Predicting the temporal and geographical occurrence of conflicts in Myanmar with two paradigms of spatiotemporal networks.
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Updated
Dec 5, 2024 - Jupyter Notebook
Predicting the temporal and geographical occurrence of conflicts in Myanmar with two paradigms of spatiotemporal networks.
Quantitative geopolitical risk dashboard tracking Iran-Israel conflict escalation via market signals, GDELT news analytics, and probabilistic portfolio regime guidance.
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.
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
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.
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.
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