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 deeptech to address real financial and economic problems. Coming from mathematical background; master’s degrees in pure math as well as applied math (optimization); I shifted focus to mathematical finance in my doctorate thesis in which inspired by the concept of antifragility (Introduced by Nassim Taleb) I dedicated my thesis research to develop a methodology to model antifragility in financial markets. Methodologically, I used Multifractal Analysis, Recurrence Analysis and Hurst Exponent to detect and model the antifragility behaviour in financial markets through the power of machine learning models.

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.

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