Machine Learning for Family Research

With an interdisciplinary background in data science and human development and family studies, our lab is also dedicated to understanding how we can use machine learning to advance discoveries in developmental and family research.

Our current project, funded by the Spencer Foundation, conducts machine learning analysis on large-scale, national longitudinal datasets to build predictive models based on adolescent experiences in predicting their developmental outcomes, including educational and career achievements and well-being.

Featured Publications

Sun, X. (2024). Supervised machine learning for exploratory analysis in family. Journal of Marriage and Family: Mid-Decade Special Issue on Theory and Methods. [online first] https://doi.org/10.1111/jomf.12973

Sun, X., Ram, N., & McHale, S. M. (2020). Adolescent family experiences predict young adult educational attainment: A data-based cross-study synthesis with machine learning. Journal of Child and Family Studies, 29. 2770-2785. https://doi.org/10.1007/s10826-020-01775-5

Featured Presentations and Talks

Sun, X. (2024, May). Introduction to Machine Learning for Developmental Psychology and Family Research. Invited talk for the Department of Psychology and Behavioral Sciences, Zhejiang University, virtual.

Sun, X. (2023, April). Machine Learning for Human Development and Family Research. Invited talk for the Minnesota Population Center, Minneapolis, MN.

Sun, X. (2022, November). Uncovering Hidden Perspectives: Using Machine Learning to Understand Fathering, Fatherhood, and Child Care Quality. Invited discussant for symposium at the National Council on Family Relations, Minneapolis, MN.

Sun, X. (2022, November). Machine Learning for Human Development and Family Research: An Overview and an Example. Talk for the Machine Learning Seminar Series, Data Science Initiative, College of Science and Engineering, University of Minnesota, Minneapolis, MN.

Sun, X., Updegraff, K. A., Cahill, K., & Umaña-Taylor, A. J. (2022, November). Identifying key contextual factors in Latinx parents’ depressive symptoms: A machine learning approach. Paper presented at the National Council on Family Relations, Minneapolis, MN.

Sun, X. (2022, February). Introduction to Machine Learning for Human Development and Family Research. Invited talk at NICHD SBSBeat (Social and Behavioral Sciences Branch Education and Training) Seminar.

Love, B. & Sun, X. (2021, July). Big Data & Data Mining Approaches for Psychology Research. Invited talk for the UCL (University College London)-PKU (Peking University) Summer School in Experimental Design in Psychology. (Love and Sun had equal contributions)

Sun, X. (2020, November). Introduction to Machine Learning for Family Research: Basic Concepts, Common Algorithms, and Application Examples. Workshop at the National Council on Family Relations, virtual conference. (137 registered attendees)

Sun, X. (2019, March). Leveraging Machine Learning Methods for Research on Child Development in the Big Data Era. Symposium chaired at the Biennial Meeting of Society for Research on Child Development, Baltimore, MD.

Sun, X., & Ram, N. (2019, March). Family experiences in adolescence predict young adult educational attainment: A machine learning approach. Paper presented at Biennial Meeting of Society for Research in Child Development, Baltimore, MD.