With a data science training background, I am interested in applying computational methods (especially machine learning) to large-scale dataset with nationally representative samples for building predictive models of adolescent future achievement outcomes based on their earlier experiences across different social contexts (e.g., family, neighborhood, culture, and school). I also study how digital technology (or the digital context) may impact family relationships and adolescent well-being. My interests and focus on contexts for families and adolescents align with the population studies.
My research agenda is focused on family systems processes, adolescent development, sociocultural and digital contexts, and computational methods for family science. Specifically, my current research lines include (1) understanding the role of digital technology in family interactions and adolescent development—specifically, how teens and parents use smartphones and how their smartphone behaviors influence their relationships and well-being, and (2) predicting young adult achievement outcomes from social determinants during adolescence for each racial/ethnic groups. In addition, I am interested in applying innovative statistical and computational methods family and developmental research. My research leverages a variety of data types, including multi-member family panel data, national longitudinal databases, and high-intensity digital data.