L · Longitudinal

Longitudinal & Clinical Trial Statistics

A central research program develops nonparametric and robust procedures for multivariate longitudinal outcomes. This includes the Longitudinal Rank-Sum Test (LRST) framework and related methods for multiple endpoints, multiple treatment arms, incomplete outcomes, and covariate-adjusted analysis.

Current directions include:

• global rank-based tests for multivariate longitudinal treatment effects;
• multi-arm and multi-endpoint clinical trials;
• power and sample-size calculation;
• missing-data methods for incomplete longitudinal trials;
• covariate adjustment using regression and machine-learning residualization;
• observational-study extensions using weighting and doubly robust ideas;
• goodness-of-fit procedures for longitudinal covariance structures.

I · Imaging

Imaging & Neurodegenerative Disease

I collaborate on statistical and computational methods for Alzheimer’s disease, Parkinson’s disease, CT imaging, digital twins, and virtual clinical trials. These projects use survival analysis, longitudinal biomarkers, imaging-derived distributions, graph-based features, and multimodal information.

Major themes include:

• survival prediction of cognitive decline using longitudinal biomarkers;
• imaging-based causal analysis and Mendelian randomization;
• MRI/CT feature extraction and distribution-valued predictors;
• disease staging and progression in Parkinson’s disease;
• virtual patient populations and digital-twin methodology;
• imaging anomaly detection and computational validation.

N · Nonparametric

Nonparametric & Robust Inference

A recurring theme throughout my work is inference that remains reliable when standard parametric assumptions are strained. I develop rank-based procedures, U-statistics, global tests, resampling methods, and distribution-free or semiparametric approaches for structured data.

This program connects longitudinal testing, nonlinear time series, socioeconomic information fusion, and high-dimensional structured problems. The common goal is to construct inferential procedures with transparent targets and robust operating characteristics.

D · Data Science

Data Science, Causal Learning & AI

My causal-methodology work addresses settings in which candidate causes, exposures, biomarkers, or outcomes are high-dimensional and structurally related. The program combines causal inference, graphical models, instrumental variables, Mendelian randomization, and graph regularization with modern data-science tools.

Current directions include:

• Penalized FCI for sparse causal structure learning;
• network-aware instrumental-variable regression;
• graph-fused regularization for causal node discovery;
• Mendelian randomization for imaging and biomarker systems;
• latent/network-aware causal discovery;
• graph neural networks and representation learning;
• AI-generated misinformation, cross-prompt generalization, and adversarial evaluation;
• spatial/environmental and population data science.

T · Time Series

Nonlinear Time Series & Higher-Order Dependence

My time-series research studies dependence that cannot be fully characterized by autocovariances or second-order spectra. I work on polyspectra, polyspectral means, higher-order frequency-domain analysis, nonlinear prediction, and resampling.

This includes:

• polyspectral mean estimation for general nonlinear processes;
• polyspectral factorization;
• quadratic prediction using auto-cumulants;
• subsampling and convolved-bootstrap inference;
• higher-order dependence measures for applications such as financial and public-health time series.