Quantitative geopolitical risk dashboard tracking Iran-Israel conflict escalation via market signals, GDELT news analytics, and probabilistic portfolio regime guidance.
Predicting the temporal and geographical occurrence of conflicts in Myanmar with two paradigms of spatiotemporal networks.
M.Sc. Thesis - Predicting Violent Conflict in Africa - Leveraging Open Geodata and Deep Learning for Spatio-Temporal Event Detection
Development of An Automated Conflict Prediction System by State Space ARIMA Methods
This project was conducted for "API 222: Machine Learning and Data Analytics", taught at the Harvard Kennedy School. We created a novel dataset and explored how machine learning can predict the onset of civil conflict.
High-performance conflict prediction engine using information theory. Measures worldview divergence via KL divergence. Rust core with WebAssembly support for browser, edge, and air-gapped deployment. 100x faster than Python.
A machine-learning pipeline that fuses real-time signals from military aviation, civic anomalies, geopolitical news sentiment, and global financial markets to output a continuously updated probability of imminent military conflict.
Final class project for Modeling II: Machine Learning at USF; a graduate course in the Data Science program.
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