Tehran, Tehran Province, Iran
I’m currently studying Actuarial Science, with a strong interest in quantitative risk, probability, and uncertainty modeling. My academic background includes engineering and applied mathematics, which helped me build a solid foundation in mathematics, statistics, and analytical thinking. I’m particularly interested in how uncertainty can be modeled in financial systems using probability, stochastic processes, and simulation-based methods. Right now, I’m focusing on Python and quantitative finance through small projects in risk modeling, portfolio analysis, and data-driven financial problems. My goal is to develop myself in quantitative risk and financial modeling, where mathematics and uncertainty play a central role in decision making.
Prepared and presented research on Extreme Value Theory (EVT) and catastrophic risk in financial and insurance systems. Focused on modeling rare events and understanding their importance in risk management.
Worked on a loss modeling project using R for insurance applications. Focused on modeling claim frequency and severity and understanding how losses behave in insurance portfolios. Used statistical methods to estimate expected losses and analyze tail risk.
Built an automated portfolio analytics and risk reporting system using Python. • Calculated returns, volatility, Sharpe ratio, fear and greed index, news sentimental and maximum drawdown for selected assets. • Generated performance charts and automated email reports.(equity curve) • Implemented basic machine learning models for short-term market direction estimation.
Traded Forex and US indices, including NAS100 (Nasdaq) and US30 (Dow Jones), under a funded account. Applied technical and fundamental analysis to identify high-probability trading opportunities. Followed strict risk management rules and firm trading objectives. Managed positions while complying with daily and maximum drawdown limits. Maintained disciplined trade execution and performance tracking
Worked on an academic ERM project where a sample company was defined and different financial risks were modeled. Used Monte Carlo simulation and VaR methods (historical, parametric, and simulation-based) to estimate potential losses under different market conditions. Also tested scenarios like oil price shocks and interest rate changes to see their impact on overall risk.