Research

Research Interest

My current research interest includes:

  • High dimensional statistics: estimation, model selection and inference for static or dynamic high dimensional structures, including sparse PCA/CCA, robust low rank matrix recovery, change point detection in high dimensional regression, etc.

  • Causal inference: design and analysis of randomized experiments, complex causal mechanisms (such as interference, sorrogacy, delay and so on), novel causal methodology (such as data fusion)

  • Learning and computing: statistical learning/machine learning, reinforcement learning, deep learning, GenAI

  • AI policy/ethics: privacy and fairness in statistical related methodology and practice.

  • Statistical application: marriage between stats and biomedical study, social science, psychology, econ, etc.

Publications/Manuscripts

Design-based causal inference

Bandit, adaptive experiment and reinforcement learning

  • Shi, L., Arbour, D., Addanki, R., Sinha, R., Feller, A. (2025+) Kernel-Based Representation Learning for Experimentation with LLM-Generated Treatments. Submitted.

  • Shi, L., Wei, W. and Wang, J. (2024+) Using Surrogates in Covariate-adjusted Response-adaptive Randomized Experiments with Delayed Outcomes. NeurIPS 2024.

  • Shi, L., Wang, J. and Wu, T. (2023) Statistical inference on multi-armed bandits with delayed feedback. ICML 2023.

Theory of permutation and permutation tests

High dimension statistics

Collaborative research

Working projects

Causal inference

  • Nonparametric lower bounds for Cronbach's alpha in the presence of missingness

  • Estimation and inference in bipartite experiments

  • Theory of two-way independent permutation with application to multiple randomization designs

Adaptive experiments and reinforcement learning

  • Adaptive experiments with delayed feedback

Application work

Collaborators

I would like to thank many amazing collaborators: Sulggi Lee, Guanghui Wang, Xiaolong Cui, Wei Zhong