Why I keep ending up in the same problem

People occasionally ask why my CV reads like four different careers stapled together: general relativity and early-universe cosmology, then black hole magnetospheres, then physics-informed machine learning for cardiology, then neutrinos, and now markets alongside all of it.

The honest answer is that I have only ever worked on one problem. You have noisy, incomplete data produced by a process you understand partially. You want a statement about the world that is not merely precise but calibrated — one whose error bar means what it claims to mean. Everything else is domain vocabulary.

Frame dragging near a Kerr horizon, conduction velocity across an atrial electrode grid, a flux systematic propagating from a near detector to a far one, a regime shift in a futures curve — the objects differ, the inference structure does not. Once you see that, moving between them stops feeling like starting over and starts feeling like carrying the same toolkit into a new room.

This is where I will write about that work: what I am reading, what is not working, and the occasional thing that is. Sparse and technical rather than frequent and thin.