interpreting and combining imaging

My data started with two great conversations about imaging and data science. In the first, Marc Gershow (NYU) and Rui Wu (NYU) came to talk to me about their project with fruit-fly larvae (mentioned here). They do a multi-step dimensionality reduction on the (immense amounts of) video data they have and then look for discrete behaviors and discrete changes in behavior. We discussed the possibility that we could re-cast all or parts of this problem as a regression, that would be fast and interpretable. It maps on to a lot of problems we are doing with stars, especially The Cannon and derivatives thereof.

In the second conversation, Mike Blanton (NYU) and Dou Liu (NYU) discussed with me their project to generalize spectro-perfectionism into a method (mentioned here) for combining badly and irregularly sampled imaging. We discussed a certain part of the problem that I was discussing many moons ago with Sam Roweis and Adam Bolton: The method involves a renormalization step, and this renormalization is very sensitive to details. Indeed, when Roweis died, he and I were looking at whether we could replace this step with something that makes more sense. I advised Blanton and Liu to take a calibration approach, where they run not on the flatfield-normalized spectral data, but run on the raw data and the flatfield data and deliver a ratio of results. For now. Until we can figure out the Right Thing To Do (tm).

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