At eighteen, I was a freshman at a university in China, fighting to rank first in computer science. I believed that rank would answer the only questions that mattered to me: Who am I? What am I here to do? It did not. So I left home to find the answer for myself.
I knew only this much: I wanted to understand myself, and through myself, other people. I had no map, only a method: to follow my own judgment into every kind of work that might teach me something true, and to let the evidence of my own experience decide who I was.
In college, I followed my curiosity and worked in campus service, community organizing, and mental health advocacy, alongside international students, student government, and inclusion initiatives. I then realized the purpose of my life — to create a positive social impact. When a fellow student died, I founded International Peer Support, a student-led effort to reach international students with help that spoke their language and understood their culture. That work taught me a hard truth: people need help long before institutions are ready to give it, and even a good program fails if it cannot reach the right person, at the right moment, at the scale the need demands.
My research assistantship taught me the price of certainty. In Dr. Kelsie Forbush’s clinical eating-disorder lab, I saw what it costs to build real evidence: years spent tracking a couple hundred patients, greeting cards and small incentives sent out for no reason except to keep a human connection alive long enough to gather the data. I learned to respect that slow, deliberate labor. One question from that lab I have never put down: could learning, engagement, and support be scaled without sacrificing the human sensitivity that made them worth anything in the first place?
I let graduate school wait. The answer I wanted lived in the world, and I went after it. I returned to China as an adult and landed in Shenzhen, China’s Silicon Valley. There, technology stopped being an abstraction. I watched an ordinary eating-behavior app, Mint (薄荷), China’s answer to MyFitnessPal, gather millions of data points in an instant, from ordinary people living ordinary days. That was my turning point. From then on, technology meant something larger to me: infrastructure. A way to understand human behavior at scale, to sharpen services, and to extend the reach of social interventions far beyond what any single program could touch. In those same years, my home filled wall to wall with books; whatever I grew curious about, I took up and made my own.
I came back to the United States for a master’s degree in data science. I wanted a stronger technical foundation, but the deeper motive never changed: to build things that matters and benefit society at scale. Years of industry work in data science, machine learning, software engineering, and AI infrastructure made me sure of my ability to design and evaluate technical systems. They also convinced me of something I could no longer set aside: these skills belonged back in social welfare, where they could do real good (not just “for good”).
Today, every path I have walked converges. I research and build responsible AI systems for social work and social welfare: child welfare, policy-aligned documentation, human-centered AI oversight, and computational methods for public systems. My aim is a single one: to build AI and data infrastructure that serves human, strengthens human judgment, accountability, and care.