Welcome

I am an Assistant Professor of Biostatistics 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, with a focus on public health and clinical medicine. This includes designing robust, efficient, and targeted estimators for learning causal effects using large-scale data generated from observational studies and randomized trials. My current areas of interest include causal inference, conformal inference, data integration, federated learning, and survival analysis.

I received my PhD in Biostatistics at Harvard University, where I was advised by Tianxi Cai and Lorenzo Trippa. I completed a postdoc in Health Care Policy at Harvard Medical School, mentored by Sharon-Lise Normand. I hold 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.

Outside of work, I enjoy playing golf, staying up-to-date on global affairs, and cheering on the UNC Tar Heels.

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 News and Upcoming Events

May 13, 2025. [Biostatistics Acceptance] A Surrogate Endpoint Based Provisional Approval Causal Roadmap, Illustrated by Vaccine Development is accepted in Biostatistics.

• Joint work with Peter Gilbert, James Peng, Theis Lange, Yun Lu, Lei Nie, Mei-Chiung Shih, Salina Waddy, Ken Wiley, Margot Yann, Zafar Zafari, Debashis Ghosh, Dean Follmann, Michael Juraska, and Ivan Diaz.

May 5, 2025. [Methods Workshop] I am giving a Methods Workshop at the Brandeis-Harvard SPIRE Center titled “Multi-Source Causal Inference Leveraging Transfer Learning”.

May 1, 2025. [ICML 2025 Acceptance] “Bridging Fairness and Efficiency in Conformal Inference” has been accepted at ICML 2025!

• Joint work with Chenyin Gao and Peter Gilbert.

Apr 10, 2025. [HDSI Causal Seminar Discussant] I am the discussant for Elias Bareinboim’s talk “Towards Causal Artificial Intelligence” at the Harvard Data Science Initiative Causal Seminar.

Mar 17, 2025. [JASA Publication] Federated Adaptive Causal Estimation (FACE) of Target Treatment Effects is published in Journal of the American Statistical Association.

• Joint work with Jue Hou, Kelly Cho, Rui Duan, and Tianxi Cai.

Jan 30, 2025. [New preprint] Targeted Data Fusion for Causal Survival Analysis Under Distribution Shift is available on Arxiv.

• Joint work with Yi Liu, Alex Levis, Ke Zhu, Shu Yang, and Peter Gilbert.

Jan 16, 2025. [New preprint] COADVISE: Covariate Adjustment with Variable Selection in Randomized Controlled Trials is available on Arxiv.

• Joint work with Yi Liu, Ke Zhu, and Shu Yang.

Jan 8, 2025. [Spring 2025 - Teaching PHTH 6800: Causal Inference in Public Health Research] I will teach a PhD-level first course in causal inference.

Jan 6, 2025. [JASA Publication] Robust Inference for Federated Meta-Learning is published in Journal of the American Statistical Association.

• Joint work with Zijian Guo, Xiudi Li, and Tianxi Cai.

Dec 17, 2024. [New preprint] On the Role of Surrogates in Conformal Inference of Individual Causal Effects is available on Arxiv.

• Joint work with Chenyin Gao and Peter Gilbert.

Dec 17, 2024. [ICSDS 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.

• Joint work with Max Rubinstein, Denis Agniel, Marcela Horvitz-Lennon, and Sharon-Lise Normand.

Nov 26, 2024. [NEJM Evidence Publication] Truncated, Not Forgotten — Handling Left Truncation in Time-to-Event Studies is published in NEJM Evidence.

Jul 23, 2024. [The American Journal of Drug and Alcohol Abuse 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.

• Joint work with Jemar Bather, Alex Bennet, Luther Elliott, and Melody Goodman.

Jul 18, 2024. [WebENAR: Van Ryzin Award Highlights] Identifying Surrogate Markers in Real-world Comparative Effectiveness Research is published in Statistics in Medicine.

• Joint work with Xuan Wang and Tianxi Cai.

May 15, 2024. [ICML 2024 Publication] Multi-Source Conformal Inference Under Distribution Shift has been accepted at ICML 2024! [Code].

• Joint work with Yi Liu, Alex Levis, and Sharon-Lise Normand.

Apr 6, 2024. [AOAS Publication] Privacy-Preserving, Communication-Efficient, and Target-Flexible Hospital Quality Measurement is published in the Annals of Applied Statistics.

• Joint work with Yige Li, Bijan Niknam, and Jose Zubizarreta.

Jan 8, 2024. [Spring 2024 - Teaching HSCI 5151: Methods for Observational Studies 2] Boston, MA.

Dec 13, 2023. [NeurIPS 2023 Publication] I will present Multiply Robust Federated Estimation of Targeted Average Treatment Effects in New Orleans, LA.

• Joint work with Zhu Shen and Jose Zubizarreta.

Jul 25, 2023. [NEJM Evidence Publication] Breaking Free from the Hazard Ratio: Embracing the Restricted Mean Survival Time in Clinical Trials is published in NEJM Evidence.

May 1, 2023. [Harvard Ph.D. Dissertation Defense!] Boston, MA.
On Causal Inference in Real World Settings.

• Thank you to my committee: Tianxi Cai, Lorenzo Trippa, Rui Duan, and Sebastian Schneeweiss!