Thoughts on BigData/InferenceSummerSchool/KTS

Two basic issues:
1)  where does the disconnect between the astronomers and the ML/CS folks actually occur?
2)  are the any women of any seniority on the astro/data-mining world

On 1)
HWR's suspicion is that astronomers almost always deal with data that are noisy,
 and heteroscedastic at that.
Therefore, astronomers know how to write down likelihood, go Bayes, but: and then??
CS/ML folks have amazing tools to classify, but these tools almost all fail ungracefully in the
"very noisy" regime. I.e. are not good at simply ignoring differences in object labels that can sensibly
attributed to only noise, not to any inherent difference of the objects.

What's a good definition of noisy here:
Let's presume any object has many data points (e.g. pixels in the spectrum of a star);
there is the regime where the data variance at any one pixel due to noise is comparable
to the (to be classified) ensemble variance (noiseless) in that 'pixel'. What are good ML tools
in that regime.

What to do about it
a) try to spell this out clearly, and ask Hogg, DFM, ZI, BernhardSchöllkopf etc. for insights?
b) initiate some culture-gap bridging exercise with the HITS group, to see how useful that is..


On 2)
The Tel Aviv Big Data conference (Dec. 15), the search for lecturers for an IMPRS summer school,
showed the paucity of "obvious" female scientists to serve as lecturers or tutors.
What to do?
-- find out whether this is just a consequence of HWR's ignorance? Ask Hogg, Schöllkopf, Ivezic, Bailer-Jones, DFM for names...
-- initiate discussion with Reutter to see whether there might be any interest in initiating
   an "award", amounting to a 3-year fellowship, for women in data science.
  To be taken as a post-doc, or a repeat summer fellowship.

Tel Aviv notes

Post Tel Aviv

-- M-stars WD's with Dani

-- Cannon residual into Nikos Gianniotis' machinery

-- write-up for Chao Liu's PD.