Congratulations to Our Newest PhD Graduate, Dr. Mengkun Chen!

The Department of Statistics at Virginia Tech is proud to announce and celebrate the successful completion of Dr. Mengkun Chen's doctoral degree requirements.

Dr. Mengkun Chen successfully defended her dissertation titled "Robust functional-input hypothesis testing and multi-task regression on mixed-type graphs for high-dimensional, complex-structured Data" on July 24, 2026, a significant milestone representing years of rigorous work, dedication, and impactful research.

Title: Robust functional-input hypothesis testing and multi-task regression on mixed-type graphs for high-dimensional, complex-structured Data.

Abstract: High-dimensional, complex-structured data have become increasingly prevalent across modern scientific disciplines such as social sciences, omics, and medical imaging. As such data continue to proliferate, developing advanced statistical methodologies for scalable estimation and inference is essential for extracting meaningful insights and driving scientific discovery. This dissertation introduces three robust, flexible, and computationally efficient nonparametric methods for functional data analysis: (1) a flexible test for detecting unknown functional departures under generalized functional regression for biomedical group discrimination; (2) a robust functional-input kernel-machine-based test for identifying brain networks associated with health outcomes; and (3) a joint functional regression on mixed-type functional graphs for dynamic network modeling in brain imaging data. For the first method, we introduce a hybrid Frequentist-Bayesian hypothesis testing procedure that combines a Bayes factor and a score-type statistic to detect nonlinear and nonparametric effects of functional predictors on scalar responses. This approach avoids restrictive parametric assumptions and explicit likelihood estimation, offering a practical and flexible tool for biomedical group discrimination. For the second method, we develop a kernel-machine-based quantile regression test to identify associations between high-dimensional, correlated functional predictors and heavy-tailed or skewed responses. This robust approach accommodates complex interactions and successfully identifies autism spectrum disorder (ASD)-related brain networks supported by neuroscience evidence. For the third method, we propose a unified joint modeling framework that simultaneously selects functional predictors embedded in a time-varying functional graph and estimates dynamic network structures without prior information. The model combines Bayesian hierarchical modeling by developing a computationally efficient marginal expected integrated penalized likelihood maximization (ME-IPLM) algorithm, enhancing variable and graph selection accuracy, interpretability, and prediction performance. Together, these three projects provide scalable, interpretable, and theoretically supported frameworks for analyzing high-dimensional functional data, advancing group discrimination, association detection, and dynamic network interpretation across diverse scientific domains. 

Dr. Mengkun Chen will be joining Fred Hutchinson Cancer Center, as a Postdoctoral Researcher Fellow - Biostatistics.