There is nothing like being isolated in the middle of nowhere with bad internet! Johnson (UCSC) and Foreman-Mackey worked on Johnson's problem of fitting simultaneously photometry and spectroscopy, with an extremely flexible model for spectrograph calibration (or calibration residuals). The key idea that moved us forward is that you can fit the large-scale calibration "vector" with a polynomial and then pick up the small remaining residuals with a Gaussian Process. The latter is analytic to marginalize out, so it doesn't increase the number of parameters in the MCMC sampling (and hence doesn't hurt much CPU-wise). The results are beautiful, even on completely uncalibrated spectra: People: Don't waste your time on calibration! I think we will be able to write a very strong paper making this point, for a large set of spectroscopy use-cases.
Weisz (UCSC) and Foreman-Mackey worked on hierarchical inference of the initial mass function of stars, given stellar-population fits to a large number of resolved clusters in M31. Each cluster gets a very different IMF in a point-estimate sense (maximum-likelihood or whatever), but is this variance intrinsic or just from observational noise? We did our usual (should be patented!) importance-sampling thing to infer the intrinsic distribution and find that the data are at least marginally consistent with a delta-function (narrow) distribution. But we are only looking at a tiny fraction of the available data.
Late in the day, Marshall showed up! Tomorrow we will do some gravitational lensing.