Navid Mehrdad
published prior to 2020 as Navid Hassanpour
Political Science
Navid Mehrdad is a political scientist working on democratization and autocratization in historical perspective, with archival and quantitative methods. He holds a Ph.D. in political science from Yale University and an earlier Ph.D. in electrical engineering from Stanford University, and has held research and teaching positions at Princeton (Niehaus Fellow in Regional Political Economy), Columbia (Lecturer; later Visiting Scholar at the Harriman Institute), the Higher School of Economics (Associate Professor of Political Science), the European University Institute, and Yale (Henry Hart Rice Faculty Fellow). His first book, Leading from the Periphery and Network Collective Action (Cambridge University Press, 2017), received the American Political Science Association Political Networks Section’s best book award. His present book, Political Origins of Dictatorship and Democracy: Elite Entrepreneurs in the First Age of Modern Mass Politics, draws on fieldwork and archival collection in Ankara and Istanbul, Beijing, Cairo, Moscow and St. Petersburg, and Tehran, and on sources in eight languages.
Artificial Intelligence
Navid Mehrdad is an engineer and applied researcher working on agentic AI and large-scale Machine Learning systems, with a background in information theory and production Artificial Intelligence. He holds a Ph.D. in electrical engineering from Stanford University and a later Ph.D. in political science from Yale University, and has held engineering and research positions at Qualcomm (Corporate R&D wireless systems; five granted U.S. patents), Allston Trading, Shell (deep reinforcement learning), Walmart Global Tech (Principal Staff Machine Learning Engineer, Search & Relevance, where his retrieval and ranking systems contributed over $150M in annual business impact), and Salesforce Product AI (Principal Member of Technical Staff, agentic runtimes and evaluation infrastructure). His work spans dense retrieval, session-aware agentic architectures, agentic eval frameworks, and open-source research on coordination in multi-agent AI systems, publications in network information theory and ML/AI. His current work centers on agentic orchestration in production systems.