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statsnow

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  1. 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.

     

  2. Actually gradient boosting  came from Berkeley from Leo Breiman if history is correct.  Freidman also got his phd at Berkeley .  Bartlett who is at Berkeley now developed functional gradient boosting which is a more general concept.  It was developed at the same time Friedman published his papers.  I dont really think you can say that one group revolutionized the field if you look at all the historical facts

  3. @statsGod  I think if you want to learn theoretical statistics you are probably correct about Stanford.  However a department is not totally defined by a few stars.  There are other factors that go into an education .  Michael Jordan is the leader in artificial intelligence and a few other areas. That is greatly valued by many people.  Practical application of statistics and methodology is important to very many people.  I am not sure there are many people at Stanford that do Causal Inference.   Departments hire by what is their perceived needs and that can vary from year to year.   Also the lack of diversity at Stanford can and has been seen as a negative by many people.    

     

     

  4. Not so sure why people think stanford is better than berkeley.  It is a smaller department than berkeley.  It is mostly theoretical.  Berkeley has much closer ties to EECS and does a lot more applied and methodological research Not getting into the politics of this however A lot of people have complained about the lack of diversity at stanford.  I have heard many female applicants accepted at Stanford have rejected it because of its diversity reputation.   I am not sure if they have ever had a black or Hispanic phd student at stanford.   

  5. Lower division means the course number is less than 100 at cal.  100 to 199 means upper division and 200 and above is graduate level.   Berkeley wants to know what you actually learned.  Telling them the text books and syllabus you went through is helpful.   Beginning ODE and linear algebra are generally considered lower division classes

  6. MrKrabs what I would do is ask for the names of former CSU students who were in their PHd statistics programs.   I would ask for UCI, UCLA, Davis and Berkeley.  That is proof one way or the other.  I have heard reasons why this happens but I would prefer not to say in an open forum.  I dont think it is fair if my information is correct.  The selection process will also change this fall  if prop 16 passes in November.  Best of luck to you.

  7. I agree with what Bayessays and Stat Asst Professor have to say.  I spent 4 summers at FAANG and the experience was well received.  It is  always a plus to have good coding skills.  Remember too that alot of the good people who get their phds go back into industry or some spend a bunch of time consulting with industry after their PHd.  The industry money temps alot of people. It also helps being a domestic URM .

    At this point I dont think you need to spend more time working on your research portfolio.  I think alot of schools will take you

     

  8. I have a variation on the original question.  Some of the posters seem very knowledgeable in this area.

    If someone attends a top 10 stats program what do the schools look for if the applicant wants a tenure track   research  based assistant professorship at an R1?

    Do teaching skills  matter?
     

    Is there any way to bypass a postdoc these days and go straight into an AP?  Is there any way to quantify how many publications and in what journals are needed for the AP?  How much do LORs really matter?   
     

     

     

  9. I have familiarity with Berkeley.  Berkeley is consistently rated number 1 or 2 in stats, math and CS.

    Berkeley stats is much bigger than CMU.  There is an extremely close tie between EECS and stats at Berkeley. Many of the stats professors have joint appointments with EECS.  Berkeley also has close ties with Stanford, Harvard and Silicon Valley.  It is extremely easy to get a summer internship in Silicon Valley because of its close ties.  Those internships can pay around 10k per month.

    Berkeley has lots of young faculty that are eager to work with new students .  Look at Ding, Feller, Fithian, Pimental and Steinhardt.  In addition there are a number of postdocs at Berkeley.  Berkeley has almost 4 times as many postdocs as CMU.

    Most students at Berkeley have two or more advisers.  There is lots of flexibility in terms of  selecting your advisers.  They do not all have to come from the stats department. Many students wait a couple of years to pick their advisers.  There is also no qualifying exams at Berkeley. There are no prescribed classes that need to be taken .  Many students easily move across research areas.  And it fact it is strongly encouraged at Berkeley

    In terms of ML Berkeley is consistently rated at the top along with CMU.  You may want to look at the Yu group or BAIR.  Berkeley Artificial Intelligence Research has 30 professors associated with it and over 200 grad students and post docs. BAIR is doing cutting edge research in a multitude of areas

    In terms of flexibility I dont think there are departments that come even close to Berkeley.  

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