About
I operate at the intersection of causal machine learning, predictive modeling, and applied econometrics; building production-grade systems that turn complex data into actionable business and policy insights. As a Data Scientist (Economist Apprentice) at Amazon, I develop surrogate index models, causal discovery frameworks, and LLM-based forecasting pipelines on AWS. As a Research Technician at UConn, I apply causal inference to study how generative AI reshapes educational outcomes and human capital formation. My work spans Python, SQL, Gen AI, Agentic AI, and AWS, with a focus on statistical rigor at scale.
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 Gen AI systems, causal inference, machine learning, and 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 Gen 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
Data Scientist (Economist Apprentice)
Amazon - PXT AMX Applied Science
Surrogate index models, Causal Discovery, Double Machine Learning, LLM (chatbot) evaluation, scalable data pipelines on AWS, science models to enhance Amazonian productivity and effectiveness.
Research Technician / Graduate Instructor
University of Connecticut - Department of Economics
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