معرفی
Kirill Golubnichiy serves as a Post Doctoral Fellow in the Department of Mathematics & Statistics at Texas Tech University, specializing in mathematical finance and computational methods for financial modeling.
Education:
- Ph.D. in Mathematics, University of Washington, Seattle (2022)
His research centers on developing mathematical tools for analyzing partial differential equations governing financial asset pricing, solving inverse problems with physics applications, and creating machine learning algorithms for forecasting financial markets. This interdisciplinary work bridges theoretical mathematics, computational finance, and artificial intelligence to address complex problems in quantitative finance.
Analysis of his 15 most recent publications (2021-2025) reveals a dominant focus on solving the Black-Scholes equation for option pricing through innovative combinations of numerical methods and machine learning. His research shows increasing integration of deep learning techniques with traditional mathematical finance models, particularly in handling ill-posed problems and volatility forecasting. Secondary research streams include theoretical physics applications involving Einstein equations and academic textbook development in economic analysis.
He participates in Texas Tech's Mathematical Finance Program, collaborating with faculty leads Dr. Zari Rachev and Dr. Brent Lindquist on quantitative finance research and graduate training initiatives.
