My Research Journey

Origins: Growing Up Amid Large-Scale Disruption

Growing up in South Korea, I witnessed how the Asian financial crisis fractured families and reshaped entire life paths. These early experiences attuned me to how structural shocks don’t just cause immediate harm—they can set lives on diverging trajectories, compounding inequality over time.

“Why do some people recover, while others fall into long-term precarity? And how do policies shape those divergent paths?”

The Question That Anchors My Work

As I began graduate training in sociology, I found myself drawn to a deceptively simple but enduring question: How do life events accumulate to shape people’s futures?

This question has anchored my research across diverse domains—job loss, divorce, and education—all unified by a broader concern with how risk and resilience are structured across the life course.

A Methodological Turn

Yet as I deepened my substantive expertise, I encountered a methodological gap. Conventional tools in the social sciences often failed to capture the temporal, contingent, and compounding nature of the processes I was studying.

In response, I pursued formal training in statistics and machine learning—not just to improve prediction, but to build analytic frameworks capable of modeling causal processes over time in order to advance sociological explanation.

Where I’m Going: Computational Sociology and Generative Models

Today, my research bridges sociological theory with computational methods. I develop causal inference tools and deep learning models—such as Transformer-based g-computation frameworks—to analyze how life trajectories unfold under different institutional conditions.

Building on this foundation, I’ve begun exploring generative AI as a new tool for modeling complex social processes—particularly those involving decision-making under disruption. This complements my broader interest in tracing how inequality is reproduced through institutional and behavioral dynamics over time.

What I Value

This integration of theory and method—of deductive and inductive reasoning—reflects what I value most about research: its ability to illuminate how inequality operates not only in theory, but in lived experience, grounded in data.

My goal is to produce work that contributes both to scientific understanding and to practical policy response.