About Me

I am a Ph.D. student in Resource Economics at the University of Massachusetts Amherst.

My research mainly lies at the intersection of industrial organization and applied microeconomics, with a focus on how market structure and public policy shape firm behavior.

On the academic job market (2025–26), I combine rigorous economic modeling with applied data analysis, while also bringing substantial teaching experience as both a sole instructor and teaching assistant across a range of economics and finance courses.

In addition to economics, I hold a Master of Science in Computer Science (Georgia Institute of Technology, 2024), which strengthens my expertise in data science, machine learning, and computational methods. This interdisciplinary training allows me to integrate advanced quantitative techniques into empirical research in industrial organization and sustainable finance.

Research

Job Market Paper

“Quality and Spatial Competition: Evidence from U.S. Restaurants”

• Extends the Hotelling spatial competition framework by introducing consumer heterogeneity and restaurant quality differentiation.
• Develops a model with four restaurants, accounting for both horizontal and vertical differentiation.
• Empirically, I analyze the U.S. restaurant industry using data from Yelp, TripAdvisor, and population density measures, showing that higher-quality restaurants cluster more in competitive markets, while lower-quality restaurants occupy less dense areas.
• Results provide new insights into how consumer preferences and quality affect spatial distribution in imperfectly competitive industries.

📄 Job market paper available upon request.

Teaching

As sole instructor, I have taught:

• Res-Econ 323: Financial Analysis for Consumers and Firms (Fall 2024, Spring 2025)
• Res-Econ 213: Intermediate Statistics for Business and Economics (Summer 2023)

My teaching evaluations demonstrate strong student engagement and effectiveness: for example, in Spring 2025, 91% of students rated my teaching as “almost always” or “usually effective” (Q10 mean = 4.5/5.0, above department and campus averages).

I have also served as a teaching assistant for Introductory Econometrics, Industrial Organization, Managerial Economics, Public Policy in Private Markets, Price Theory, and Statistics for Social Sciences.

In recognition of my contributions, I received the Vijay Bhagavan Teaching Assistant of Distinction Award (2022), awarded by the Department of Resource Economics.

Details are available on my Teaching page.

Skills

• Econometrics and Quantitative Methods: Panel data models, causal inference, treatment effects (DiD, IV, matching), time series, spatial econometrics.
• Machine Learning and Data Science: Supervised and unsupervised learning, clustering, natural language processing, text mining, predictive modeling.
• Programming and Computational Tools: R, Python, MATLAB, SQL, Stata, Git/GitHub, LaTeX.