About Me

Welcome! I am a PhD candidate in Statistics at George Mason University, advised by Dr. David Kepplinger and Dr. Lily Wang. My research focuses on trustworthy AI, with an emphasis on robustness, scalability, and valid uncertainty quantification. I develop statistically principled methods for high-dimensional data on complex and irregular domains, including images, surfaces, and multivariate time series, drawing on robust nonparametric statistics, functional data analysis, geometry-adapted smoothing, and scalable distributed computation to handle realistic challenges such as heavy-tailed noise, acquisition artifacts, missing modalities, and imperfect AI-generated surrogates.

My current work spans two connected streams. The first centers on imaging: I build distributed robust regression for brain imaging on complex spatial domains, synthetic-surrogate inference frameworks that integrate AI-generated scans while guarding against surrogate misspecification, and illuminant spectrum inference for multispectral ecological imaging. The second develops covariate-assisted learning for replicated tensor time series, focusing on dependence-driven dimension reduction to extract directions of covariate-modulated serial structure. More broadly, I am interested in developing trustworthy data-analytic pipelines that are interpretable, reproducible, and scientifically reliable across high-dimensional and structurally complex settings.

Research Interests

• Trustworthy AI for complex data

 

• Robust statistics and uncertainty quantification

 

• Functional data analysis on complex domains

 

• Distributed and scalable statistical learning

 

• Multispectral imaging data analysis

 

• Multivariate time series analysis

 

• Non-convex optimization for robust estimation

News

• April 2026: I successfully defended my PhD dissertation, “Trustworthy AI Through Robust Functional Data Analysis and Statistical Inference for Imaging Data.” I am sincerely grateful to my dissertation director, Dr. David Kepplinger, co-director, Dr. Lily Wang, and committee members, Dr. Anand Vidyashankar and Dr. Zeda Li, for their invaluable guidance and support throughout this journey. I would also like to thank my collaborators, Dr. Guanqun Cao, Dr. Zhiling Gu, Dr. Daniel Hanley, Dr. Guannan Wang, and Dr. Shan Yu, for their support and collaboration.

 

• March 2026: My poster, “Synthetic Surrogate Functional Regression (SSFR) for AI-Augmented Neuroimaging”, received second place in the student poster competition at the StatConnect@AI Conference at Georgetown University.

 

• March 2026: I am excited to share that I accepted a postdoctoral associate offer from the Yale School of Public Health, where I will work under the supervision of Dr. Yize Zhao starting in August. I am deeply grateful to everyone who has supported me along the way.

 

• July 2025: My work, “Robust Mean Signal Estimation and Inference for Imaging Data”, with Dr. Guanqun Cao, Dr. David Kepplinger, and Dr. Lily Wang, was accepted for publication in Statistica Sinica.

 

• May 2025: My manuscript, “Robust Mean Signal Estimation and Inference for Imaging Data”, received the Student Paper Runner-Up Award (Theory and Methods) at the 2025 Statistical Methods in Imaging Conference.

 

• May 2025: I was honored to receive the Washington Statistical Society Outstanding Graduate Student Award from the Department of Statistics at George Mason University.