Overview

My research investigates how disruptive life events—such as divorce and job loss—contribute to social inequality across the life course. I bridge sociological theory with computational and statistical methods, including machine learning and causal inference, to examine how institutions shape individual trajectories of risk and resilience. Drawing on longitudinal data and a life course perspective, I develop and apply methods to capture time-varying processes, estimate causal effects, and understand how advantage and disadvantage accumulate over time. Ultimately, my work aims to generate insights that inform targeted interventions and promote equity across diverse social contexts.

Substantive Research Areas

Life Course Dynamics and Cumulative Disadvantage

Guiding Question:
How do the timing, duration, and sequencing of life events contribute to cumulative inequality over time?

Research Focus:

  • Trajectory-based causal inference

  • Post-divorce transitions

  • Longitudinal sequence modeling

Infracategorical Inequality and Within-Group Stratification

Guiding Question:
How do differences in experiences and resources within social groups shape patterns of inequality?

Research Focus:

  • Skin color and intra-racial stratification

  • Gender ideology and cultural variation

  • Within-group heterogeneity in social outcomes

Social Stratification and Institutional Contexts

Guiding Question:
How do welfare states and mobility regimes shape the distribution of life risks and opportunities?

Research Focus:

  • National and cross-national comparisons

  • Welfare state regimes

  • Institutional foundations of inequality

Methodological Research Areas

Machine Learning & Representation Learning

Guiding Question:
How can deep learning models be adapted to improve counterfactual reasoning in life course research?

Research Focus:

  • Transformer-based architectures for estimating counterfactual outcomes

  • Masked sequence pretraining for learning temporal structure

  • Joint trajectory embedding for assessing plausibility and supporting positivity

Causal Inference &
Estimation

Guiding Question:
How can we estimate causal effects—both static and time-varying—using flexible, data-adaptive tools?

Research Focus:

  • Parametric and semi-parametric g-computation

  • Marginal structural models for time-varying treatments

  • Causal mediation forest and heterogeneous effect estimation

Longitudinal Modeling & Sequence Analysis

Guiding Question:
How can we model trajectories and transitions over time to capture cumulative and path-dependent forms of inequality?

Research Focus:

  • Sequence clustering and trajectory typologies

  • Longitudinal modeling of life course trajectories

  • Cumulative exposure models for mortality and inequality