Meng Song

I am Meng Song, a Ph.D. Candidate in Econometrics and Quantitative Economics (STEM) at the University of Connecticut, where I am concurrently pursuing a Master’s degree in Data Science. I serve as a Quantitative Graduate Assistant at the UConn Center for Advancing Research, Methods, and Scholarship in Gun Injury Prevention (ARMS).

My research interests lie in applied microeconomics, with a focus on labor economics, the economics of education, and public policy.

The first strand of my research examines the future of work, investigating how technological and structural changes—such as artificial intelligence and the rise of remote work—reshape labor markets, human capital formation, and economic inequality. I study topics including productivity and performance, gender inequality and worker displacement, as well as task transformation and industry-specific adaptation.

The second line of my research explores the future of education, analyzing how institutional reform and technological innovation influence students’ academic performance, skill development, and long-term outcomes. I am particularly interested in how artificial intelligence affects learning behaviors and achievement, and how education systems can adapt to promote equitable access and effective learning in a digital era.

Methodologically, I integrate applied econometrics with natural language processing (NLP) and empirical machine learning (ML) methods. I use these tools to analyze large-scale and unstructured data, aiming to enhance causal inference, reveal behavioral mechanisms, and strengthen the precision and impact of my research.