Biostats PhD/Masters 2021: Profile Eval

[icantdoalgebra]

As a side note to this point, a lot of Berkeley's most famous statisticians have passed away. Note this is not particularly relevant to the current discussion at hand since we are discussing the departments today (and also because ... dead people don't do research), but if we do want to talk about "revolutionary" developments I think it is somewhat worthwhile to bring up Berkeley's significance (although it really shouldn't affect anybody's decision in where to go or departmental rankings because this is quite historical).

Jerzy Neyman was a professor at Berkeley and was the key driving force behind the formation of the department and he was no slouch when it came to revolutionary developments (quotes deliberately omitted because confidence intervals, hypothesis testing, potential outcomes are all, beyond any doubt, revolutionary developments).

George Dantzig was a student and professor at Berkeley for a while (although he left for Stanford later) but while at Berkeley he developed the simplex algorithm which may not be revolutionary in statistics, but is the foundation for several other fields.

David Blackwell was a professor at Berkeley for several decades; you might know him from Rao-Blackwell, but he also did a lot more than that (Blackwell-Macqueen, etc.).

There are a few others that can be mentioned too, David Freedman has had quite a bit of influence outside the world of statistics (and in statistical education), Leo Breiman has been mentioned above (CART and random forests), Le Cam and his work on lower bounds, and several more. 

I bring them up not because I'm trying to argue against any of the positions taken here: I agree that Stanford's statistics department is better today. It is in a class of its own when you consider traditional statistics. But I still feel somewhat obligated to defend Berkeley if we are mentioning "revolutionary" developments without mentioning Berkeley's own rich history. However let me re-emphasize, none of the above should be a part of one's consideration for picking a program for grad school nor should it be a part of decision making about which departments are stronger.

[statsnow]

Not that any of this matters now but when Friedmans paper was published he was at Berkeley where he got his Phd.   I agree for theoretical statistics that Stanford is the best now.  However when it comes to many other areas of statistics including applied statistics, causal inference etc I believe Berkeley is better.  Berkeley is also much better when it comes to diversity.  The world only needs so many theoretical statisticians

I am a strong believer that the best place is where you feel the most comfortable no matter where that may be.