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