. Jiang’s clinical expertise encompasses the application of artificial intelligence to support medical decision-making and improve patient care. Her work focuses on Bayesian networks, causal learning, deep learning, and large language models to develop interpretable approaches for clinical decision support, risk prediction, and biomarker discovery. She applies these methods to areas such as breast cancer metastasis prediction, cancer genomics, and the integration of multimodal clinical and genomic data.
Research Interests
Dr. Jiang’s research focuses on developing advanced artificial intelligence and machine learning methodologies — including Bayesian networks, causal inference, and deep learning — to analyze complex biomedical data and support clinical decision-making. Her work emphasizes integrating multimodal data sources such as genomic, clinical, and real-world health data to identify key risk factors and predict disease outcomes. A central application of her research is breast cancer metastasis prediction and personalized treatment recommendation. She also investigates causal discovery methods for understanding disease mechanisms and is exploring the use of large language models in biomedical informatics to improve knowledge extraction and clinical reasoning.
Specialties
- Artificial Intelligence in Healthcare
- Machine Learning and Deep Learning in Medicine
- Clinical Decision Support
- Bayesian Networks and Causal Learning
- Large Language Models in Biomedical Informatics
- Biomedical Data Science
Websites & Links
Education & Training
- B.S., EngineeringJiangXi University of Science and Technology, Ganzhou, China
- M.S., Mechanical EngineeringRose-Hulman Institute of Technology, Terre Haute, IN
- M.S., Computer ScienceNortheastern Illinois University, Chicago, IL
- Ph.D., Biomedical InformaticsUniversity of Pittsburgh, Pittsburgh, PA2005–2008
- NLM Postdoctoral Scholar / AssociateUniversity of Pittsburgh, Pittsburgh, PA2008–2011
Certifications
- Microsoft Technical Certifications (legacy)
Awards & Distinctions
- NIH K99/R00 Pathway to Independence AwardNational Institutes of Health / National Library of Medicine2010–2015
- National Library of Medicine Postdoctoral Training FellowshipNational Library of Medicine2008–2010
- Finalist Award, Student Paper CompetitionAmerican Medical Informatics Association2011
- Semi-finalist, Best Publication of the YearInternational Society for Disease Surveillance2008
Representative Publications
Xu C, Coen-Pirani P, Jiang X. Empirical Study of Overfitting in Deep Learning for Predicting Breast Cancer Metastasis. Cancers (Basel). 2023;15(7):1969.
Jiang X, Xu C. Deep Learning and Machine Learning with Grid Search to Predict Later Occurrence of Breast Cancer Metastasis Using Clinical Data. J Clin Med. 2022;11(19):5772.
Jiang X, Wells A, Brufsky A, Neapolitan RE. A Clinical Decision Support System Learned from Data to Personalize Treatment Recommendations Towards Preventing Breast Cancer Metastasis. PLoS ONE. 2019;14(3):e0213292.
Hill SM, Heiser LM, Cokelaer T, et al. (including Jiang X). Inferring causal molecular networks: empirical assessment through a community-based effort. Nat Methods. 2016;13(4):310–318.
Jiang X, Barmada MM, Visweswaran S. Identifying genetic interactions in genome-wide data using Bayesian networks. Genet Epidemiol. 2010;34(6):575–581.