There’s a quote I keep coming back to — attributed to Richard Feynman, though who really knows — that goes something like: “What I cannot create, I do not understand.” I’ve always taken this to mean that the act of building something (a model, an argument, a piece of writing) forces a kind of understanding that passive reading never achieves.
This blog is my attempt at taking that seriously during graduate school.
What This Space Is For
I’m starting an MS + MBA, and the next two years will involve a density of new ideas that I won’t be able to absorb without some system for processing them. This blog is that system. Expect a mix of:
- Paper reviews — distilling academic papers into their core ideas, with commentary on what’s interesting and what’s missing
- Code experiments — when I implement something from a paper or class, I’ll write it up here with code and results
- Research dispatches — notes from ongoing research, including dead ends
- Reflections — on the experience of grad school, career thinking, and the intersection of technology and business
A note on audience: I'm writing primarily for myself — as a learning tool. But I'm keeping it public because I believe in working with the garage door open. If you find something useful here, that's a bonus.
The Broad Knowledge Base Philosophy
Throughout my career, I’ve optimized for breadth over depth. At dive.ai, I was building LLM chatbots one week and redesigning cloud infrastructure the next. Before that, I was doing econometrics for a think tank while teaching linear algebra at a university.
This isn’t accidental — it’s a deliberate strategy. The most interesting problems I’ve encountered sit at the intersection of disciplines. Fraud detection requires understanding both the ML models and the business processes they protect. A competitiveness index requires PCA and political context.
Graduate school is an opportunity to add more tools to this toolkit, and this blog is where I’ll document what I’m learning.
