Danny Ebanks

Danny Ebanks is a Postdoctoral Research Fellow at Harvard University's Institute for Quantitative Social Science (IQSS). He earned his PhD in Quantitative Social Sciences from the California Institute of Technology (Caltech), focusing on Political Methodology, American Politics, and Congress. His research integrates Bayesian statistics, latent variable models, and natural language processing to advance understanding of political systems and data-driven analysis. At IQSS, he develops statistical methods and explores machine learning and AI innovations to address political science challenges.

• University: Harvard University
• Education: PhD in Quantitative Social Sciences from Caltech

Danny's work spans diverse political science subfields, including electoral accountability, climate change policy, social media dynamics, and methodological critiques. His recent publications analyze topics like partisan unity, trust in scientists, and NLP applications in legislative studies, reflecting his commitment to rigorous statistical modeling and interdisciplinary approaches.

His scholarly output emphasizes methodological rigor and policy relevance, with notable work on electoral systems, climate communication, and identity-driven political action. While no specific advising, grant, or team affiliations are listed, his focus on innovative statistical techniques positions him as a key contributor to computational political science. Outside academia, he is a lifelong runner from New York.