I am a tenure-track Assistant Professor in Statistic at Rice University. Previously, I was a William H. Kruskal Instructor in Statistics at the University of Chicago. I obtained my Ph.D. in Statistics at Washington University in St. Louis in 2024, advised by Likai Chen and Todd Kuffner. I was also fortunate to be a visiting student at UChicago advised by Wei Biao Wu in 2022-2024. Prior to the graduate school, I received my B.S. in Mathematics at Soochow University in 2019, advised by Rui Ding. My research is supported by the National Science Foundation. Here is a copy of my [CV].

My research interests include machine learning theory, time series, high-dimensional data, change points, and their applications in neuroimaging and brain dynamics. In particular, I provide mathematical foundations for machine learning from a time series perspective. I also derive interpretable theoretical findings for the real-world data, especially for the human brain connectome, to predict, intervene and even prevent neurological disorders like Alzheimer's disease.

Contact: firstname dot lastname at rice dot edu [Google Scholar]

News

• May 2026: Our paper on online quantile estimation has been accepted to IEEE Transactions on Information Theory! New techniques for stationary distributions of SGD with non-strongly convex and nonsmooth objectives!
• Feb 2026: Our work on recursive statistical inference for SGD with dropout regularization has been accepted to JMLR! Novel tools for online undertainty quantification in ML!
• Sep 2025: Excited to share that all three of my papers on SGD theory have been accepted to NeurIPS! Call it beginner’s luck in AI/ML. Huge thanks to my amazing collaborators!
• Jun 2025: I am so grateful to have my first ever proposal awarded by NSF DMS! Can't wait to start our exciting works on online statistical inference for modern machine learning!
• May 2025: A new preprint on statistical guanrantees for high-dimensional SGD! What are the scaling limits of SGD in large-scale training? Complexity and probabilistic bounds are provided!
• Nov 2024: I will be organizing a session on Statistical Foundations and Inference for Machine Learning at the ASA SLDS Conference at Newport Beach. Check out our nice talks at South Cal!
• May 2024: I obtained my Ph.D. degree in Statistics! I am extremely grateful to be in press interviewed by the new Department of Statistics and Data Science at WashU!
• Mar 2024: I was invited to visit the Department of Applied Mathematics at University of Twente and the Department of Economics, Econometrics & Finance at University of Groningen to give two seminar talks. Insightful conversations with the nice faculty members there!
• Mar 2024: I will be presenting our work on SGD theory from a nonlinear time series perspective at the 15th Workshop on Stochastic Models, Statistics and Their Applications (SMSA 2024) in TU Delft, Netherlands!
• Feb 2024: Our work on L^2 inference of change points in high-dimensional time series has been accepted to the Annals of Statistics (AOS)! It's been my first paper during the PhD training! I greatly appreciate all the support from my coauthors Likai, Weining and Wei Biao!
• Sep 2023: Our Github repo 'MachineLearning_NetworkLevelAnalysis' on machine learning pipelines for functional brain connectivity analysis is now public! First time provide biological interpretation for ML results by the network-level enrichment. Have a try!
• Aug 2023: I will be chairing an IMS-sponsored session and presenting our new work on Adaptive MOSUM: Inference for Multiscale Change Points in High-Dimensional Time Series at JSM in Toronto, Canada!
• Jul 2023: Our new R package 'L2hdchange' for high-dimensional change-point testing and estimation is now available on CRAN! Interesting examples such as COVID-19 outbreaks are included. Check them out!
• Jul 2023: I will be presenting our new work on SGD at the Data Science and Dependence 2023 Conference in Heidelberg, Germany! For the first time, we introduce time series to ML for more exciting theoretical findings!
• Oct 2022: I will be chairing a session at Women in Statistics and Data Science Conference on Oct 8, 2022!
• Aug 2022: Check out our paper on L^2 inference of change points in high-dimensional time series at JSM in Washington, DC!
• Jun 2022: I will be presenting a poster on the network level analysis for biological interpretation of machine learning results at the Annual Meeting of The Organization for Human Brain Mapping (OHBM) in Scotland!
• Feb 2021: I have been awarded the Best Talk in the Workshop: Machine Learning Tools Applied on Omics in Neuroscience organized by WashU!