Research Interests
Causal inference, federated learning, survival analysis, quality measurement
Overview
Larry Han develops novel statistical and machine learning methodology to synthesize real-world data and integrate information from heterogeneous data sources to improve decision-making in clinical medicine and public health. His research interests include causal inference, conformal prediction, federated learning, transfer learning, surrogate markers, quality measurement, healthcare operations, and sensitivity analysis. His applied interests include clinical trial design and the safe, efficient, and robust use of observational study data such as electronic health records. In addition to his methodological research, he has experience leading epidemiological studies in disease areas such as COVID-19, cardiology, dementia, and infectious diseases (e.g., HIV, STIs, malaria).
Selected Publications
Han, L.; Li, Y.; Niknam, B.; Zubizarreta, J. (2023), “Privacy-Preserving, Communication-Efficient, and Target-Flexible Hospital Quality Measurement.” Annals of Applied Statistics.
Han, L., Shen, Z., and Zubizarreta, J. R. (2023), “Multiply Robust Federated Estimation of Targeted Average Treatment Effects,” Advances in Neural Information Processing Systems (NeurIPS).
Han, L.; Arfe, A.; Trippa, L. (2023), “Sensitivity Analyses of Clinical Trial Designs: Selecting Scenarios and Summarizing Operating Characteristics.” The American Statistician.
Han, L.; Wang, X.; Cai, T. (2022), “Identifying Surrogate Markers in Real-World Comparative
Effectiveness Research.” Statistics in Medicine.
Han, L.; Duan, R.; Cai, T. (2021), “Federated Adaptive Causal Estimation (FACE) of Target Treatment Effects.” Arxiv.
Selected Public Service
Associate Editor, Journal of Causal Inference, 2023-Present
Review Committee, American Statistical Association Statistics in Epidemiology Young Investigator Award, 2023-Present
Review Committee, Morehead Cain Scholarship, 2018–Present
Courses
HSCI 5151 Methods for Observational Research 2
