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.

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 stratification and mobility, poverty, inequality, population studies, and quantitative methodology. Her current topics of investigation include the gap between factual and perceived inequality, multigenerational social mobility and kinship inequality, the evolution of occupational structure, and statistical methods for characterizing the link between intra- and intergenerational mobility..

As a demographer, Song uses mathematical, statistical, and computational methods to study the rise and fall of families in human populations across time and place. Her past research has demonstrated the values of genealogical microdata for studying long-term family and population changes. These data sources include historical data compiled from family pedigrees, population registers, administrative certificates, church records, and surname data; and modern longitudinal and cross-sectional data that follow a sample of respondents, their offspring, and descendants prospectively or ask respondents to report information about their family members and relatives retrospectively.

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.

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.