Xi Song (宋曦) is an Associate Professor of Sociology (2019—present) and a faculty member of the Graduate Group in Demography at University of Pennsylvania. She is an external affiliate of the Population Research Center and the Human Capital and Economic Opportunity Global Working Group at the University of Chicago and a faculty affiliate of the International Max Planck Research School for Population, Health, and Data Science in Germany. She was an Assistant Professor of Sociology and the College at the University of Chicago from 2015 to 2019. She was a visiting scholar at the Russell Sage Foundation during 2023–2024.

Song received her Ph.D. in Sociology from the the University of California—Los Angeles (UCLA) in 2015, with the dissertation "Social Stratification in Multiple Generations." Before that, she completed her Master of Sciences in Statistics at UCLA in 2013, Master of Philosophy in Social Sciences at HKUST in 2010, and Bachelor of Arts with the Highest Academic Honors ​at Renmin University of China in 2008.

Her research interests include social mobility, occupations and work, population studies, and quantitative methodology. Her current topics of investigation include economic mobility through lifecycle and intergenerational processes, the evolution of occupational structure, new occupational ranking and autocoding methods, kinship network, and the link between intra- and intergenerational mobility.

Song's research focuses on the patterns and determinants of steps in career progression — through changes in jobs, occupations, employers, organizations, and earnings — that occur over the stages of the life course. Her early work explored the conditions under which families rise and fall across multiple generations — why do some families successfully preserve their advantages while others fall into poverty or experience downward mobility after a certain number of generations? What strategies do elite families adopt to maintain their growth and sustainability? She answered these broad questions by combining data sources from linked censuses, longitudinal surveys, family genealogies, and administrative and business records. This research developed a complex, dynamic view of how individuals, families, and organizations create and reproduce economic advantages. The findings also showed micro-macro linkages between workers’ working trajectories and evolutions in technology, foreign trade, outsourcing, workforce restructuring in occupation and industry, and consumption shifts.

As a quantitative methodologist, Song has developed Markov chain demography models for genealogical processes, population estimation methods for overlapping generations, the extended Goodman-Keyfitz-Pullum kinship model with time-varying rates, multivariate mixed-effects location-scale models for inter- and multigenerational data, and weighting methods for reconciling prospective and retrospective mobility estimates. She is currently working on occupation and industry write-in auto-coding using machine learning methods.

Song received the 2021 William Julius Wilson Early Career Award from the American Sociological Association. Her publications received multiple awards from the American Sociological Association (ASA), the International Sociological Association Research Committee on Social Stratification and Mobility (ISA-RC28), the Integrated Public Use Microdata Series (IPUMS), and the Demographic Research. She received the Mentor of the Year Award from the Department of Sociology at the University of Pennsylvania in 2022. She has served on the editorial boards of the American Journal of Sociology, Demography, Sociological Methodology, Social Science Research, and Research in Social Stratification and Mobility.