Biography
Hairong Wang supports clinical and rehabilitation research through data management, statistical analysis, study design and the development of reproducible analytical workflows. His work includes building research cohorts, analyzing longitudinal health data, developing predictive models and translating complex datasets into evidence that can inform clinical research and decision-making. With a background in data science, machine learning, network analysis and complex engineering systems, Wang brings an interdisciplinary perspective to health and rehabilitation research. He is especially interested in helping research teams use data thoughtfully and rigorously to address meaningful real-world questions, improve health outcomes, and strengthen practical, data-driven research skills among students and investigators.
Education
- Doctor of Philosophy in Environmental Engineering, Carnegie Mellon University
- Master of Science in Analytics, Georgia Institute of Technology
Research Interests
- Clinical and health data science
- Artificial intelligence and machine learning
- Longitudinal and real-world data analysis
- Predictive modeling
- Network analysis
- Causal and interpretable modeling
Publications
- “Integrating multimodal clinical data to predict intravenous (IV) fluid utilization: A comparative analysis of natural language processing techniques.”
- “Deep learning-assisted chrysotile asbestos screening in soil: A hierarchical statistical approach to error propagation.”
- “Beyond megawatts: Structural configurations and project ecologies in global utility-scale solar.”