My research interest lies at the interface of applied probability, applied statistics and machine learning. My research is driven by collaborations with many extremely brilliant and outstanding probabilists/statisticians/biostatisticians/computer scientists.

Network Analysis

I am particularly interested in the following topics:

• Neuroimaging and social science applications
• Network data analysis (e.g., community detection and link prediction)
• Random network models (e.g., degree distribution and topological measures)

Longitudinal Data Analysis

I am committed to developing novel statistical methods for assessing dementia risk in neurodegenerative diseases and related disorders. Specifically, my work addresses challenges arising from missing data and measurement error, with a particular focus on applications to Alzheimer’s disease, Parkinson’s disease, and other neurodegenerative disorders.

Causal Inference

I establish analytical frameworks to enhance understanding of how molecular signatures (e.g., proteomic profiles) influence cognitive decline in Alzheimer’s disease and aging populations. These frameworks integrate pathway-level analyses and are specifically designed to address statistical challenges associated with high dimensionality.

Collaborative Research

I have engaged in a variety of research projects, in collaboration with the faculty members, trainees and medical professionals from

Vanderbilt University Medical Center

• Vanderbilt Memory & Alzheimer’s Center (VMAC)
• Medical-Image Analysis and Statistical Interpretation (MASI) Lab

Perelman School of Medicine at the University of Pennsylvania

• Center for Neurodegenerative Disease Research (CNDR)
• Penn Frontotemporal Degeneration Center (Penn FTD Center)
• Penn Memory Center (PMC)