Welcome
I am an Assistant Professor in the Department of Public Health and Health Sciences at Northeastern University and an Affiliate Investigator in the Vaccine and Infectious Disease Division at the Fred Hutch Cancer Center.
My research focuses on developing novel statistical and machine learning methods to leverage real-world data to improve decision-making in public health and clinical medicine. This involves designing robust, efficient, and targeted estimators for causal effects using large-scale data generated from electronic health records and clinical trial data. Active areas of research include causal inference, conformal inference, data integration, federated learning, and survival analysis.
I obtained my PhD in Biostatistics at Harvard University, advised by Professor Tianxi Cai and Professor Lorenzo Trippa. I completed a postdoctoral fellowship in Health Care Policy at Harvard Medical School, advised by Professor Sharon-Lise Normand. I received an AM in Biostatistics from Harvard, an MPhil in Healthcare Operations from the University of Cambridge, an MA in Global Affairs from Tsinghua University, and a BS in Public Health and Biostatistics from UNC - Chapel Hill.
I enjoy playing golf, especially in my home state of North Carolina, reading history, especially biographies, and staying up-to-date with global affairs, especially US-China relations. Feel free to reach out via email: lar.han@northeastern.edu.
[I currently have openings for postdocs, PhD students, and research assistants! Feel free to contact me if you’d like to learn more about our research.]
Recent and Upcoming News
Apr 10, 2025. [Discussant] I will be the discussant for Elias Bareinboim’s talk “Towards Causal Artificial Intelligence” at the Harvard Data Science Initiative Causal Seminar.
Apr 4, 2025. [Invited talk] I will present “On the Role of Surrogates in Conformal Inference of Individual Causal Effects” at the Department of Management Science and Statistics at the University of Texas at San Antonio.
Mar 14, 2025. [Invited talk] Yi Liu will present our work, “Targeted Data Fusion for Causal Survival Analysis Under Distribution Shift” at the University of Washington Biostatistics Working Group.
Mar 14, 2025. [Invited talk] Yi Liu will present our work, “COADVISE: Covariate Adjustment with Variable Selection in RCTs” at the ASA Covariate Adjustment Working Group Journal Club.
Jan 30, 2025. [New preprint] Targeted Data Fusion for Causal Survival Analysis Under Distribution Shift is available on Arxiv.
Jan 16, 2025. [New preprint] COADVISE: Covariate Adjustment with Variable Selection in Randomized Controlled Trials is available on Arxiv.
Jan 8, 2025. [Teaching] I will teach a PhD-level first course in causal inference, PHTH 6800: Causal Inference in Public Health Research in Spring 2025.
Jan 6, 2025. [Publication] Robust Inference for Federated Meta-Learning has been published in Journal of the American Statistical Association.
Jan 5, 2025. [Acceptance] Federated Adaptive Causal Estimation (FACE) of Target Treatment Effects has been accepted at Journal of the American Statistical Association.
Dec 17, 2024. [New preprint] On the Role of Surrogates in Conformal Inference of Individual Causal Effects is available on Arxiv.
Dec 17, 2024. [Invited talk] I will present “On the Role of Surrogates in Conformal Inference of Individual Causal Effects” at the International Conference on Statistics and Data Science (ICSDS) in Nice, France.
Nov 27, 2024. [New preprint] Bounding Causal Effects with an Unknown Mixture of Informative and Non-informative Censoring is available on Arxiv.
Nov 26, 2024. [Publication] Truncated, Not Forgotten — Handling Left Truncation in Time-to-Event Studies is published in NEJM Evidence.
Nov 21, 2024. [Talk] I will present at the UPenn Center for Causal Inference.
Nov 17, 2024. [Acceptance] Robust Inference for Federated Meta-Learning has been accepted at Journal of the American Statistical Association.
Nov 13, 2024. [Talk] I will present at the Duan Lab in the Department of Biostatistics at Harvard University.
Nov 7-8, 2024. [Invited talk] I will present at the Forum on the Integration of Observational and Randomized Data (FIORD) in Bethesda, MD.
Sep 10, 2024. [Invited talk] I will present “Multi-Source Conformal Inference Under Distribution Shift” at the Department of Biostatistics at NYU Langone.
Aug 5-8, 2024. [JSM 2024] I will present in the session New methods for integrative and adaptive analysis in Portland, OR.
Jul 24-26, 2024. [ICML 2024] I will present Multi-Source Conformal Inference Under Distribution Shift in Vienna, Austria.
Jul 23, 2024. [Publication] Detecting Univariate, Bivariate, and Overall Effects of Drug Mixtures using Bayesian Kernel Machine Regression is published in The American Journal of Drug and Alcohol Abuse.
Jul 18, 2024. [WebENAR: Van Ryzin Award Highlights] Identifying Surrogate Markers in Real-world Comparative Effectiveness Research.
Jul 11, 2024. [New preprint] A Surrogate Endpoint Based Provisional Approval Causal Roadmap is available on Arxiv.
Jul 8-10, 2024. [Invited session organizer and speaker] at the International Conference on Frontiers of Data Science in Hangzhou, China.
Jul 4-7, 2024. [Invited talk] I will present at the National Committee on U.S.-China Relations - Schwarzman Scholars Global Health Seminar in Geneva, Switzerland.
Jun 24, 2024. [Invited talk] I will present at the Department of Health Care Policy at Harvard Medical School.
May 16, 2024. [ACIC 2024] I will present at the American Causal Inference Conference (ACIC) in Seattle, WA.
May 15, 2024. [Acceptance] Multi-Source Conformal Inference Under Distribution Shift has been accepted at ICML 2024! [Github]
Apr 6, 2024. [New paper] Privacy-Preserving, Communication-Efficient, and Target-Flexible Hospital Quality Measurement is published in the Annals of Applied Statistics!