Ethnographic Edge has been funded by the LSE-Oxford Commission on State Fragility, Growth, and Development, Mercy College’s Faculty Development Grants, and Pukyong National University’s National Research Foundation Grants. Data analytics and web intelligence services are provided by internet technology company Recorded Future.
Eduardo Zachary Albrecht, the project’s research director, obtained his PhD in Social Anthropology from the School of Oriental and African Studies at the University of London. He is currently Program Head of the International Relations and Diplomacy Program at Mercy College in New York, and was previously Associate Professor of Anthropology and International Studies at Pukyong National University in South Korea. He has served as Visiting Fellow at the European Institute for Asian Studies in Brussels and the International Peace Institute in New York.
Chris Mahony, the project’s legal and political economy scholar, is Research Fellow at the Centre for International Law Research and Policy. He is also Political Economy Adviser at the Independent Evaluation Group at the World Bank, where he was formerly Criminal Justice and Citizen Security Specialist from 2014 to 2015. He is Consultant Strategic Policy Adviser to the United Nations Development Program since 2015. He holds Bachelor of Commerce (B.Com.) and of Laws (LL.B.) degrees from the University of Otago, and a Master’s in African Studies (M.Sc.) and a D.Phil. in Politics from the University of Oxford. He was admitted to the bar of the High Court of New Zealand in 2006 where he appeared for the Crown in criminal and refugee matters. His research focuses on Violent Conflict, International Law, and Transitional Justice.
Melih Kandemir, the project’s data scientist, is Research Scientist at the Bosch Center for AI in Stuttgart, Germany, and was previously Assistant Professor at Ozyegin University in Istanbul. Before that he held a postdoc position at the University of Heidelberg and obtained his PhD from Aalto University in Finland. His research activity focuses on Novel Bayesian Inference Techniques for Deep Learning, and their applications to Computer Vision, Time Series Analysis, and Medical Image Analysis problems.