My path into computer science started with human-centered design. I studied Human-Centered Design & Engineering at the University of Washington, where I learned how to conduct user research, design accessible systems, and think carefully about the ways technology shapes people's experiences.

After graduating, I worked in UX design, but I gradually became more interested in the problems happening underneath the interface. A thoughtful design can make a product easier to use, but it cannot fix a biased model, an unreliable AI system, or a backend that does not work as intended. I wanted to understand and work on those deeper technical layers, which led me to pursue an M.S. in Computer Science at USC.

Since then, I've been building a stronger foundation in software engineering and machine learning. At Google, I've worked on internal AI systems for software issue triage, collaborative debugging, user-feedback analysis, and developer workflow automation. In my academic work, I've explored fairness in toxicity detection and how people interpret and respond to AI-generated debugging guidance. I'm also beginning to contribute to research involving conversational AI, persona inference, and the safety of increasingly social and emotionally significant AI systems.

My HCI background still shapes how I approach technical work. I care about whether a system performs well, but I also want to understand who it works for, how it fails, and how those failures affect real people. Going forward, I hope to keep developing both the technical and research skills needed to build AI systems that are reliable, responsible, and genuinely useful.