Sasha Shapoval, Prof. Dr. | Quantitative Economist

• Projects in Applied Microeconomics focusing on Social Economics, Political Economy, and Computational Finance: By addressing heterogeneity in economic systems, I uncover their structural properties, predictability, and controllability. My analysis draws on Game Theory, General Equilibrium Theory, Big Data Analysis, Statistical Learning, and AI.
• Interdisciplinary Research Profile: Strong background in Applied Mathematics and Data Science, with expertise in modeling, computational methods, and machine learning.
• Publications: Bridging Quantitative Economics (with papers in Journal of Economics, Journal of Mathematical Economics, Journal of Public Economic Theory) with Complex Systems (Scientific Reports, Networks and Spatial Economics, Physical Review).
• Research Leadership (PI): Headed the Laboratory in Complex Systems, supported by grants and contracts from research foundations and industry partners, and served as a deputy-head of a department for research
• Program Leadership: Director-level experience in the creation and management of educational programs.
• Advanced Computational & Econometric Expertise: Proficiency in Python (NumPy, SciPy, Pandas, Scikit-learn, Matplotlib/Plotly), MatLab, and C applied to Advanced Econometrics (ML/AI), Time Series Analysis, and Forecasting.