Tianchen Qian

News.

• [Apr. 2025]      Our paper led by Jeremy Lin, titled "Micro-randomized Trials with Categorical Treatments: Causal Effect Estimation and Sample Size Calculation", is now available on arXiv (link).

• [Apr. 2025]      Our paper led by Jiaxin Yu, titled "Modeling time-varying effects of mobile health interventions using longitudinal functional data from Heartsteps micro-randomized trial", is accepted by Annals of Applied Statistics. Congratulations, Jiaxin!

• [Apr. 2025]      Ph.D. student Jeremy Lin's submission to AAIC, titled "Clustering Cognitive Decline Trajectories in Mild Alzheimer’s Dementia: Insights from the EXPEDITION3 Trial", was accepted as a poster presentation!

• [Apr. 2025]      Ph.D. student Jiaxin Yu won "The Beall Family Foundation Graduate Student Social Impact Award in Statistics". Congratulations, Jiaxin! 

• [Feb. 2025]      I gave a talk at the Banff Workshop of Emerging Statistical Methods for Digital Health Data on "Causal inference and machine learning in mobile health: modeling time-varying effects using longitudinal functional data" (video recording).

• [Feb. 2025]      Our paper titled "Distal Causal Excursion Effects: Modeling Long-Term Effects of Time-Varying Treatments in Micro-Randomized Trials" is now available on arXiv (link).

• [Nov. 2024]      Congratulations to Jeremy Lin, who won the UCI Statistics Outstanding TA Award for 2023-24!

• [Nov. 2024]      Our paper led by Jiaxin Yu, titled "Doubly robust estimation of causal excursion effects in micro-randomized trials with missing longitudinal outcomes", is now available on arXiv (link).

• [Nov. 2024]      I gave a talk at the Online Causal Inference Seminar on "Causal inference and machine learning in mobile health: modeling time-varying effects using longitudinal functional data" (video recording).

• [Oct. 2024]      Our paper led by Jiaxin Yu, titled "Modeling time-varying effects of mobile health interventions using longitudinal functional data from Heartsteps micro-randomized trial", is now available on arXiv (link).

Research. 

        The theme of our research group is developing data science tools for optimizing adaptive digital interventions. We contribute new methodology and software for

• causal inference — such as how to define and estimate causal effects in longitudinal settings with time-varying treatments;

• experimental design — such as how to design micro-randomized trials for mHealth interventions;

• policy learning — such as how to learn the optimal way of delivering interventions from offline or online data.

        We collaborate with scientists on digital intervention development from a variety of domains, including physical activity, substance abuse, smoking cessation, weight management, drinking behavior, anger management, and so on.

        We also have work in the following areas:

• semiparametric efficiency theory;

• clinical trial design and analysis;

• Alzheimer's disease and related dementia.

        Here are selected publications by topic, with more context on each of the directions.

Training and Education.

• 2017-2020: postdoc, Department of Statistics, Harvard University, mentored by Susan Murphy

• 2012-2017: Ph.D. in Biostatistics, Johns Hopkins University, advised by Constantine Frangakis and Michael Rosenblum

• 2008-2012: B.S. in Mathematics, Tsinghua University