Meng Song

I am a Ph.D. candidate in Econometrics and Quantitative Economics (STEM) at the University of Connecticut, where I am also pursuing a Master’s degree in Data Science. My work sits at the intersection of economics, data science, and artificial intelligence, combining rigorous quantitative methods with modern computational tools to understand complex problems and generate actionable insights. I serve as a Quantitative Graduate Assistant at the UConn Center for Advancing Research, Methods, and Scholarship in Gun Injury Prevention (ARMS).

My research lies in applied microeconomics, spanning labor economics, education, public policy, digital economics, the economics of technology and AI, behavioral economics, and environmental economics, with emerging interests in AI for Science. I study how technological, institutional, and environmental changes shape economic behavior, human capital, and inequality.

My technical expertise includes causal inference, experimentation and A/B testing, statistical modeling, machine learning, natural language processing (NLP), and large language models (LLMs). I develop end-to-end data-driven solutions, from processing large-scale structured and unstructured datasets to model development, evaluation, and deployment. I apply these methods to solve real-world problems, develop scalable and efficient solutions, and support evidence-based decision-making.