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Stat Assistant Professor

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Stat Assistant Professor last won the day on September 30 2020

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About Stat Assistant Professor

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    Statistics (faculty)

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  1. @Bumblebea This is all very fascinating to me. I read your post with great interest (although I am not in the humanities, it is always interesting to read others' accounts who have also braved the academic job market). I definitely agree with you that aspiring faculty should give themselves a time limit for amount of time they are willing to be on the academic job market. In my field, it is commonly the case that PhD graduates need to do a postdoc or two in order to land a TT position. In my specific field, I would strongly encourage PhD alumni to weigh their other options if they come up empt
  2. I don't have any first-hand experience working in big pharma, but I'm wondering if there is a difference between the jobs that are available for Biostat Masters holders vs. Biostat PhD holders? At your current workplace, do the employees with Biostatistics PhDs get to do more "stimulating," methodological/statistics work than regulatory writing? I know some Biostat PhD graduates who work as research scientists at Eli Lilly and Company. Is the type of work they do materially different from the work that gets done by Biostat MS holders? Also, I have definitely seen Biostatistics PhDs can go
  3. Besides the required classes for your Masters program (I'm assuming that this entails 2 semesters of Casella & Berger-level mathematical statistics, 2 semesters of applied statistics, and a semester Linear Models), I recommend that you take: 2 semesters of real analysis proof-based upper-division linear algebra one other advanced math class of your choosing, if you can fit it in your schedule (if not, then the 3 math classes above should suffice). If you can afford it, it may be worthwhile to enroll in a math class for credit at an accredited school over the summer *b
  4. Yes, you could do that. I think you might have the most luck at schools ranked a bit lower, e.g. those ranked 20-50 in USNWR. However, there is quite a bit of noise in PhD admissions, so you never know. Despite the noisiness in admissions, I *would* say that the very 'elite' programs will most likely favor international applicants who have degrees in Mathematics or Statistics from elite schools like Peking, Tsinghua, USTC, SNU, ISI, etc. and who are usually at the very top of their class. Usually the successful applicants will also have relevant research experience (I saw that Stanford admitte
  5. It does seem as though the very top-tier schools (Stanford, Harvard, UChicago, Berkeley) do accept mainly international students who majored in mathematics or statistics, but I have occasionally seen some economics majors do okay in Statistics PhD admissions in the tier of schools below that (e.g. I know of some people who went to Duke for their PhD who have undergrad degrees in Economics). If you major is not Math or Statistics, then I think it is very important to demonstrate strong evidence of mathematical maturity in your application -- e.g. two semesters of real analysis and other evidenc
  6. No, it shouldn't put you at any disadvantage for industry. In fact, PhD grads from programs ranked 60-80 also can (and do) get jobs at Facebook, Google, and other "prestigious" companies. Industry as a whole is less "prestige"-driven than academia. Relevant work experience (e.g. an internship) and hacking skills are also more essential to getting most of these jobs than publications. I personally know Statistics PhD graduates who got jobs at Google and the like who had no publications. FWIW, schools ranked 30-40 (and lower) are also good enough to land jobs at R1 universities and prestigi
  7. Your list looks very reasonable to apply to with your profile. I think BU and Pitt are relatively safe choices for your profile, and it wouldn't hurt to add another school like MD Anderson or University of Minnesota Biostatistics (i.e. in roughly the same tier as Columbia and UPenn Perelman).
  8. Even if you were to do well in your Masters program, I would consider UC Berkeley, Columbia, and Yale to be completely unrealistic. You have too many B's, and the competition for these schools is very stiff. Some of these schools only accept very few domestic applicants to begin with, and I'm afraid you won't be able to compete against applicants from Ivy schools, Stanford, UChicago, MIT, etc. with higher GPAs and possibly some solid research experience. UC-Davis, UCLA, UC Irvine, and UT-Austin are reaches as well, IMO. It seems as though the UC schools are all very competitive, regardl
  9. Yes, Ohio State had a very strong spatial statistics group at one point including Noel Cressie and Tao Shi, who had great PhD placements (e.g. Jonathan Bradley at FSU was supervised by these guys). However, it seems as though the group has reduced in size. Both Cressie and Shi have left OSU. I would keep an open mind about research areas -- you may ultimately change your mind about what you're interested in, and nowadays, it is common for a lot of people who go into academia to change their research focus during/after their postdoc. This was probably less common many years ago (so you could sp
  10. Gradient boosting is essentially an additive model tailored to decision trees, and the concept of additive models was first developed by Friedman and Stuetzle at Stanford. It is possible that somebody else suggested the idea of boosting for tree-based models, but the gradient boosting machine (GBM) paper that gets cited the most often was written by Friedman when he was at Stanford. The generalized additive model (GAM), an important development in nonparametric regression, was also developed by two Stanford statistics faculty, Hastie and Tibshirani (albeit this was before they joined the Stanf
  11. Yeah, Stanford truly is in a class of its own. No other program has as many faculty who revolutionized the field -- e.g. Tibshirani, Hastie, Johnstone, Candes, Efron, Friedman, etc. Lasso, bootstrapping methods, compressed sensing, additive models (including gradient boosting), etc. were basically invented at Stanford. I would note also that Stanford is also very strong in applied statistics. Efron is the founding editor of Annals of Applied Statistics, and Stanford also has people like Susan Holmes, Trevor Hastie, and Robert Tibshirani. Some of these folks have also worked on statistical
  12. Yeah, in terms of academic placements, Yale has done exceptionally well. This leads me to believe that Yale S&DS must be viewed favorably among many departments. Of course, it's really the responsibility of the applicants themselves to make themselves 'stand out' (you can't just rely on the brand name of your alma mater). But the fact that Yale has produced so many of these outstanding job market candidates who got tenure-track jobs at University of Chicago Statistics, Columbia Statistics, UPenn Wharton Statistics, and Princeton OFRE speaks to the department's strengths. I am not sure abou
  13. I am not inclined to answer this on a public forum. But to make my own personal assessment, I look at student outcomes (i.e. where the PhD graduates have placed), the caliber of the faculty (i.e. current big names and "rising stars"), and the research output (i.e. how actively are the faculty publishing? And which venues are they publishing in?). Note that to assess how "strong" the research output is, it may be a bit different for Statistics vs. Biostatistics. For example, Biostat departments might possibly put heavier weight on publications in journals like Biostatistics, Biometrics Practice
  14. Congrats on your outstanding acceptances! IMO, Yale is a very good program with some excellent professor (e.g. John Lafferty and Harrison Zhou) who are very renowned and well-known in the field of statistics. Yale's academic placements are also fairly impressive -- they've placed PhD alumni at UC Berkeley, University of Chicago, UPenn Wharton, and Princeton ORFE to name a few. This is an excellent placement record, which leads me to believe that many top schools view the Yale S&DS program favorably. If you are leaning towards industry, then I think that either Chicago or Yale would be fine
  15. Well, if your chances of continuing on in a PhD program at UPenn are well above average, then I would strongly consider UPenn AMCS. I know that several of the professors in the Wharton Statistics Department supervise PhD students in AMCS. I also know that some folks in the DBEI Department supervise students in other departments as well (e.g. some CS students who work on NLP are co-advised by faculty in DBEI). Some DBEI PhD students have also been advised by Wharton Statistics faculty (e.g. Edward Kennedy who is now a rising star professor at CMU), so it seems like they're pretty flexible about
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