About
I operate at the intersection of causal inference and machine learning, engineering end-to-end automated models that turn complex data into actionable policy and business insights. As an Economist Apprentice at Amazon and a Research Technician at UConn, I specialize in bridging the gap between academic rigor and industrial scale, developing at pace. I leverage Python, Gen AI, SQL, and AWS, to generate solutions for high-stakes problems that require scalable scientific excellence.
My long-term research interests lie in applied econometrics and the economics of generative AI. Feel free to reach out via email or LinkedIn.
Research Narrative
My research agenda sits at the nexus of AI systems, causal inference, and infrastructure economics. At Amazon I develop surrogate models and LLM-based forecasting pipelines on AWS, while at UConn I apply quasi-experimental methods to study how generative AI tools reshape educational outcomes. A unifying thread across these projects is the question of how rapidly scaling computational infrastructure, from data centers to large language models, generates second-order effects on local economies, energy markets, and human capital formation. By combining econometric rigor with modern machine learning tooling, I aim to produce actionable evidence that informs technology policy at the institutional, regional, and federal level.
Current Positions
Economist Apprentice
Amazon — PXT AMX Applied Science
Surrogate index models, LLM (chatbot) evaluation, scalable data pipelines on AWS, science models to enhance Amazonian producitvity and effectiveness.
Research Technician / Graduate Instructor
University of Connecticut
Evaluate the impacts of policy on Higher Education learning outcomes, applied econometrics research, instructing undergradute students.
Education
MS Data Science
Central Connecticut State University
2025
Coursework:
Predictive Analytics: Estimation and Clustering, Predictive Analytics: Classification, Multivariate Statistics, Text Analytics with Information Retrieval, Text Analytics with Natural Language Processing, Advanced Estimation Methods, Introduction to Data Science
MS Quantitative Economics
University of Connecticut
2020
Coursework:
Machine Learning for Economists, Applied Econometrics I, Applied Econometrics II, Open-Source Programming with Python, Programming and Computation with R for Economists, Mathematical Economics, Operations Research, Panel Data Econometrics, Consumer Demand Analysis, Microeconomic Theory I (PhD), Advanced Mathematical Economics (PhD), Applied Econometrics I (PhD), Doctoral Dissertation Research
BA Economics
University of Connecticut
2019
Coursework:
Econometrics I, Economic Forecasting, Money and Banking, Financial Accounting, International Finance, Public Finance, Statistics I, Statistics II, Mathematical Economics, Operations Research, Economic Growth, Economic Development, Healthcare Economics, Applied Linear Algebra, Transitions to Advanced Mathematics