PhD recipients are very welcome too. The registration is put on hold for me to check manually. The algo to auto verify works for PhD candidates in several universities (US and abroad, not only .edu)
The application and approval process, filtering, certification, authentication, etc. sounds like a separation problem. Since the site is about math, IIRC there's some math for separation problems??
Uh, since the site is for "math/stat" and there have to be rates of false positives, that people are already complaining about, and false negatives, I don't see people complaining about spam, then we're into statistical hypothesis testing, right? Sooo, to do better on the rates of false negatives, we want more data for a more powerful test. Sooo, we need a multi-variate test. Since no way can we justify assuming probability distributions for all the relevant data, we need a distribution-free test. So, where can we find one of those???
Disclosure: This question is just an exercise. For an answer, I published one of those. So, it's an applied probability calculation based on an algebraic group of measure preserving transformations! It may be a rationalization of resampling theory. Crudely the result is obvious, but a proof is tricky. It may be that the work is a stimulation for and or connection with approximate independence, e.g., maybe as in some work of Choquet student M. Talagrand.
Could you elaborate on the vision behind the "selection" criteria? Also, it would be best to clearly explain that in the Hessix user guide, in the spirit of communicating clearly with your audience :-)
You should probably make it more clear on that page. If the email address is only required to verify current candidates, why is it required for all registrants?
Uh, since the site is for "math/stat" and there have to be rates of false positives, that people are already complaining about, and false negatives, I don't see people complaining about spam, then we're into statistical hypothesis testing, right? Sooo, to do better on the rates of false negatives, we want more data for a more powerful test. Sooo, we need a multi-variate test. Since no way can we justify assuming probability distributions for all the relevant data, we need a distribution-free test. So, where can we find one of those???
Disclosure: This question is just an exercise. For an answer, I published one of those. So, it's an applied probability calculation based on an algebraic group of measure preserving transformations! It may be a rationalization of resampling theory. Crudely the result is obvious, but a proof is tricky. It may be that the work is a stimulation for and or connection with approximate independence, e.g., maybe as in some work of Choquet student M. Talagrand.