Research Statement
This interdisciplinary approach has led to significant discoveries, including the isolation of polonium and radium, contributing to the foundation of modern nuclear physics and radiochemistry.
Research Areas
Causal Inference
Focuses on applying Propensity Score Matching and regression techniques to evaluate treatment effects in observational studies, particularly in health data.
Public Health
Applies statistical methodologies to study mental health, substance use, and health equity, using both quantitative and qualitative data.
Statistical Programming
Development and deployment of statistical models using R, Python, and SAS, with a focus on machine learning, data mining, and simulation techniques.
Current Projects
Minimizing Selection Bias through Propensity Score Matching
Developing and applying Propensity Score Matching techniques on NSDUH-2019 data to study the relationship between health insurance status and substance use disorder treatment using logistic regression and Poisson GLMs.
Evaluation of TRIO Programs
Conducting mixed-methods analysis and thematic coding using Dedoose to evaluate how TRIO supports career development for low-income students; presenting findings to institutional stakeholders.
Stress Regulation through an Intervention-Based Approach
Analyzed the impact of mindfulness and self-compassion interventions on doctoral students; performed exploratory data analysis and documented results leading to peer-reviewed publication and presentation.
System Design with Database Integration
Developed a restaurant reservation website using HTML, PHP, and MySQL; designed an ER model and implemented data insertion and updates through the web interface.
Multivariate Analysis of Wine Recognition Data
Developed a predictive model using discriminant analysis on wine data using SAS; conducted exploratory analysis using MULTINORM and tested classification accuracy.
Interactive learnR Tutorial
Created an interactive R Markdown tutorial on tidyr with embedded examples and quizzes to facilitate user learning and hands-on practice.
Machine Learning Assessment of Clustering Techniques
Applied K-means and Hierarchical Clustering on customer travel review data; implemented feature selection using random forests to handle high-dimensional data.
Non-parametric Analysis of PTSD Symptoms
Performed exploratory data analysis and non-parametric statistical tests on Marine Study data to assess the relationship between trauma exposure and PTSD symptoms.