Stanford University
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Jun Li
Affiliate, SGS Stanford Global Studies
Visiting Scholar, Stanford Global StudiesBioJun Li (pen name: Sipan Li) is a Visiting Scholar at the Stanford Global Studies Division. Her research focuses on the relationship between feminist movements, media, and the state, and examines media practices and digital activism in authoritarian contexts. She earned her Ph.D. in Political Sociology from the University of Macau, and previously served as Associate Professor at the School of Journalism and Communication, Shantou University, as well as a Scholar-in-Residence with the Beatrice Bain Research Group (Gender Studies) at the University of California, Berkeley.
Li is an award-winning journalist and public intellectual, and one of the earliest investigative reporters in China to focus on sexual harassment and sexual assault in the workplace and in educational institutions. She founded the Women Awakening Network, one of the most influential feminist advocacy NGOs in China. Through her work and organizational leadership, she has had a broad impact on government policy, academia, and public awareness in efforts to combat gender-based violence. Her coordinated policy advocacy contributed to the Ministry of Education issuing its first formal anti-sexual harassment policy.
From 2014 to 2015, Li served as the Media and Public Advocacy Lead for the civil coalition advocating for China’s anti-domestic violence legislation. She has also served as an expert reviewer for “Love Ideas – Women’s Project Guangdong,” an innovative initiative jointly launched by the Guangdong Provincial Government and the Li Ka Shing Foundation to improve women’s well-being, and as a consultant expert for the Guangdong Women’s Federation. In addition, Li has collaborated with UNESCO, the Caixin Media Foundation, the Heinrich-Böll-Stiftung (Heinrich Böll Foundation), and Oxfam Hong Kong on journalism and communication training programs. -
Zetian Li
Masters Student in Computational and Mathematical Engineering, admitted Autumn 2024
Current Research and Scholarly InterestsStatistical Learning, Machine Learning, Bayesian Statistics, Probability Theory
