Yu Wang obtained his Bachelor's degree in English Language and Literature from Sichuan University in June 2011. In April 2014, he received a Master's degree in Economics from the Toulouse School of Economics in France. In August 2018, he earned his PhD in Political Science and Computer Science from the University of Rochester in the United States. In November 2023, he was introduced as a strategic talent under the "Young Talents" program and appointed as Associate Professor,  Research Fellow, and PhD supervisor at the Institute for Advanced Studies in Social Sciences at Fudan University.

Dr. Yu Wang's research fields include computational social science, international relations, political methodology, artificial intelligence, big data applications, and machine learning. In recent years, he has published 24 academic papers as sole author, first author, or corresponding author in top international journals or conferences, including 10 papers indexed by authoritative foreign journals such as SSCI or SCI. Specifically, these include 3 papers in the top political science journal Political Analysis (impact factor 9.015), 1 paper each in the top-tier political science journals Review of International Organizations (impact factor 7.833) and International Organization (impact factor 5.754), as well as in top international relations journals like The Chinese Journal of International Politics (impact factor 3.300) and International Studies Quarterly (impact factor 2.799). Additionally, he has published 1 paper in the SCIE top-tier computer science journal IEEE Transactions on Big Data (impact factor 4.271), totaling 5 papers in this category, and 2 papers in top-tier psychology journals. Furthermore, since 2018, he has published over 10 papers in flagship conferences in the fields of machine learning, data mining, and others. Among these, he authored or co-authored 2 papers at internationally recognized top conferences like EMNLP (2019) and EMNLP Findings (2020) (ranked 2nd in computational linguistics according to Google Scholar data), which are considered equivalent to publishing in top academic journals in machine learning and related cutting-edge fields.