About
I am a final year Ph.D. candidate in the Department of
Statistics
at the University of Washington advised by Elena Erosheva. I am broadly interested in causal inference, causal discovery, and fairness. In my PhD, I develop methodologies for causal fairness and causal discovery, focusing on applications in the social sciences.
During my PhD, I completed two Applied Scientist internships at Amazon. In Summer 2024, I developed novel Bayesian methods to reduce noise in feature-impact estimates for A/B tests within Weblab, Amazon's internal experimentation platform that supports over 100K large-scale experiments each year. In Summer 2025, I developed instrumental-variable survival models to study causal factors driving workforce attrition as part of the Execution Planning Science (EPS) team, which develops science solutions for labor planning and operations optimization in Last Mile delivery.
Before coming to UW, I worked as a data scientist at Marinus Analytics. Here, I analyzed time series data from unstructured child welfare case records to identify factors associated with child removals and reunifications in the child welfare system. Additionally, I implemented spam filters and underage person detection algorithms in TraffickJam, an application that supports law enforcement by analyzing human trafficking advertisement data to identify victims and traffickers.
I received my Bachelor of Science degree in Statistics and Machine Learning from Carnegie Mellon University where I worked with Peter Freeman and Alexandra Chouldechova.
You can find my CV here.
General Interests
1. Causal Inference
2. Causal Discovery
3. Fairness