I am a PhD candidate in Biomedical Informatics and Data Science at Johns Hopkins University, with over seven years of experience developing AI and machine learning methods for healthcare. My work spans bayesian modeling, federated analytics, natural language processing, and multimodal modeling, with a focus on health equity and real-world clinical impact.
Research Themes
Bayesian Hierarchical Transfer Learning
- Hierarchical transfer learning frameworks that borrow strength from large datasets to improve inference in small or underrepresented populations.
- Developed global–local shrinkage priors for high-dimensional sparse settings and efficient rejection-sampling algorithms to enable scalable posterior inference in federated and clinical/genetic applications.
Real-World Evidence & Causal Inference
- Conducting large-scale federated causal analyses across 10+ international healthcare databases (>5M patients) to evaluate sex difference in the effectiveness and safety of second-line T2D agents.
- Quantify the impact of social determinants of health on diabetes management.
Natural Language Processing in Healthcare
- Specialty referral triage, phenotype extraction, sentiment classification, and parameter-efficient fine-tuning in resource-constrained healthcare settings.
Multimodal Models
- Developing cross-attention architectures to integrate imaging, biomarkers, and clinical text for Alzheimer’s disease diagnosis.
- Emphasize low-compute, high-efficiency modeling strategies that maintain performance under limited data and infrastructure constraints.
Education
- PhD, Biomedical Informatics and Data Science, The Johns Hopkins University (2021 - Present)
- MS, Computational Biology, Harvard T.H. Chan School of Public Health (2018-2020)
- BS, Biological Science, Nanjing University (2014-2018)
Experiences
- Research Intern, Advanced Intelligence, Eli Liily and Company
- Data Scientist, Computational Health Informatics Program, Boston Children’s Hospital
- Research Assistant, Dana-Farber Cancer Institute
- Research Analyst, Global Health Research Center, Duke Kunshan University