My latest work lives at the intersection of data science and human-centered design. At Honor, I’m designing how an org’s culture gets encoded into a knowledge graph — and how that data flows back out to learners through an AI coach and curriculum. I think about where value is created (the moment of genuine curiosity), how to preserve it (private reflection, not just reactions), and what the product owes the person down the line (evidence of growth).

I believe that the friction of working through an idea — arguing with a peer, sitting with a question, changing your mind — is where genuine understanding forms. My design philosophy is that AI should deepen human connection, not sever it. At Honor, that meant building a coach that catches a learner at the moment of real curiosity and invites them to think harder and engage more honestly with the people around them — not click past the hard part. A concept graph tracks what you actually understood; a notebook preserves the struggle; a social feed surfaces who else cared about the same idea, and why they disagreed.

I design with words and tools, variables and logic, but above all else, I design in constant conversation with engineers — not to hand off specs, but to imagine what’s possible together. The best questions emerge when design and engineering are in the same room. I know what I don’t know, and I’ve found that knowing that clearly is often the most useful thing I can offer a technical team.