In a talk-filled day, McWilliams (Princeton) talked about super-massive black-hole merger events and their detectability (through gravitational waves) with pulsar timing, Zrake (NYU) defended his PhD on relativistic turbulence, and Schölkopf gave the first of his Courant Lectures at NYU on causal inference and machine learning. Zrake is particularly deserving of congratulations: He has demonstrated that (warm) relativistic turbulence has very similar statistics to non-relativistic turbulence, which is very, very new. He did this by writing and operating some pretty high-end open-source code.
The day began with a discussion with Tinker (which led to a set of email trails with SDSS-IV eBOSS) about how to make a uniform sample of quasars on the sky, when quasars look very like stars (morphologically and in color) and the photometric errors and stellar density are varying significantly. The answer is: You can't make a uniform sample, for extremely deep reasons: There really is far less information when the errors get worse, and there really are different prior expectations in regions of different stellar density. However, we talked about various approaches to mitigating final, observed quasar density variance in the final observed sample. Nothing is easy here. The problem is that quasars and stars look very similar in the SDSS ugriz filter set. (The crazy thing is that LSST has now set in stone that they will use the same filters! That seems like a big mistake, given everything we now know about stars, galaxies, and quasars in the ugriz bands.)