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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.
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
Final class project for Modeling II: Machine Learning at USF; a graduate course in the Data Science program.