PhD Student, Political Science
University of Houston
Public Policy · Methodology · AI Governance
I am a second-year PhD student in Political Science at the University of Houston, with concentrations in Public Policy and Methodology. My research examines how governments adopt, procure, and regulate emerging technologies—particularly artificial intelligence—across U.S. state bureaucracies.
My work draws on policy diffusion theory, causal inference, and computational methods to understand how administrative agencies navigate technological uncertainty. I am especially interested in the intersection of executive politics and bureaucratic implementation, exploring how gubernatorial signals shape downstream procurement behavior.
Prior to my doctoral studies, I earned a Master of Public Policy from Arizona State University and a Bachelor of Science in Applied Quantitative Sciences, also from ASU. My methodological training spans causal inference, machine learning, multilevel modeling, and applied econometrics.
Working Papers
This paper examines how AI procurement instruments spread through state bureaucracies under uncertainty, focusing on imitation and competition as mechanisms. Using text similarity in RFP language and a technology-weighted spatial-ideological rival intensity measure, I analyze how gubernatorial agenda salience shapes instrument diffusion.
Using an interrupted time series design, this paper investigates how public bureaucracies responded to the release of ChatGPT 3.5—a major technological shock that fundamentally altered the AI landscape and forced rapid administrative adaptation.
Using a Regression Kink Design, this paper investigates whether NYC's 5-candidate ranking cap affects ballot completion. Drawing on cast vote record data from the 2021, 2023, and 2025 NYC Council elections, I test whether the relationship between slate size and ballot completion changes once the number of candidates exceeds what voters can rank. Multilevel models with district-level random effects identify a structural break at the cap threshold, isolating the causal effect of the ranking constraint on voter behavior.
This paper examines how gubernatorial executive orders shape bureaucratic AI procurement. I classify state AI executive orders as either acceleration-oriented (encouraging rapid adoption) or constraint-oriented (imposing governance requirements), then estimate their downstream effects on procurement activity using the Callaway and Sant'Anna (2021) staggered difference-in-differences framework. The analysis tests whether executive intervention serves primarily as an enabling political signal or as a governance constraint that slows procurement by design.
Public Policy and Methodology
University of Houston
Arizona State University
Applied Quantitative Sciences
Arizona State University