CV
A PDF version is available here: Yanfei Wang CV
Professional profile
I am an Assistant Scientist whose work sits at the intersection of causal inference, longitudinal clinical modeling, and real-world healthcare data. My research focuses on building decision-relevant methodological frameworks for electronic health records and claims data, with particular emphasis on treatment strategies, disease progression, and clinically interpretable evidence generation.
Selected highlights
- Develops methodological frameworks for target trial emulation, causal AI, and longitudinal treatment strategy evaluation using routine clinical data.
- Works across electronic health records, claims, genetic data, and multimodal clinical data, linking statistical methodology with real clinical questions.
- Published in venues including npj Digital Medicine, Advanced Science, npj Precision Oncology, Cardio-Oncology, JAMIA, and Nucleic Acids Research.
- Combines method development, translational collaboration, grant writing, and reproducible clinical data workflows.
Research strengths
Causal inference and target trial emulation
My current work emphasizes target trial emulation, time-varying treatment definitions, support-aware estimation, and decision-aligned analysis for longitudinal EHR and claims data.
Longitudinal and interpretable clinical modeling
I develop longitudinal disease progression models and clinically interpretable modeling strategies that connect predictive structure with decision-relevant questions.
Translational data science in biomedicine
My work spans oncology, cardiovascular safety, neurodegeneration, chronic disease progression, and precision medicine, combining methodological rigor with applied clinical relevance.
Current and prior positions
Assistant Scientist, 2026 to present
- Leading methodological work in causal AI and target trial emulation for longitudinal EHR and claims data
- Developing frameworks for add-on, switching, overlap, lag-aware, and time-updated treatment strategies
- Contributing to grant writing, analytic design, and interdisciplinary collaboration across clinical and quantitative teams
Postdoctoral Associate, 2024 to 2025
- Developed statistical and machine learning frameworks for sparse, high-dimensional biomedical data
- Combined causal inference methods with neural-network-based counterfactual modeling
- Led EHR data standardization and ontology-aligned clinical data workflows
- Conducted GWAS, polygenic risk score analyses, and survival modeling
Graduate Research Assistant, 2019 to 2023
- Developed deep learning and causal discovery approaches for longitudinal clinical data
- Studied disease heterogeneity using Gaussian process modeling and interpretability methods
- Conducted genomic and causal analyses, including GWAS and Mendelian randomization
- Designed counterfactual and domain-adaptive modeling approaches for treatment effect estimation
Education
- PhD in Biomedical Informatics, University of Texas Health Center Houston, 2019 to 2023
- Master of Statistics, George Washington University, 2016 to 2018
- Bachelor of Mathematics, University of Colorado Denver, 2011 to 2015
Professional service
- Ad hoc reviewer for Scientific Reports, BMC Cancer, and IEEE ICH
- Member of AMIA
- Programme Committee for International Digital Public Health Conference 2026 and IEEE ICHI 2026
- Reviewer for the 2026 to 2027 PHHP AI PhD Fellowship