My research interests lie in statistical inference and statistical learning in the computer age, motivated by questions arising in clinical trials, epidemiology, and precision medicine. I am particularly drawn to the interplay between flexible computational methods and the theoretical principles that make their conclusions reliable and explainable. My goal is to develop methods that improve efficiency and reveal meaningful structure while providing a rigorous account of uncertainty, assumptions, and limitations.

My current interests include:

• Survival and longitudinal analysis: Inference for censored and repeated-measures data and time-varying effects. I am interested in developing methods to mitigate the impacts of model misspecification on estimation, statistical power, and interpretation, with a particular focus on nonparametric approaches.

• Statistical learning and structured regularization: Sparse and high-dimensional methods that exploit scientifically meaningful structure. I hope to study the relationship between structural selection, estimation, and valid inference.

• Causal inference and precision medicine: Counterfactual reasoning, identification, heterogeneous treatment effects, and individualized treatment decisions. I am especially interested in causal questions within randomized trials, including treatment switching and other intercurrent events, as well as covariate adjustment (originated from the causal inference literature) for more efficient treatment-effect estimation.

• Clinical-trial design, adaptive, and Bayesian methods: Power and sample-size calculations under uncertain planning assumptions, information-based designs, and Bayesian methods.

𝙷𝚒! 𝙸’ m Yu Lu – 卢钰

🌱 I am a thorough follower of Thomas Bayes, Pierre-Simon Laplace, Frank Ramsey, and Bruno de Finetti. I am exploring the direction of statistics that I most enjoy.

I enjoy running simulation since at this time I could stand on the perspective of the god and only god knows the real equation and the real population. Luckily, probability and statistics help human beings to approximate that.

Good statisticians could even intuitively guess the result of the simulation. Anyway, Prof. Persi Diaconis said that “computer simulation is replacing theorems” but the question is “how do you tell the computer to shuffle cards, which is totally the meaning of the charismatic probability and mathematics”

I really enjoy chatting with people about math knowledge, reading good books about stat. and prob. But only enjoying is totally not enough—“We want something more than mere theory and preaching now, though.” —Sherlock Holmes 我们不能坐而论道，必须有所作为。

Let’s enjoy the world by statistics, analysis, equations, a pounding heart and a burning soul!

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