Research Interests
Causal inference, conformal inference, federated learning, infectious diseases, surrogate markers, survival analysis, quality measurement
Overview
Larry Han develops novel statistical and machine learning methods motivated by high-stakes challenges in clinical medicine and public health. His work focuses on integrating heterogeneous real-world data to improve decision-making in contexts such as congenital heart defects and vaccine efficacy. His research advances causal inference, conformal prediction, federated learning, surrogate markers, survival analysis, and quality measurement. Beyond methodology, he has led epidemiological studies across diverse disease areas, including COVID-19, cardiology, mental health, and infectious diseases such as HIV, STIs, and malaria.
Selected Publications
Han, L.; Hou, J.; Cho, K.; Duan, R.; Cai, T. (2025), “Federated Adaptive Causal Estimation (FACE) of Target Treatment Effects.” Journal of the American Statistical Association.
Guo, Z.; Li, X.; Han, L.; & Cai, T. (2025), Robust Inference for Federated Meta-Learning. Journal of the American Statistical Association.
Han, L. (2024),“Truncated, Not Forgotten — Handling Left Truncation in Time-to-Event Studies.” NEJM Evidence.
Liu, Y.; Levis, A.; Lise-Normand, S.; Han, L. (2024), “Multi-Source Conformal Inference Under Distribution Shift.” Proceedings of the 41st International Conference on Machine Learning (ICML).
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. (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. (2023), “Breaking Free from the Hazard Ratio: Embracing the Restricted Mean Survival Time in Clinical Trials.” NEJM Evidence.
Han, L.; Wang, X.; Cai, T. (2022), “Identifying Surrogate Markers in Real-World Comparative Effectiveness Research.” Statistics in Medicine.
Selected Public Service
Associate Editor, Health Services and Outcomes Research Methodology, 2024-Present
Associate Editor, Journal of Causal Inference, 2023-Present
Organizing Committee, New England Rare Disease Statistics Workshop, 2025-Present
Scientific Committee, ASA Boston Pharmaceutical Statistics Symposium, 2024-Present
Steering Committee, Annual Symposium on Risks and Opportunities of AI in Pharmaceutical Medicine
Awards Committee, ASA Statistics in Epidemiology Young Investigator Award, 2023-Present
Review Committee, Morehead Cain Scholarship, 2018–Present
Courses
PHTH 6800 Causal Inference for Public Health Research
HSCI 5151 Methods for Observational Research 2
