Welcome! I am a doctoral candidate in economics at The University of Arizona currently on the 2026-2027 job market. I specialize in industrial organization and applied microeconomics, drawing on structural modeling, causal inference, and machine learning to study how firms and consumers behave in imperfectly competitive markets.
Ph.D., Economics (Expected May 2027)
The University of Arizona
M.S., Data Intelligence and Applied Economics
University of Nevada - Las Vegas
B.A., Economics
University of Nevada - Las Vegas
My research answers questions in health economics, artificial intelligence, and antitrust and competition policy. My job market paper Hooked on Flavor: Addiction, Present Bias, and the Consequences of E-Cigarette Flavor Policy examines how flavored e-cigarette regulations affect addiction and consumer behavior through a dynamic structural model. I also use economic theory and reinforcement learning to study the competitive effects of pricing algorithms and recommendation systems, an area of growing antitrust enforcement interest. My paper Algorithmic Pricing, Recommendation Systems, and Competition is currently under a Revise and Resubmit at the International Journal of Industrial Organization, and multiple of my papers in this area have each received a best paper award at The University of Arizona.
Beyond research, I have gained hands-on experience applying my training in industrial organization and applied microeconomics to real-world economic issues. As an Associate Extern at Analysis Group, I analyzed financial and subscriber data to estimate damages and inform strategic recommendations in a high-profile carriage-fee dispute. As a research assistant for the Arizona Residential Utility Consumer Office, I supported expert witness testimony by conducting data-driven impact assessments of proposed utility rate increases. I have also brought this training into the classroom as the sole instructor of record for an undergraduate econometrics course, teaching 20+ students applied econometrics and machine learning in R, earning a 94% mean course evaluation (Teaching Evaluations).
I am well-versed in structural econometrics, both static and dynamic, along with quasi-experimental methods such as difference-in-differences, regression discontinuity design, and synthetic control, to help answer empirical questions. I implement these methods using R, MATLAB, C++, Julia, and Python, along with Git/GitHub for version control, Docker for reproducible environments, and Slurm for high-performance computing.
When I'm not doing economics, you can usually find me traveling with my wife, hanging out with our cat, training calisthenics in the gym, running in the Tucson desert, or talking about the Chicago Bears 🐻⬇️.