A tool for quantitative risk analysis of Android applications based on machine learning techniques

Practical, hands-on risk modeling, risk assessment and verifications of risk models across major risk classes and understanding risk regulation as well. Implementing risk models in Python, R and Excel.

This project studies the effects of the shape parameter estimator uncertainty at different threshold levels on the value-at-risk confidence interval for quantitative risk management (QRM) using the Generalized Pareto Distribution (GPD) from the Extreme Value Theory (EVT) approach.

Replicating Hull & White (2017) paper on minimum-variance delta hedging

Geographical Risk Insurance Tool (GRIT) prototype built in 24 hours for MITxOpenAI Hack-Nation 2025: 3rd (VC track) out of >2800 globally + 1 of 15 selected to receive mentorship from Stanford, Harvard, and Microsoft to scale idea (ongoing).

Homeworks and final project of Quantitative Risk Management. Please Luigi don't ever shave your mustache, keep resembling the actual Luigi.

Python implementation of the rearrangement algorithm to find bounds of functions of dependent risks, e.g., best- and worst-case value-at-risk (VaR).

A Python app for quantitative cyber risk analysis using Monte Carlo simulation. RiskQuant models uncertainty in loss frequency and impact to estimate annualized loss exposure, visualize risk distributions, and map scenarios to NIST/ISO/PCI controls for actionable GRC insights.