My Story
As of September 2023, I serve as a Senior Economist in the Economics Reimbursable Surveys Division (ERD) at the U.S. Census Bureau. In this role, I've been entrusted with leading strategic efforts to reduce respondent burden and enhance data quality. My focus is on identifying content where traditional data collection techniques can be replaced or augmented with advanced data modeling techniques using alternative data sources such as administrative records, commercial data, and direct company feeds. Additionally, I lead the creation and maintenance of a dual contact information directory for businesses and their owners, and households and individuals to ensure comprehensive, accurate, and reliable contact information for outreach for both economic and demographic surveys and censuses.
Before transitioning into this role, I served as a Senior Economist in the Interdisciplinary Research Area, Center for Economic Studies (CES), also at the U.S. Census Bureau. This position, starting in May 2023, represented both growth and a new set of challenges from my previous work in the Labor Force Statistics Branch (LFSB), the Social, Economic, and Housing Statistics Division (SEHSD). At CES, I explored the use of statistical methodologies like Inverse Probability Weighting, Entropy Balancing, and Empirical Likelihood Weighting to mitigate bias arising from nonrandom assignment of Protected Identification Keys (PIKs). PIKs are anonymized, unique identifiers that the Census Bureau probabilistically assigns to individual records to facilitate linkage with other datasets. Minimizing bias in PIK assignment is critical for data equity, as it impacts what can be known and answered about an individual.
My journey to these roles has been filled with curiosity, exploration, and mentorship. It was through the encouragement of one of my undergraduate professors that I began to explore economics, building on my dual major in political science and mathematics. I graduated Magna Cum Laude from St. Mary's College of Maryland in 2014 as a triple major in political science, mathematics, and economics. Pursuing my passion for understanding human interactions and systems, I earned my doctorate in economics from the University of Illinois at Chicago (UIC) in 2020.
Alongside these applied projects, I continue to pursue academic research that spans a wide range of applied microeconomics topics. My research interests include gender and immigration economics as well as financial economics, particularly focusing on the engagement of unbanked households with the tax system. This interest stemmed from my work on developing imputation specifications for the Unbanked and Underbanked Supplement of the Current Population Survey while in LFSB. Another significant area of my academic research, inspired by applied projects, examines the impact of randomized monetary incentives on item non-response rates and measurement errors, reflecting my growing interest in survey methodology research.
In addition to my academic and applied work, I lead the ML/AI Affinity Group at the U.S. Census Bureau, fostering a culture of innovation and collaborative learning. My blog posts on Medium aim to demystify machine learning and artificial intelligence, helping people overcome the intimidation often associated with these concepts. I strive to make these transformative technologies accessible to a broader audience, ensuring everyone can contribute to the evolving discourse.
Outside of work, I practice Kung Fu and Tai Chi, and I single-handedly keep Barnes & Noble in business with my love of books. This balance of technical expertise, leadership in artificial intelligence and machine learning, and personal hobbies makes me a versatile and engaging contributor to any team or project.