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Stat PhD Now Postdoc

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Stat PhD Now Postdoc last won the day on September 4

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  1. UPenn Wharton Department of Statistics also has some very strong probability faculty (Bhaswar Bhattacharya and Jian Ding). In particular, Bhattacharya at Penn Wharton is an expert on combinatorial probability, so that would be an excellent fit for your research interests. And Jian Ding and his PhD students regularly publish in top probability theory journals like "Annals of Probability" and "Probability Theory and Related Fields." Which Asian nation are you from, if you don't mind me asking? If you are from China, South Korea, or India, your profile is pretty strong and you stand a good chance. If you are from another country, competition will be tougher (only because there are already so many excellent applicants from the aforementioned countries). Other than that, there will probably be a few differences between Statistics and Math programs. First, most Stat programs will tend to be dominated by statisticians rather than probabilitists, though I'm sure there will be at least one other member in your PhD cohort who will share your interest in probability. Second, the coursework will be different, i.e. you wouldn't need to take classes in topology, abstract algebra, etc. in a Statistics department like you would in a Math department. However, you would still need to take a few proof-intensive, heavily theoretical classes in a Stat department like measure-theoretic probability theory, theory of linear models, advanced statistical inference/decision theory, etc. As far as getting postdocs in the field of probability (many of which will be in math departments), this shouldn't be any problem for someone coming from a Statistics Department as long as they have great recommendation letters from well-regarded professors and as long as they have one article in a top journal like Annals of Probability or Annals of Applied Probability. As far as I'm concerned, as long as there are at least some probability theory faculty in a Statistics department, you could do just as well there as you would in a math department. Most of the stuff you do for your dissertation research is stuff you have to teach yourself on your own anyway, not things from classes (though the classes will give you a basic foundation for more advanced self-study).
  2. You are right to be concerned if you are an international student -- though this may also depend on which region you are from. If you are from Asia or Europe, it may be tougher because you will be competing against those with degrees from the likes of Peking, Fudan, USTC, ISI, Oxbridge, ICL, etc., and these schools are *known* for intense rigor and producing outstanding PhD STEM students. It might be less tough if you are from Africa or Latin America. I think it is worth applying to schools ranked 20-40 with some lower ranked schools, and to apply for several Masters programs to be on the safe side. With strong performance in a Masters program from an R1, you can likely get into schools like University of Florida and Ohio State for your PhD -- and possibly schools even at the level of University of Minnesota. I know some international students with BS/BA's from less prestigious schools in China/India who obtained Masters degrees from schools like Rutgers and then went on Statistics PhD programs at UMN, UF, etc.
  3. Your coursework is more than sufficient for admissions to Statistics programs. A potential concern may be about the rigor of the classes at your institution. Since you're not coming from a highly ranked institution, your letters of recommendation will matter a great deal. I would try to get the best possible ones that you can (i.e. they should come from professors who can say you're one of the top 5% of students they've ever taught and that you have strong research potential, etc.). Taking the math subject GRE may not be a bad idea either in your case -- you can always just not report it if the score isn't ideal.
  4. Check out Philip Guo's blog post about how CS admissions typically works: http://www.pgbovine.net/PhD-application-tips.htm
  5. @bayessays also makes a good point here. OP: if those are your interests, then a computer science department would probably serve your needs better. CS PhD admissions is also quite different in that they are much more forgiving about lower GPAs, and admissions is based heavily on research experience. Your research experience seems solid. If you are interested in topics like deep learning and computer vision, I would check out the sub-forum for Computer Science. If you are able to continue your current research and get your name as a middle author on a conference paper, then you might stand a fairly good shot for some decent CS programs. You could also do a Masters in Computer Science, get some research experience there (by reaching out to a PI and asking to work for them -- some PI might be amenable to this, since you have research experience in computer vision, reinforcement learning, etc.), and then transfer to the PhD program. At my alma mater, a lot of the CS doctoral students I knew started out this way -- they started out in the (terminal) MS program, and while they were completing the coursework, they also worked/volunteered in a PI's lab, and then they transferred into the Computer Science PhD program directly. And some of these PhD students didn't even have a CS background, their undergrad major was in unrelated subjects like Civil Engineering. Just a thought.
  6. I am not extremely familiar with finance/financial engineering grad programs, so I would suggest you post your profile on the Finance and Financial Engineering subforum: https://forum.thegradcafe.com/forum/67-finance-and-financial-engineering/ Maybe someone there can give you better feedback on your chances and what you can do to improve your profile.
  7. Both your undergrad and grad GPAs seem to be a little bit on the lower end (graduate school grades tend to be inflated so a 3.7 is a little bit on the lower end for a grad GPA), and the fact that your undergrad is an unknown public school will be also be a ding against you. Second, you could also stand to improve your GRE Quantitative score at least a few point, so I would definitely recommend retaking it to improve your score at least a few points. Finally, you are an international student, so unfortunately, I am not sure how attainable it would be to get admitted to any of the programs ranked in the top 60 schools of the USWNR rankings. But also, if you are interested in financial mathematics, I am not sure if you will find many statistics faculty who are specifically interested in this. I can only think of a few Statistics programs like UC Santa Barbara and Rice University that have faculty working on financial mathematics -- and unfortunately, I am not sure that your profile is competitive for these schools. Are you sure that a Statistics PhD program is the most appropriate choice for your interests? If you are insistent on getting a PhD in Stats, I would look at schools starting around the level of University of Missouri-Columbia and work your way down. There are some good schools in this tier that may have some strong faculty with interests that match yours.
  8. OP: I'm afraid that bayessays is right that your current profile is just not competitive for any of the PhD programs ranked at the level or UMass Amherst or higher (including GWU). I'm not sure about University of Utah or Baylor. You certainly need to raise your GRE Quantitative score. If you are insistent on getting a PhD in Statistics, your best bet is to first obtain a Masters in Statistics or Mathematics from some decently reputable university (not a Masters in Applied Statistics, Analytics, or Data Science but mathematical statistics or math with a stats concentration), perform extremely well there, and then to apply to PhD schools mostly at the level of Michigan State through Kansas State, with some schools like Ohio State, UConn, or University of Florida in the mix. Even with a Masters degree, Minnesota is probably a reach (but you can certainly try), and I wouldn't bother applying to Duke, Michigan, or Washington, as there is not really a realistic path for you to be admitted to these places.
  9. That should help your application. Your profile is strong enough that you probably would have been admitted to at least one of (likely several of) Harvard, Berkeley, UPenn Wharton, Columbia, Duke, University of Washington, etc. regardless. But I think this will definitely help your application. I assume these government scholarships are very competitive to get, and it looks great on your application.
  10. This is program-specific. Not all schools that reject applicants for the PhD program automatically consider them for admissions to a Masters programs.
  11. Nearly all of the international PhD students at my program (which is well-regarded but by no means considered an "elite" school) ended up staying in the U.S.A. and going to work in industry or doing a postdoc after graduation. My program was over 70% international students, and the ones that went into industry post-PhD had no difficulty getting jobs at good companies like Amazon, Google, Wells Fargo, JPMorgan, etc. For industry, it doesn't matter that much where your PhD is from, as long as it is from a school with some name recognition (which would include most of the flagship state schools in the country and schools like Northwestern). It is quite difficult for international students to get these jobs without a PhD in a STEM discipline (whereas domestic students can often get these jobs with only a Masters or a Bachelor's), but with a STEM PhD from *any* decently reputable program, it is significantly easier for them. For academia, it is a little bit harder to move up in the ranks, but not impossible if you publish in good journals/conferences, work with good postdoc and PhD advisors, and network with the top people in your field (it is highly advantageous to have a famous professor be familiar with your work and write you a letter of recommendation). But still, most people should not expect to land a job at an "elite" program -- even the majority of PhD graduates from top schools like Stanford, Berkeley, Harvard, etc., will end up working as professors at large state schools or small liberal arts colleges if they choose to stay in academia. There are only a finite number of jobs at "top" programs, so the chances of ending up as a professor at one of the elite programs tend to be minuscule for most people, unless you are a true rock star.
  12. Your profile looks pretty good. Your math background might be a bit "light" compared to other applicants who have degrees in mathematics, but you did go to an Ivy League school and have done well in proof-intensive courses like abstract algebra and real analysis. So there shouldn't be much worry about your ability to complete courses in Casella-Berger statistical inference, probability theory, or large sample theory. I think these factors will work in your favor. If you're concerned about this, then maybe ask your math professor LOR writer to explicitly highlight the fact that you got A's in Real Analysis and Abstract Algebra and that you have strong math skills. However, your list of schools is indeed very top-heavy, and some are extremely difficult to get into (Princeton OFRE, for example). I think you should have a decent shot at UW Statistic, CMU Statistics, and Cornell ORIE. I would recommend adding a few schools like NCSU, University of Michigan, or UNC Chapel Hill, which I think you would have a decent shot at (though I am not sure how these programs are perceived in China, but they have very good reputations in the U.S.).
  13. An emerging area in the MCMC literature right now is approximate MCMC, where you replace the Markov transition kernel with a low-rank approximation so that it is faster than vanilla Gibbs sampling/MH algorithms. James Johndrow at UPenn Wharton works a lot on this area, and you can check out some of his papers. In addition, I have seen Bayesian coresets work being done, where you approximate the full data set with a much smaller, weighted random subsample at each iteration (so you can run MCMC faster on the weighted subsample than the full data set): https://arxiv.org/abs/1605.06423 I think MCMC and its related theory is still an active research area, but it is a bit more difficult to publish papers on it unless it is truly state-of-the-art (for application or theory). So papers that simply verify geometric ergodicity for a model using the "traditional" drift and minorization methods may not fly well for the top journals. But if you work on something very state-of-the-art, it should be fine. The guy I linked to above, Qian Qin at University of Minnesota (a PhD alum of University of Florida) has initiated several new tools for theoretically analyzing MCMC which were not previously considered (e.g. using Wasserstein-based methods). I think there will be a lot of interest in MCMC in the future, as long as it can assert its relevance to "big data" through things like approximate MCMC, weighted subsampling schemes, etc.
  14. If you are more interested in methodological/applications research (especially applications in public health, genomics, etc.), then I would suggest applying to more Biostatistics programs than Statistics. You can also work on applied stuff in Statistics departments, but I would say in general, Stat programs are a bit more focused on statistical theory than Biostat (outside of the tip-top Biostat programs).
  15. I would have to agree with bayessays. I think even with a MS, your chances at the Statistics PhD programs on your list are not very good (not sure about Biostat -- maybe your chances there are better, since they do seem to highly value methodological/applied publications more than Stat). These programs are super difficult to get into -- especially UPenn Wharton which matriculates only 4-6 students every year, and only one or two of those will be domestic students. I don't see schools like Harvard or Penn taking a chance on someone with your GPA. I would apply much more broadly than only the top-tier programs. With a strong performance in a Masters program (if you go that route), your chances are probably pretty good at schools like UFlorida, Ohio State, etc.
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