Bee Rosa Davis.

I joined Pandora as an early engineer working on its machine-learning platform. The Music Genome Project’s carefully curated musical descriptions offered a lesson that stayed with me: useful computation begins with deciding which relationships to record.

Trained musicians described music through its characteristics. Those descriptions helped make similarity something a machine could work with. I came to see curation as a way of giving data a useful shape—a choice about which distinctions mattered before any prediction was made.

My own work included audience modeling, contextual prediction, ad automation and streaming analytics. Across the engineering roles that followed, I kept returning to that curatorial approach.

What began as a lesson in describing music became a research program about representation. I began investigating related questions across domains: which descriptions preserve a system’s structure, which identify distinct states as equivalent, and what is lost when we translate between them?

I have built a fiber-bundle database and computational tools to explore these questions. My aim is to connect the translations engineers implement with the geometry they preserve—or fail to preserve—and derive usable bounds on the resulting error. Each application needs its own assumptions, comparisons and evidence.

Working independently has taught me to return to earlier results. When I find an error in a test or proposed proof, the work is to correct the method, revise the conclusion, or retract the claim. That responsibility is part of the research itself.

I am seeking a research home that will mentor me, challenge my arguments and hold the work to demanding standards. I bring experience building systems, a willingness to make testable predictions, and a commitment to following the evidence when it changes my mind.