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Stat PhD Now Postdoc

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Stat PhD Now Postdoc last won the day on September 25 2018

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About Stat PhD Now Postdoc

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    Statistics (Postdoc)

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  1. Stat PhD Now Postdoc

    Biostat & Computational Math & Statistics

    If you are interested in Statistics, it would make more sense to me to go to a Statistics department. But there are PhD's in other subjects like Computer Science, Electrical Engineering, and Mathematics/Applied Math who eventually become faculty in Statistics departments -- some pretty renowned ones as well, like Michael Jordan (PhD in Cognitive Science) and John Lafferty (PhD in pure mathematics, focusing on geometry). Depends mainly on your research area and what journals you can publish in. If you publish in good statistics journals and related journals/conferences like the IEEE pubs or NIPS, the degree field itself is not so important.
  2. Stat PhD Now Postdoc

    Fall 2019 Statistics Applicant Thread

    It should be the same in Biostatistics as in Statistics -- or any other department where acceptances are made at the departmental level rather than into a PI's lab. When my father was working as a professor, his department aimed for an incoming class of around 8 PhD students, and they usually sent acceptances to around 12 applicants annually and had a short waiting list. But in order to get to the waiting list, at least 5 would have to decline the offer.
  3. UW is particularly strong for social science statistics and analysis of network/spatial data. It also has a few outstanding researchers in the machine learning (both theoretical foundations and methodology/applications).
  4. Stat PhD Now Postdoc

    Fall 2019 Statistics Applicant Thread

    Sorry to be a buzzkill but I would not count on getting off the waitlist at a reputable PhD program. It happens sometimes, but PhD adcoms always extend offers to more than their target incoming class (so for example, if Stanford is targeting an incoming class of 8-10 students, they will give offers to 12 applicants with the expectation that at least 2 will turn them down). At a bigger program where they expect 50-60% matriculation, it is likely that they have extended offers to 10 or more applicants than their target number.
  5. Stat PhD Now Postdoc

    Biostat & Computational Math & Statistics

    No, courses and grades do not matter one bit in hiring. In order of importance for faculty hiring for an R1 or R2 institution: 1) publication record, 2) recommendation letters, 3) teaching experience (not always necessary to have, especially not in Biostatistics). For hiring at liberal arts colleges and regional schools, that order will be flipped. I am just saying that JHU and UW Biostatistics resemble "traditional" statistics departments in that they expect their students to have a firm foundation in theoretical probability and mathematical statistics so that they can conduct research in theoretical statistics if they choose to. In contrast, lower ranked Biostatistics programs might not require things like measure theory, because their students are probably going to conduct research exclusively in more applied areas and target publications in journals like Biometrics, Statistics in Medicine, or BMC Bioinformatics, which don't need much (if any) theory to get published.
  6. Have you checked the professors who could act as potential advisors and their job placements? I'm not familiar with any renowned faculty from TAMU in the area of Markov chain/stochastic process theory, but UCLA has Qing Zhou.
  7. Stat PhD Now Postdoc

    Biostat & Computational Math & Statistics

    It seems like the top tier Biostatistics programs place more emphasis on theory. UW Biostat requires a semester of measure theory and JHU Biostatist requires a full year of measure-theoretic probability. One of my postdoc PI's got his PhD in Biostatistics from Hopkins and did his PhD thesis on large sample theory for boundary problems in multiple hypothesis testing. It also depends on what your research is, though. If you did genomics/statistical genetics, precision medicine, or something like that at UW or JHU, it might not position you as well for a faculty position in a Statistics department (as opposed to Biostat) than if you did something like causal inference or something less specifically related to public health/biology like high-dimensional machine learning.
  8. Stat PhD Now Postdoc

    Biostat & Computational Math & Statistics

    UW Biostatistics is very strong in both theory and applications. I've seen some faculty like Daniela Witten and Ali Shojaie consistently publishing in top statistics (not just biostat) journals like JASA, JRSS-B, and Biometrika. OSU has some strong faculty; it seems to be more of a Bayesian department and strong in particular areas like spatial statistics.
  9. Stat PhD Now Postdoc

    Biostat & Computational Math & Statistics

    That said, it probably is harder to go from Biostatistics to a Statistics department if your work was all applied and in stuff like clinical trials, statistical genetics, or medical imaging. These are very important areas of research, no doubt, but Ithese graduates will have a much easier time getting employed by a Biostat department than a Statistics department.
  10. Stat PhD Now Postdoc

    Biostat & Computational Math & Statistics

    Your publication record is ultimately what matters more than what your degree is in. For example, Veronika Rockova at University of Chicago and David Dunson at Duke University both have PhD's in Biostatistics, but they are now consistently publishing in top journals like JASA, Annals of Statistics, etc. So it doesn't really matter if your degree is in Biostatistics or Statistics as long as your work is good and well-respected. Most Biostatistics departments are happy to hire both Biostatistics and Statistics postdocs or faculty, but at least one paper in a venue like Biometrics, BMC Bioinformatics, or JASA Case Studies and Applications is needed in order to be a competitive job candidate. The very top-tier Biostatistics departments in the country may also want to see publications in Biometrika, JRSS-B, or JASA Theory and Methods (which tend to fall more on the theoretical side). Statistics departments will also hire those with Biostatistics PhDs, but some may only consider job candidates with Biostatistics PhD's if they have published one or two papers in a more theoretical journal like JASA Theory and Methods, Annals of Statistics, Annals of Probability, Biometrika. I think University of Washington Biostatistics PhD students and faculty have a very good track record of publishing in both 'applied' and theoretical venues, so there are a handful of UW Biostatistics PhD's who end up as faculty in Statistics departments. .
  11. Stat PhD Now Postdoc

    Perspectives: international applicant changing fields

    It would make sense for you to obtain a Masters in Statistics or Mathematics first in order to alleviate concerns about your mathematical ability and your lighter math background -- at the very least, you need to take real analysis and some upper division/graduate-level math classes to demonstrate that you can do proof-based mathematics. Assuming that you perform well in your Masters program, I imagine you have a very good shot at schools in the range of Ohio State, UIUC, and University of Florida. I think that those would be your best shot, though you might also be able to get into a school like University of Minnesota or Texas A&M. There are graduates from top universities in Latin America who get into top-tier PhD statistics programs in the USA (e.g. Jose Zubizarreta is from Chile and got his PhD at UPenn Wharton and is now a professor at Harvard, and Carlos Carvalho who got his PhD at Duke is from Brazil). So you finishing in the top 3% of your graduating class at the top university in Brazil will certainly work in your favor, and I think you can get into a respectable PhD program in the U.S.
  12. Stat PhD Now Postdoc

    Ph.D. Advisors with Many Students

    I typically met with him once a week, unless he was traveling (then once every 2-4 weeks). He traveled more during the summer and winter breaks when I was also traveling a bit though, so it's understandable that I met with him less during those times than during the fall and spring semesters. Once a week is pretty standard -- I don't think there is much value in meeting more than that, since less than a week doesn't give the advisee much time to make noticeable progress (even if the progress is as minor as rereading a paper and understanding the contents more clearly). If you are looking to submit some papers to computer science/stat conferences which have hard deadlines for paper submissions, then you might need to meet with the advisor more frequently right before the submission deadline.
  13. Stat PhD Now Postdoc

    Texas Stats PhD Programs

    UT Austin is a great place to be a Bayesian. TAMU also has a few very strong faculty in Bayesian statistics as well, including Johnson, Mallick, Bhattacharya, and Pati. These professors consistently publish in top journals and have placed PhD graduates in top postdocs like Harvard and Duke. Overall, I'd say that these are a few great schools you have to choose from. I personally love the city of Austin, so I would be biased towards UT Austin. But Rice and TAMU are great schools too.
  14. Stat PhD Now Postdoc

    Ph.D. Advisors with Many Students

    My PhD advisor didn't have an overwhelming number of students (my department was smaller when I was a student there) but he was a Distinguished Professor who traveled a lot, had a lot of collaborations, and sat on a lot of PhD committees (nearly every PhD committee in our department and the external committee member for a fair number of students in other departments, e.g. CS and Biostatistics). Nevertheless, I still completed my PhD in just under 4 years. If you want to work with a very busy/distinguished professor, my suggestion would be to make sure to be persistent about meeting with them and to make sure that *every* meeting you have with him/her is productive and moving you forward. I personally would type up a "progress report" in Latex before every single meeting I had with him outlining what papers I had read, what questions I had, and what my thoughts and ideas were -- even if I hadn't made any progress on my research topic or even if I had to completely abandon an unpromising research direction, I still wrote up what I had done for that week. We would then go through that every meeting and decide on the next steps. Once I began generating my own ideas more successfully, I would write summaries of them, write out the technical details thoroughly, and type up any theorems and proofs for him to review. When I began writing my own papers, I would email him revisions/new drafts at least four days before our scheduled meeting so he would have time to read the draft and give me feedback in our next meeting. In my experience, this helped a lot. Finally, don't be afraid to chat with the other PhD students of your advisor. Chances are that you will learn just as much or more from talking with them about research than from completing a problem set or trying to make sense of a very difficult paper. Sometimes you have a general, vague idea of what is going on, but you just need that "a-ha!" moment, and chatting with more senior students can give you exactly that to make things click.
  15. Yes, if you are very motivated and driven, you can teach yourself. That's what a PhD program and postdoctoral training are, for the most part... learning how to teach yourself new things. The coursework is useful for gaining a basic foundation, but after you're done with classes, it's all about teaching yourself (with guidance from your PhD advisor, of course).

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