معرفی
Lena Sheveleva is a Senior Lecturer at Cardiff Business School, Cardiff University, where she has been teaching since completing her PhD at Penn State University in 2014. Her academic position places her at the intersection of economic theory and practical business applications, with a strong focus on international trade dynamics and data-driven analysis.
Education:
- PhD in Economics, Pennsylvania State University
- BA in Mathematics, American University in Bulgaria
- BA in Economics, American University in Bulgaria
Dr. Sheveleva's research program bridges traditional economic frameworks with modern data science methodologies. Her work examines how multi-product firms operate in global markets, how governmental trade policies affect business decisions, and how economic principles can inform data science applications in business contexts. She applies econometrics and machine learning to large-scale datasets for predictive modeling, causal inference, and business analytics, with particular emphasis on workforce productivity and resource allocation problems.
Her recent publications demonstrate a clear trajectory toward integrating economic theory with practical business analytics, focusing on worker productivity, multi-product export strategies, and the impact of trade policies on firm behavior. This research combines rigorous theoretical frameworks with empirical analysis of real-world business challenges.
Scientific Recognition:
- Journal reviewer for American Economic Journal: Microeconomics and Economics Letters
- Recipient of multiple research grants including ESRC Business Boost and Seed Fund projects
- Cardiff-Xiamen Mobility Grant recipient (suspended due to Coronavirus)
Dr. Sheveleva actively supervises postgraduate students and serves as a valuable resource for research projects examining the intersection of economics and business analytics. Her current work with co-author Jiangyang Wang on non-tariff measures demonstrates her ongoing commitment to understanding how trade policies affect importing firms. She also explores how economic principles can inform data science applications in fraud detection, worker productivity optimization, and pricing strategy development.
