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Hi Manav,
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<div> I took another look at the poster. Overall, it looks good. Here are a few comments/questions.</div>
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<div>1. The pie chart on left shows “No Data” as a categories and a number “21” looks to be indicating</div>
<div>that at 1 of 4 white “spokes” in the plot. </div>
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<div>2. The two methods look to be presented quantitatively in different ways. A plot for Hydra and a</div>
<div>table for the Non-ML method. Given the bullet about the 1275 plots, I’m not sure I know how to</div>
<div>compare the two. Does it make sense to add a horizontal line to the plot? Hydra numbers to the</div>
<div>table? </div>
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<div>I see two significant results here:</div>
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<div>A. Hydra model takes relatively little data to generalize the problem of identifying “Bad"</div>
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<div>B. A custom algorithm designed specifically to classify a known problem doesn’t necessarily </div>
<div>do better than an ML-model for a broad classification. They *can* though be coupled to</div>
<div>refine the classification in cases where it doesn’t make sense to train a model (i.e. class for</div>
<div>every possible bad module).</div>
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<div>I understand there is a tight timeline so take these as minor comments on the poster that</div>
<div>don’t necessarily require action. I would, however, suggest you make sure to emphasize</div>
<div>these point in the thesis.</div>
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<div>Regards,<br>
-David<br>
<br>
-------------------------------------------------------------<br>
David Lawrence Ph.D.<br>
Staff Scientist - - EPSCI Group Lead<br>
Thomas Jefferson National Accelerator Facility<br>
Newport News, VA<br>
davidl@jlab.org<br>
(757) 269-5567 W<br>
(757) 746-6697 C</div>
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