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  1. “The job candidate did go to one of the top 15 schools (according to the USNWR rankings).” @Stat Assistant Professor One last question. What was her research area? Was she applied, theoretical or methodological?
  2. @Stat Assistant ProfessorI don't think working with an Assistant Professor is necessarily an issue. There was one job candidate on the job market in the 2019-2020 hiring cycle who got like, 20 interviews, and her advisor was an Assistant Professor. She also got offers from UIUC, UNC, UFlorida, UMinnesota, Columbia, and probably others as well What was the profile of this candidate?
  3. 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?
  4. To clarify what bayessays just posted. Jordan is not the only professor that does ML at Berkeley. There are probably at least 30 professors and countless more postdocs involved in some form of ML at Berkeley.
  5. 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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