I am now a Postdoctoral Fellow in the Department of Statistics at Harvard University, under the supervision of Prof. Zheng (Tracy) Ke. I obtained my Ph.D. degree in Statistics at UC Davis, where I was fortunate to be co-advised by Prof. Krishna Balasubramanian and Prof. Wolfgang Polonik. Before coming to UC Davis, I received my Bachelor’s degree in Math at Fudan University, where I was advised by Prof. Lei Shi.

My current research lies at the intersection of three domains: Statistics/Applied Probability, Computer Science, and applications in Economics, Biostatistics, and Casual inference. To be precise, I am interested in the following problems:

• Statistical foundations for learning theories: Focus on generalization, transferability, and interpretability of deep learning and machine learning via principled statistical frameworks.
• Uncertainty quantification in Biostatistics, Market Analysis, and Causal Inference related problems.

I have been trained in normal approximation of geometric and topological statistics, nonparametric statistical learning and network analysis as Ph.D. and Postdoctoral fellow.

Recent News

• I will give a talk titled `On the nonasymptotic statistical inferences via stabilization theory of Gaussian approximation bounds’ at the 22nd INFORMS Applied Probability Society Conference at Georgia Institute of Technology from June 30th to July 3rd, 2025.
• Our recent work Gaussian and Bootstrap Approximation for Matching-based Average Treatment Effect Estimators is now avaible on Arxiv! Taking ATE as an example, we provide a general framework for non-asymptotic statistical inference via a local geometric concept called `stabilization’.
• I joined the Department of Statistics, Harvard University, on September 1, 2024, as a Postdoctoral Fellow.