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Stat PhD Now Postdoc

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Stat PhD Now Postdoc last won the day on November 29 2019

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About Stat PhD Now Postdoc

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    Statistics (Postdoc)

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  1. I have never heard of prospective grad students being able to negotiate a higher stipend, in any field. In any case, this is almost certainly not possible in Statistics. As @bayessays pointed out, the stipend is determined by the department and pretty much all students get paid the same (except for a few students who may be on a fellowship or some special scholarship, but these students do not get to negotiate amount for those either).
  2. Having been on both the primary (TT) and secondary (postdoc/VAP) academic job markets twice now, I will reiterate that if there is any chance that you are considering an academic job, you should definitely prioritize quality of life for your PhD, because there won't be as many opportunities to be geographically mobile afterwards. The academic job market in Statistics/Biostatistics is crowded and competitive enough these days that many PhD holders/postdocs from the Ivy League, Stanford, Berkeley, etc. are taking jobs at schools whose programs are ranked much lower than their alma mater/postdoc institution. If you can publish extremely prolifically during your PhD and postdoc *in the top journals* (a long publication list without -any- in top venues typically won't cut it either), you can certainly improve your chances of landing at a "dream" school in your most desired location. But that isn't guaranteed. I decided that the academic lifestyle suits me and that the job security and relative freedom in day-to-day job duties were worth sacrificing the geographical flexibility and taking a slightly lower salary than in industry. But I could see that this is (understandably) not the case for everyone.
  3. Agreed with the above that Brown Biostatistics is a great department. If you are interested in industry, Brown University's proximity to Boston is also a plus. Boston have something like a thousand biotech companies, ranging from start-ups to huge pharmaceutical companies. Brown is certainly good enough to land you academic jobs, provided you have a record of achievement. For academic jobs, the CV (namely, the publications) and the recommendation letters are truly the most important factor for landing interviews/jobs. This past application cycle, I saw some folks with PhDs from schools like University of Illinois at Chicago and University of Cincinnati (which are great schools but not "elite") getting interviews for TT jobs at R1's because they had strong CV's with a at least one or two publications in top journals. A strong pedigree is certainly helpful -- I won't deny the correlation between publication record/strong recommendations and institutional reputation. But it's not the *only* thing that matters.
  4. It would be a good idea to ask that during your visit days... though if there are any lurkers on this forum who currently attend those programs, maybe they can answer.
  5. Congratulations on your acceptances! Those are some fine places. I cannot comment so much on the cultures of the departments, but for what it's worth: Columbia has David Blei who works on Bayesian nonparametrics and computational statistics (mainly from an optimization-based approach, e.g. variational inference). His students and postdocs are quite successful at securing jobs in both academia and industry (e.g. Google, Apple, Deep Mind). The same is true of Michael Jordan at UC Berkeley who was Blei's PhD advisor. Students and postdocs of Jordan do extremely well in the job market. Harvard strikes me as a big MCMC department. It seems to me that in the machine learning community (rather than the "pure statistics" community), variational inference methods are currently of greater interest than MCMC. But there are also people working on scalable MCMC so that it can be more attractive to "big data" practitioners (possibly some at Harvard). I know that at Duke, there are also several people working on things like approximate MCMC and "embarrassingly parallel" MCMC (e.g. David Dunson). Duke also seems to have the shortest PhD completion time on average (most students seem to finish in four years), if that is an important factor to you. Overall, I would say that you should definitely consider things like geography and quality of life. If you choose to go the academic route, you may not have much of a choice in where you end up geographically, unless you are a superstar (and even then, it's not a "sure" thing). So the PhD may potentially be the only time that you have an enormous say in where exactly you want to go.
  6. Congratulations on your acceptances! University of Toronto and Carnegie Mellon are both excellent schools. The Department of Statistics & Data Science at CMU is particularly strong in the areas that you listed as interests -- not to mention its proximity with the CMU Machine Learning Department which is also world-class. So this would be an excellent choice for your particular interests. I am not as familiar with UofT, but for the field of Statistics, I don't think it really matters if you go to the same school for your PhD as your undergrad. I know several people who did this, and they are currently in great academic jobs right now (and this is including not just people who went to Stanford, Harvard, or Berkeley for both their undergrad and grad, but also folks who did both their Bachelor's and PhD's at schools like University of Florida). So this isn't really an issue, IMO. If you are interested in academia, you should aim to work with a PhD advisor who can help you publish/submit papers *before* graduation, so you can be competitive for postdocs and TT Assistant professorships. As for the immigration stuff, it should be noted that even in the current political atmosphere, there is no H1-B quota or cap for university workers in the U.S. So usually, American universities have a lot of free reign to hire people irrespective of their immigration status. And if you are reasonably productive in research and your teaching is adequate, then your job is very secure, and there should be very little difficulty transferring from H-1B status to green card. But this pertains mainly to faculty -- international PhD students typically can't become PR's as easily unless they marry an American citizen.
  7. It's definitely possible for somebody with a "non-traditional" background such as yours to transition into Biostatistics. But if you haven't taken math since high school, you would realistically need to take Calculus I-III and Linear Algebra before applying to graduate programs in Biostatistics. Those would be the bare minimum courses required to get into a Masters program. But it can be done. I've seen people with all sorts of undergrad degrees like sociology, biology, etc. pursue Masters in Biostatistics, but they did have to spend some time getting the math requirements done. Some of them were able to do it in less than a year (i.e., they took Calc I in the spring, then Calc II one summer term and Calc III and linear algebra in the second summer term). To get into a Biostatistics PhD program, you would also need to take Real Analysis (an upper division math class). If you decide that Biostatistics is really the career path you want to pursue, then you need to figure out a road map for fulfilling these course requirements. And you need to perform reasonably well in them (B's or higher).
  8. As it currently stands, I think it would be tough for you to be admitted to the PhD programs you listed in your original post, or a school like UMichigan, since your math background is not as strong as that of the strongest applicants from ISI (or students from more closely related majors like CSE from IIT). You have a solid GPA from a strong pedigree, but I'm not sure that is enough when compared with other international applicants -- it would be a different story if you had lots of experience with advanced math, in addition to your B. Tech from a prestigious school. Mathematical preparation/grades and letters of recommendation are the most important part of PhD admissions for Statistics (I'll assume that your GRE Q score would be more than adequate with your background). Unlike other fields, research experience tends to be downweighted for Stats PhD admissions, and there are plenty of successful applicants from pure mathematics backgrounds who have little to no research experience. I don't think online courses are a substitute for grades earned in regular classes. If you could obtain a Masters degree in Statistics first (including one from a institution as reputable as ISI), that would help your profile a lot. Even taking just one year of courses in a Masters program would make your profile much more competitive. I know that this would delay your PhD applications, but I think your chances would improve greatly if you were to enroll in a Masters program.
  9. Oh, I just noticed in the end of your second paragraph that you mentioned you've only taken the minimum math requirements. I think your best bet would be to take a few more advanced, proof-based courses and submit these grades with your application. I am not sure how feasible it would be to take these classes at a reputable institution in your home country and get grades on a transcript for them in time for the December application cycle. But if this is in the realm of possibility, I think that should be your absolute top priority rather than working on economics research or studying for the math subject GRE. Economics research is unlikely to factor heavily in the admissions decisions, and there will be a *lot* of international applicants who scored well on the math subject GRE, so I'm not sure how much that will be make your profile "stand out."
  10. How much mathematics have you taken beyond Calculus I-III and Linear Algebra? Have you taken any proof-intensive courses, namely Real Analysis? Based on your description, I will assume that you attended a school like IIT Bombay or IIT Delhi, which should be favorably viewed by admissions committees. Most adcoms will likely either have at least one member who is from India, or if not, they will be able to consult an Indian colleague in their department about your school. So I am sure that they will be aware of the grade deflation and the rigor of your institution. I think you could potentially have a shot at some Statistics programs in the top 30 if you have taken some advanced mathematics beyond the Calculus sequence and LA. However, you would be competing against some very strong applicants from Indian Statistical Institute (all of whom either have or are in the process of obtaining Masters in Statistics and have already taken classes like measure theory). So I would consider a school like University of Michigan to be a more appropriate "reach" school for your profile (I know of some people who attended the IIT's for engineering and then went to a school like UM for a PhD in Statistics).
  11. You have an excellent GPA in Statistics/IE from one of the best schools in South Korea. I hardly think you have "no chance at all." However, you may need to calibrate your expectations a bit (e.g. Berkeley) since admissions is extremely competitive for international students. But being a strong student from Yonsei certainly gives you a great chance at schools like NCSU, TAMU, Purdue, Penn State, etc. I would target larger programs at big public universities and apply to a few "reaches." I would just apply and see what happens. I mean... you miss 100% of the shots you don't take. Try to get very strong letters of recommendation.
  12. Students from the top universities in South Korea, including Yonsei, tend to do pretty well in Statistics PhD admissions. If you get A's in Real Analysis I & II (I would take two semesters of it if you're worried about the lack of proof-intensive classes), that will help your profile a lot. That said, it is extremely competitive to be admitted to UC Berkeley, Stanford, etc., especially for international students, and you will be competing with a lot of international students (including from Yonsei, SNU, KAIST, etc.) who have a lot more advanced math than you. In fact, if I recall correctly, when you submit your PhD application for the Statistics program at Berkeley, you need to submit a list of ALL the math classes you have taken, the grade you earned, the material that was covered, and the textbook(s) used. So yeah... these schools will tend to favor students with heavy math backgrounds, much moreso than those who have taken a lot of applied statistics classes. As far as top 30 schools, I think you would have a very good shot at being accepted at a large state school like Texas A&M, Purdue, or Penn State. I think this may be the target range for your profile, but you can also apply to several ranked higher than that for good measure.
  13. I think you should have an above average shot at NCSU and Duke. After all, you did go to a top liberal arts college and did pretty well in the math and stats classes you did take. Your profile is by no means a "shoe-in" at these places, but I think that if you can get excellent letters of recommendation, I wouldn't discount your chances at either of these schools. Your profile reminds me: there is an outstanding statistics researcher James Johndrow (now an Assistant Professor at Penn Wharton) who got his PhD in Statistical Science from Duke even though his undergrad degree was in Chemistry (also obtained from a top liberal arts college). And I also know of a young woman whose Bachelor's is in Psychology/Pre-med from Columbia but she also got her PhD in Statistics from NCSU (now a postdoc at Columbia). I think that Duke and NCSU are more open to accepting applicants from "lighter" math backgrounds and different majors than other places -- I know of at least a couple students/alumni from NCSU and Duke Statistics who did not have extensive math backgrounds. However, they did come from strong pedigrees, so your chances may be inversely proportional to how prestigious your undergrad is (that is, you can possibly get away with a lighter math background if your undergrad institution is very prestigious. But if you went to an unknown school, then you need to have very strong performance in math classes to be competitive). However, UPenn Wharton, CMU, and Columbia may be tough for you to break though without a lot more math, and I would recommend that you apply to a wide range of PhD programs, like the other posters have suggested. I also think you can definitely get into a top 10 Biostats PhD program, no question, if you think that this aligns better with your interests than traditional Statistics programs.
  14. I think that earning A's in the more advanced classes can mitigate the B's in Calc III and Linear Algebra. But you may want to take more advanced math classes to demonstrate that you can get *consistent* A's in advanced upper division math courses. Is there a proof-based upper division linear algebra class you can take? If so, you could try to ace that class and that would certainly help your application if you were to apply to math/statistics PhD programs. For math PhD programs, it is my impression that many of these programs want to see that you've taken a few PhD-level courses too (e.g. measure theory or topology).
  15. UCSD Mathematics has some great statistics faculty, particularly for high-dimensional statistics and ML. Many of them seem to be publishing in the top statistics journals and ML conferences, so you could do quite well there if you were to go to UCSD and be supervised by one of these professors. There are also a lot of great faculty in the UCSD Halıcıoğlu Data Science Institute whom you can work with as a PhD student, and that can also help set you up well for a career (academic or non-academic) later. I don't think your opportunities would be limited if you were to go there, to be honest. There are some excellent mathematics departments that house statistics within them, and I would count UCSD as one of them.
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