I find great joy in sharing my knowledge with others. Observing the growth of students also contributes to my own growth. The process of learning is a profound source of happiness and fulfillment for me, and my aspiration is for students to experience the same sense of enrichment and satisfaction.

My academic training, including a PhD in Quantitative Psychology at the University of Notre Dame and an MA in Mathematics at Temple University, provides me with a robust foundation in mathematics, statistics, programming, and quantitative methodologies. I have expertise in structural equation modeling (SEM), longitudinal data analysis, hierarchical linear modeling (HLM), text mining, and computational statistics, particularly Bayesian methods. My research has focused on developing innovative statistical approaches to address perennial challenges in statistical modeling such as mixture structure, model selection, missing data, and outliers, as well as topic modeling and reliabilities.

I actively apply statistical models to diverse domains, including education, social and behavioral sciences, biostatistics, sports management, and engineering. Recognizing that a deep understanding of quantitative methodologies enables the exploration of critical questions within these fields, I am here to help you enter this captivating quantitative world.

Beyond academia, my interests span caring for animals (I have a lovely greyhound), gardening, traveling, and watching movies. During the pandemic, I found great enjoyment in planting trees and flowers in my backyard.