About me
I am an instructor in Quantitative Finance, Programming, and Applied Machine Learning/AI for finance, marketing, and business.
My academic work is driven by a strong interest in using mathematics, data science, and technology to address real financial and economic problems. In my courses my focus is on turning complex quantitative concepts into clear, structured, and project‑based learning experiences that prepare students for real industry challenges.
At the Bachelor’s and Master’s levels, I have taught:
Financial Modeling
Python and Data Analysis for Finance and Marketing
Machine Learning and AI for Finance and Business
Differential Equations, Time Series Analysis, and Stochastic Processes
My teaching philosophy goes beyond traditional instruction. I focus on bridging academia and industry through applied, data‑driven projects. This approach is now evolving into a broader Talent Foundry initiative aimed at aligning advanced quantitative education with the realities of today’s job market.
Alongside my academic work, I have worked in industry roles at Natixis and Nexus Horizon, contributing to projects in security management, financial modeling, and quantitative research. Across both academia and industry, I emphasize rigorous modeling, real financial datasets, and practical problem‑solving.
**Research Focus ** My doctoral research in Quantitative Finance examined market antifragility using machine learning and mathematical modeling techniques.
Current research directions include:
Rolling Fractal Dimension as an Early-Warning Signal for Market Regime Shifts
A Quantitative Antifragility Index for Financial Markets