Economist & Researcher, University of Strasbourg
I am Emmanouil Sofianos, a researcher in empirical economics with a strong interest in macroeconomic forecasting, machine learning, and the intersection of economic behavior and policy. I currently hold a research fellowship at BETA (Bureau d'économie théorique et appliquée), University of Strasbourg, where my work focuses on forecasting public debt in the euro area using political social and institutional variables alongside machine learning techniques.
I completed my PhD at the Department of Economics, Democritus University of Thrace. My dissertation, titled "Study of Macroeconomic Variables of the Euro Area Using Machine Learning," explored data-driven methods for forecasting and understanding macroeconomic trends. I received a PhD scholarship from the State Scholarship Foundation (IKY), and my research has been published in journals such as the Journal of Forecasting, Applied Economics Letters, and Energies.
Alongside my PhD, I earned an MBA from the same department, graduating with distinction and receiving a scholarship for academic excellence. From 2017 to 2023, I served as a teaching assistant at the Democritus University of Thrace, contributing to courses in business analysis, financial tools, and applied economic methods. Since 2021, I have also taught at the university’s Lifelong Learning Center (KEDIVIM), across programs including Economics for Non-Experts, Machine Learning and Artificial Intelligence, and Modern Business Tools.
At the University of Strasbourg, I lecture in the Master’s program in Data Science for Economics and Business, where I recently taught Data Infrastructures Introduction to Data Science. Across my teaching roles, I aim to combine technical rigor with accessibility, drawing on more than nine years of academic experience.
I am particularly interested in building interpretable models that are both policy-relevant and grounded in empirical reality, while also informed by behavioral considerations. I’m especially motivated by interdisciplinary approaches that connect predictive analytics with economic theory and real-world decision-making.