data science; data-driven models

Today I turned down an invitation to the White House. That might not be research, but it sure is a first for me! I turned it down to hang out more with Vanderplas (UW). I hope he appreciates that! At the White House Office of Science and Technology Policy (okay, perhaps this is just on the White House grounds), there was an announcement today of the Moore-Sloan Data Science Environment at NYU, UW, and Berkeley. This is the project I was working on all summer; it has come to fruition, and we start hiring this Spring. Look for our job ads, which will be for fellowship postdocs, software engineering and programming positions, quantitative evaluation (statistics) positions, and even tenure-track faculty positions (the latter coming from NYU, not Moore and Sloan, but related).

At lunch, Vanderplas, Foreman-Mackey, Fadely, and I discussed alternative publication models and how they relate to our research. Foreman-Mackey reasserted his goal of having any exoplanet discoveries we make come out on Twitter before we write them up. Vanderplas is wondering if there could be a scientific literature on blogs that would "play well" with the traditional literature.

Earlier in the morning, Vanderplas gave us some good feedback on our data-driven model of the Kepler focal plane. He had lots to say about these "uninterpretable" models. How do you use them as if they provide just a calibration, when what they really do is fit out all the signals without prejudice (or perhaps with extreme prejudice)? Interestingly, the Kepler community is already struggling with this, whether they know it or not: The Kepler PDC photometry is based on the residuals away from a data-driven model fit to the data.

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