Experienced Data Scientist specializing in political and social science research, with a strong background in event dataset creation and computational data analysis.

Previously, he managed data collection projects at Georgetown and Princeton University, designing and curating large-scale protest event datasets, leveraging advanced data science techniques to streamline academic research.

His expertise includes machine learning, data engineering, and natural language processing, applied to social movement analysis and public policy research.

To enhance efficiency in event data collection, he developed an automation app that uses large language models and Python scripts to extract, categorize, and structure protest event data from Arabic news sources. This tool significantly improves the speed and accuracy of dataset creation for large-scale research projects.

With a passion for innovation, he continuously explore ways to bridge data science and social research, driving insights that inform policy and academic discourse.