Yong Chen

Yong Chen, PhD, is an Assistant Professor of Biostatistics specializing in advanced statistical methodologies for healthcare data analysis. His research bridges biostatistics, machine learning, and real-world evidence generation with significant contributions to federated learning frameworks and meta-analytic techniques.

His core research interests include:

• Statistical methods for electronic health record data
• Meta-analysis and network meta-analysis
• Mathematical statistics with healthcare applications
• Federated learning for decentralized research networks
• Causal inference methods for observational data
• Real-world evidence generation in rare diseases

Analysis of Dr. Chen's 2025 publications reveals a dominant focus on privacy-preserving distributed analytics for multi-site healthcare studies, particularly through one-shot lossless algorithms (e.g., COLA-GLM). His work demonstrates methodological innovation in addressing unmeasured confounding through negative control calibration and advancing multitask learning frameworks. Key application areas include SARS-CoV-2 sequelae analysis, neuropsychiatric outcomes, and disparities in obesity treatment access, consistently leveraging electronic health records for real-world evidence generation.

Scientific recognition:

• FACMI (Fellow of the American College of Medical Informatics)