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
Ding, P., Fang, Y., Lu, S., Shi, L. and Zhang, W. (Alphabetical order) Design-based causal inference in bipartite experiments. [Slides]
Shi, L., Wang, J. and Ding, P. (2024) Forward selection and post-selection inference in factorial designs. Annals of Statistics. Accepted. [Slides]
Shi, L. and Li, X. (2024) Some theoretical foundations on the design and analysis of randomized experiments. Journal of Causal Inference. Accepted.
Shi, L. and Ding, P. (2022+) Berry–Esseen bounds for design-based causal inference with possibly diverging treatment levels and varying group sizes. [Slides]
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
Shi, L., Wang, G. and Zou, C. (2024) Low-Rank Matrix Estimation in the
Presence of Change-Points. Journal of Machine Learning Research. [Distinguished Student Paper Award Winner for ENAR 2023]
Cui, X., Shi, L., Zhong, W. and Zou, C. (2023) Robust high-dimensional low-rank matrix estimation: optimal rate and data-adaptive tuning. Journal of Machine Learning Research.
Shi, L. and Zou, C. (2020) Noisy Low Rank Matrix Completion Under General Bases. Stat.
Shi, L., Yang, H. and Xue, L. (2023+) Oracle Inequalities for Sparse Principal Component Analysis based on the Linear Manifold Approximation.
Collaborative research
Barbehenn, A., Shi, L., Shao, J., Hoh, R., Hartig, H.M., Pae, V., Sarvadhavabhatla, S., Donaire, S., Sheikhzadeh, C., Milush, J., Laird, G.M., et al. (2024)
Rapid Biphasic Decay of Intact and Defective HIV DNA Reservoir During Acute Treated HIV Disease. Nature Communications. Accepted.
Shi, L., Pang, H., Chen, C., Zhu, J. (2024+) rdborrow: An R package for Causal Inference Incorporating External Controls in Randomized Trials with Longitudinal Outcomes. [Slides]
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
Application work
Collaborators
I would like to thank many amazing collaborators: Sulggi Lee, Guanghui Wang, Xiaolong Cui, Wei Zhong
|