STATISTICAL MODELING
Regression, GLMs, mixed models, missing data methods, and uncertainty quantification for applied research problems.
MACHINE LEARNING & PREDICTION
Predictive modeling, recommender systems, random forests, neural networks, transfer learning, and model explainability.
CAUSAL INFERENCE & RESEARCH DESIGN
Potential outcomes, propensity scores, and study design for observational and experimental research.
PROGRAMMING & DATA WORKFLOWS
Python, R, and SQL for data cleaning, visualization, modeling, simulation, and reproducible analysis.
About Me
I am a third-year PhD student in Statistics and Data Science at Northwestern University. I work under the advisement of Professor Elizabeth Tipton at the Institute for Policy Research.
My current research uses statistical and social science methods to study how large language models represent human responses, with a specific focus on whether these models preserve meaningful variation across individuals and groups. My other applied projects span machine learning, causal inference, recommender systems, missing data, and statistical modeling for real-world datasets.When I'm not working with data, you can find me traveling or running. I'm currently training for the 2026 Chicago Marathon!