My interest in technology grew out of questions about people: how we understand their needs, how services reach them, and what happens when institutions cannot respond. Studying psychology gave me a way to explore those questions. Research and community work showed me how much depends on the systems around an individual.
In college, I worked in campus service, community organizing, and mental health advocacy. After a fellow student died, I founded International Peer Support, a student-led initiative for international students. I wanted support to be accessible to students whose language and cultural experiences were not always reflected in existing services. The experience made me pay closer attention to the gap between offering help and making it possible for someone to use it.
As a research assistant in Dr. Kelsie Forbush’s clinical eating-disorder lab, I learned about the work required to produce useful evidence. Maintaining relationships with participants, following them over time, and collecting data carefully were central to the research. I came to appreciate both the value of that sustained effort and the difficulty of extending it to larger populations. I began asking how data and technology might support research and services while retaining the attention to people that made them useful.
Later, living in Shenzhen exposed me to a different way of thinking about technology. Consumer applications such as Mint (薄荷), an eating-behavior app, showed me how everyday interactions could generate data about behavior. I became interested in what those data could reveal and how digital systems could extend the reach of services. It also raised questions that would stay with me: what can a system actually tell us, whose experiences does it capture, and how should that information be used?
I returned to the United States to pursue a master’s degree in Applied Analytics at Columbia University. I wanted the technical foundation to investigate those questions and build systems myself. My subsequent work in data science, software engineering, and AI infrastructure gave me experience with the practical demands of developing technical systems. It also strengthened my interest in how we evaluate them and decide whether their outputs are useful for a particular task.
My doctoral work in Social Work at Fordham University brings these interests together. I use computational methods to study social and public health questions, and I examine how AI systems can support work shaped by policy and professional judgment. My dissertation focuses on policy-aligned AI for child welfare documentation. Through this work, I am examining how systems retrieve relevant policy, support documentation review, and provide evidence that people can check. Human oversight is part of the research: I want to understand how practitioners can assess AI outputs and retain responsibility for their decisions.
Across these experiences, the questions have become more specific. I want to understand how technical choices affect the quality of evidence, how people interpret and challenge AI outputs, and how organizational rules shape their use. These questions guide my work on AI evaluation, computational social science, and the relationship between technology and public institutions.