machine learning

The day started with a somewhat stressful call with Hans-Walter Rix (MPIA), about applied-math issues: How to make sure that numerical (as opposed to analytic) derivatives are calculated correctly, how to make sure that linear-algebra operations are performed correctly when matrices are badly conditioned, and so on. The context is: Machine-learning methods have all sorts of hard numerical issues under the hood. If you can't follow those things up correctly, you can't do correct operations with machine-learning models. It's stressful, because wrongness here is wrongness everywhere.

Later in the morning, Kilian Walsh (NYU) brought to me some ideas about making the connections between dark-matter simulations and observed galaxies more flexible on the theoretical / interpretation side. We discussed a possible framework for immensely complexifying the connections between dark-matter halos and galaxy properties, way beyond the currently-ascendent HOD models. What we wrote down is interesting, but it might not be tractable.