Speaker: Xiaoming Zhang (BIMSA)
Time: 2025-11-13 16:00-17:30
Location: 1310, SIMIS
Abstract: I will begin by briefly introducing the historical development of micro-scale governing equations in opinion dynamics, followed by a refined model constructed from empirical data and related studies. This model is then applied to analyze the patterns of opinion distributions in the U.S. Congress, using 154 years of recorded data. The talk aims to address two central questions: What drives the long-term separation and convergence of ideologies in the U.S. political system? And is there a connection between partisan polarization and major historical events? Finally, I will describe several dynamic-system problems arising from social opinion exchange processes and conduct a questionnaire survey.
About Speaker: Prof. Zhang is currently interested in developing machine learning algorithms driven by both data and existing domain knowledge, and applying them to the interpretation and quantification of various physical, biological and social phenomena. He received his bachelor’s, master’s, and doctor’s degrees from Zhejiang University, Peking University, and Massachusetts Institute of Technology, respectively. He is currently a professor at the Beijing Institute of Mathematical Sciences and Applications in the artificial intelligence and machine learning research group. He is a National Specially-Appointed Expert and a National High-Level Foreign Expert.
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