Amandeep Singh

Amandeep Singh is a first-year Ph.D. student in Probability and Statistics at George Mason University. His academic journey spans three countries: a B.Sc. in Mathematics from the University of Delhi (India), an M.Sc. in Stochastics and Data Science from the University of Turin (Italy), and now doctoral studies in the United States. This international background has shaped both his mathematical depth and his ability to approach problems from multiple perspectives.

Before beginning his Ph.D., Amandeep worked in actuarial science and quantitative finance at MetLife, KPMG, and most recently Hildene Capital Management. His professional projects ranged from GGY-AXIS modeling of fixed indexed and multi-year guaranteed annuities, to stochastic simulations and risk analysis under Solvency II, to developing Python-based Bayesian and machine learning tools for financial modeling. These roles gave him a unique vantage point at the intersection of industry practice and academic research.

At George Mason, his research interests include stochastic processes, Bayesian non-parametrics, statistical machine learning, and the design of intelligent algorithms for high-dimensional data. His master’s thesis—on dependent Dirichlet processes for predicting macroeconomic crises—was recently published as a chapter in Springer’s New Trends in Bayesian Statistics (2025). Alongside research, he serves as a Graduate Teaching Assistant, supporting undergraduate statistics courses and problem-solving sessions.

Amandeep is passionate about bridging theory and practice. His long-term vision is to contribute to the development of statistical methods that are both theoretically rigorous and practically impactful in finance, economics, and data science.