Proceedings of the 2019 International Conference of The Computational Social Science Society of the Americas

Dr. Zining Yang is Senior Manager at Southern California Edison. She also works as Clinical Professor at Claremont Graduate University and Associate Director at the TransResearch Consortium. She sits on the Board of the Computational Social Science Society of the Americas (CSSSA), and serves as Scientific Advisory Board Member for Human Factors and Simulations. Dr. Yang received her Ph.D. in Computational and Applied Mathematics and Political Economy from Claremont Graduate University in 2015. Her research interests include Data Analytics, Machine Learning, Modeling and Simulation, Complex Adaptive Systems, Agent-Based Models, and Network Analysis. Dr. Yang has been published numerous times in the fields of Computer Science, Economics, Public Policy, and Political Science. She has been identified as an outstanding researcher by the government, worked on a National Science Foundation-sponsored project, and won multiple awards from various organizations, including the Ministryof Education of the People’s Republic of China; International Social Computing, Behavioral Modeling and Prediction; and the International Institute of Informatics and Systemics.

Dr. Elizabeth von Briesen is an Assistant Professor of Computer Science at Elon University, and is a member of the board of the Computational Social Science Society of the Americas. She received her Ph.D. in Computing & Informatics from the University of North Carolina at Charlotte in 2020. Her research interests are focused on the study of complex adaptive systems using computational techniques, particularly with respect to social systems experiencing identity-based conflict. She primarily works with agent-based models, and performs data mining and sentiment analysis to inform those simulations. Finally, in her current position, Dr. von Briesen strives to contribute toward an evolving undergraduate computer science experience through research, service, and high-quality, innovative teaching.