I spent the day in Princeton, visiting both the Department of Astrophysical Sciences and the Center for Statistics and Machine Learning. I had many great conversations, and I gave a talk that is about data-driven methods and their relationships to what we think of as being machine learning.
One highlight was a conversation with Ryan Adams (Princeton), who has brought some serious probabilistic methods to astronomy but is himself a computer scientist and statistician. He and I discussed the issues of algorithmic real-time, adaptive target selection for astronomical projects (especially EPRV-like). There is the full Bayesian decision thing, which I know how to do but which is expensive. But there is the idea that the decisions should be simple, and explainable. He pointed out that this is a huge area of research right now, and it connects to many things, especially in ethical situations: We want simple, explainable decisions! That's an interesting idea to bring into astrophysics.
There were many other great conversations, ranging across polarimetry of exoplanets, star shades and coronographs, neuroscience, stellar surface mapping, photometric redshifts, and astronomical catalog making.
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