Stat Assistant Professor

I based my assessment on the rankings, the fact that it is a slightly larger program, and the fact that some of the alumni I'm familiar with from TAMU are also from the top universities in South Korea (Yonsei, etc.). I don't think the fact that your degree is not in Stat is really a problem, as long as your mathematical background is strong -- there are definitely PhD students in Statistics (including international students) who come from an Econ background. But it may help to have your letter writers stress that you took a ton of advanced mathematics like probability theory, measure theory, topology, etc. You can ask them to mention this.

You may want to have one of your LOR writers point out that you retook measure theory in your home institution and got an A and explain the P, as their words carry more weight. This should also be mentioned in your SOP (though I wouldn't say it was because you were "mathematically immature," but put it in a more positive light, like how you were determined to prove to yourself that you could do well in measure theory and were successful to that end). Also, make sure that your letters of recommendation are *very* strong. These make a big difference in getting your application into the "Accept" pile. Your profile looks pretty good, in my opinion. Usually international students from the top 3 schools in South Korea do pretty well in PhD admissions, but the top schools will of course have very, very fierce competition so it helps to apply more broadly. Your list of schools looks very reasonable. You would probably also have a good shot at schools like Texas A&M and other schools of that caliber.

It is hard to gouge your chances at the schools in the 25-50 range of USNWR rankings (I think your chances based on your current profile may be slim for any higher ranked programs), partly because admissions are very competitive for international students. That being said, you definitely need to improve your quantitative score on your GRE in order to have a chance. The B's you received in Analysis and undergrad probability are also potentially concerning. I would recommend applying to mainly Masters programs in Statistics (not Applied Statistics, Data Science, or Data Analytics programs -- make sure that the progrms you apply to explicitly require a year-long mathematical statistics sequence taught at the level of Casella & Berger, as well as a course on the theory of linear models) and using that as stepping stone for PhD applications. It may be worthwhile to also retake Analysis in your MS program to get an A or A-. Do well in a MS program, and you can likely get into some program in the range of UMinnesota to Temple U for your PhD.

Agreed with the others that you should do a bit more research. For example, University of Florida does not have an official Masters program in Statistics, only a PhD program with a Master of Statistics "along the way" (some students exit with only the Masters). OP: Being an international student, if your goal is to ultimately work in the U.S.A., the PhD is your best bet. Even if you plan to go work in industry as a data scientist, it is currently very hard for international students to get these jobs in the U.S. without a PhD in a quantitative field (doesn't have to be Statistics, it could also be math, computer science, physics, industrial engineering, etc.). American citizens and Green Card holders can usually get such jobs easily with only a Masters (and sometimes only a Bachelor's), but the bar is higher for noncitizens. An MS in Analytics may not cut it for an international student to get a job in the U.S, nor would it help for PhD admissions down the road. I would apply to mainly PhD programs if I were you, and Masters programs in traditional Statistics which could make you more competitive for PhD admissions at mid-tier programs if you perform well.

Berkeley and UMich may be difficult to crack, especially since you are an international student (there is much more competition). If you want to try a "reach" school, I would recommend NCSU or UWisc. The others on your list seem reasonable targets, provided that you do well in Real Analysis. I would add more schools ranked at or below the level of UCLA to your list.

I don't think it matters much where you take these classes -- doing them at a Cal State should be fine. You just need a new transcript that shows you performing well in these courses (Calc I-III, Linear Algebra, and undergrad Probability at the very least). Your current transcript will probably keep you out of traditional Statistics MS programs (not the applied stat or data analytics MS programs), but if you have this new transcript, you can explain that you retook the classes and did well the second time, etc. You don't typically need Real Analysis for Statistics MS admissions, but you do need to have real analysis on your transcript if you are aiming for an Econ or Stat PhD eventually.

For your profile, I'd say the "sweet spot" is probably the general USNWR rankings of Iowa State through UCLA. Since admissions is very competitive for international students, it may also be a good idea to apply to a few schools like UConn, MSU, and maybe Pitt. These mid-tier but still solid schools are more likely to accept international students who aren't from the most elite schools in their respective region (in your case, Asia) -- some will even admit students from obscure undergrads as long as they are completing a Masters degree from an R1 in the U.S.

Princeton ORFE and MIT Applied Math are very difficult to get into but I wouldn't rule these as completely out of the question since you finished First Class with a math degree, have a MS from a top 30 school in the world, and scored exceptionally well on the Math GRE. I think you will definitely be in the discussion for all Statistics PhD programs though, and you may be able to get into one of the very top depts like Berkeley, Stanford, or Harvard.

Your profile looks very strong, and with a BS from ICL and a MS from Toronto (both excellent schools) and an excellent math subject GRE score, you should be able to get into some of your "reach" schools and most likely all of the "match" and "safety" ones as well (fyi, I wouldn't consider Duke, CMU, or Washington a "reach" at all for your profile though). I think you have a good shot at UPenn Wharton and Chicago, and you would be very much in the discussion at Harvard, UC Berkeley, and Stanford as well if you were to apply to them.

Good to know. I figured that even unranked MS programs in (traditional) Statistics would have this kind of threshold for admissions.

The OP might be able to get into Applied Statistics or Data Analystics MS programs as it is, but these are unlikely to help her (for either PhD admissions later or an industry job when she already has relevant work experience). I think it will be a lot harder for "traditional" Statistics MS programs. OP: I would suggest you retake Calculus at the very least. Otherwise I think a lot of MS programs will automatically reject just based on the grades in the prerequisite classes. If your grades in upper division classes were better, you may have been able to convince some to overlook this, but your grades in undergrad Prob/Stat were not good either...

Which international university did you attend, and what was your class rank (roughly)? If your undergrad was not Peking, Tsinghua, USTC, Fudan, ISI, KU, SNU, or KAIST, then you might have difficulty being admitted into Stanford, Columbia, or Cornell. The other programs you listed seem very reasonable, however (UNC STOR might be a "reach" school, but I could see you getting into at least one of the others). You probably don't need to apply to Oklahoma State or Utah State, but I would apply to Michigan State if I were you -- there are a few strong faculty in probability theory there, and it seems like a school you have a reasonable chance at (irrespective of what your undergrad's prestige is, a Masters in math with a 4.0 from an R1 would be seen as a positive for schools in this general tier).

Unfortunately, I think you will struggle to be admitted to even Masters programs in Statistics. MS admissions do not take into account things like "research potential," and letters of recommendation aren't weighed nearly as heavily (that is, mediocre LORs should not preclude an applicant from being admitted if the rest of the application is strong). MS admissions are pretty heavily based on (math) grades and test scores. And the B's and C's you received in Calculus, linear algebra, and undergrad probability/statistics classes are big red flags. If you are insistent to get admitted to a MS program in Statistics, I would strongly recommend that you retake classes like Calculus and linear algebra (and maybe Prob/Stat as well, since you received C's in them) at a local university to demonstrate that you have matured and that you can get A's in them. You can then secure letters of recommendation from these new professors. In your statement of purpose, you could also highlight your improved performance and explain why the adcom should overlook your earlier performance. As an analogue, I have a friend who was rejected from all medical schools for two rounds because his grades in pre-med classes were not good. But he retook these prerequisite classes at a local university, did well, and was admitted finally the third time. In your case, you need to demonstrate that you can succeed in coursework in a graduate program, and your current profile doesn't inspire much confidence that you will be able to do that.

For academia, most departments at R1's (with the possible exception of the very top ones) don't care so much about where you got your PhD, they care mainly about your ability to do high-impact work. SLAC's will care about your teaching as well. That being said, there is certainly a correlation between doing a PhD or a postdoc at a higher ranked department and getting high-impact work done -- higher-ranked departments do tend to have more productive and famous faculty, as another poster above mentioned. And more productive/famous professors means you have better chances of getting good postdocs or publishing in good journals. It's still possible to get a good academic job with a PhD from a mid- or lower-ranked PhD program (one of my cohort got an Assistant Professor job at University of Minnesota with no postdoc, and my PhD advisor has former students working as professors at TAMU, Duke, etc.), but it might be a little bit harder. But if you do good work and publish in good journals and ML conferences, your chances in academia should be good, irrespective of your PhD alma mater.

Grades in graduate school tend to be a bit inflated, so anything sub-3.7 in a Masters program would probably give pause (C's are rarely given in grad school and basically are equivalent to D/F's in undergrad, while B's in grad school are the equivalent of C's in undergrad). The MS GPA shouldn't matter much at all though for industry and would only be important if you plan to apply to PhD programs later.

Among international applicants, it seems as though the majority of the competitive applicants have taken at least measure theory. If you went to a top school like Oxbridge, Peking, Tsinghua, etc. and majored in math/stat, it probably isn't unusual for you to have taken graduate-level (in the U.S.) real analysis as an undergrad. I'm not sure about stochastic differential equations, though -- probably the students who are especially interested in quantitative finance have taken a class on stochastic calculus. Among domestic applicants, it is rarer for applicants to PhD programs in Stat or Biostat to have taken these courses. But in my opinion, it is not that important to know measure theory before entering grad school in Stat (or math), as they'll teach you what you need to know during the coursework phase of the program.

With a degree from MIT/UChicago/Caltech, you should have no difficulty getting into any Statistics MS program with your record. These schools are known for both rigor and grade deflation, so you would probably also be a competitive candidate for PhD programs down the road. Ace your Masters program, and I anticipate you will have good luck with PhD programs as well..

Agreed with bayessays and Geococcyx. Stanford, Chicago, Harvard, Columbia, Penn, etc. are very difficult to get into and have a strong preference for applicants who have taken a lot of advanced mathematics -- many of the students they accept will have already taken the Masters-level mathematical statistics sequence (based on Casella & Berger), as well as advanced math classes like graduate measure theory or graduate-level probability theory. I think the best range of schools for your profile would probably be Texas A&M to Purdue, and you should have a very good shot at schools like UIllinois and Rutgers. If you are interested in academia, I note that there are PhD graduates from these aforementioned institutions who end up at postdocs at places like Harvard, Penn, Columbia, Duke, etc. So if you work with a good PhD advisor and excel at one of those programs, you should be able to land a good postdoc.

Your math background is a bit too light for PhD programs, but you could most likely get into an MS program with your current profile. What are your ultimate career goals? Academia, industry, etc.? If the type of work you see yourself doing requires only a Masters, then I'd just apply directly to Masters programs. For PhD admissions to top PhD programs in Biostat (and even lower ranked ones), evidence of mathematical ability is very important. You certainly need to take more math, including at the very minimum an intro to proofs class and at least one semester of real analysis. Taking undergrad Calculus-based probability and theoretical statistics, as well as proof-based linear algebra, may also be helpful (and doing well in an upper division Linear Algebra class would also mitigate the B you got in the lower-division LA class).

You don't need Real Analysis for Masters programs. People get accepted to MS programs in Stat/Biostat with only Calc I-III and lower-division Linear Algebra and no other math. If you want to get into PhD programs, then more math does help. If you did well in Abstract Algebra and you also mention in your statement of purpose that you are currently taking Analysis (and hopefully expecting to get an A), I think you should be fine. If it concerns you, you can ask one of your LoR writers to mention this as well, as those will carry more weight than the SoP (you should have your LOR writers make the case about your mathematical abilities anyhow). From what I gather, applications directly to Masters programs are considered separately from PhD applications. However, in some cases, the PhD admissions committee may decline an person's application to the PhD program but admit them to their Masters or ask them if they are interested in their Masters program. MS programs are not usually funded, so there doesn't tend to be a target number for incoming matriculations for Masters programs (as opposed to PhD programs).

Your GRE scores are fine. It is just that with admissions being so competitive for international students, your best bet is probably at schools in the general tier of University of Florida and Rutgers for Statistics and similarly ranked schools in USNWR for Biostatistics (of course for "safety" sake, you may want to apply broadly and at lower ranked schools too). UNC Biostat and NCSU Statistics will probably be very hard to crack but it doesn't hurt to try a few reaches.

In the PhD applications, there should be separate fields to put your overall GPA and your major GPA. You said you completed a math degree, so you can put that for your major GPA, and in your statement of purpose, you can further explain why the medicine GPA is lower. You will also want to make a clear case why you are switching to Biostatistics from Medicine and emphasize your mathematics preparation and research experience in your statement of purpose (e.g. summarizing briefly the first-author paper you are about to submit). I think with a math degree from one of the top 4 universities in South Korea (and a stellar GPA at that) and a first-author publication, you will be in very good shape to get into a good Biostatistics PhD program.

Your math background already looks quite strong for Biostatistics PhD programs and is already much more exhaustive than many Biostat PhD applicants -- the majority of Biostat PhD applicants have not written first author papers or taken graduate real analysis, stochastic processes, or algebraic topology. You would make a great applicant for Statistics PhD programs as well, if you are interested in applying to those. Where did you complete your math degree? Is it at one of the top institutions in your home country (i.e. KAIST, Yonsei, KU, or Seoul National)? If so, I would advise you to apply to all the top Biostatistics PhD programs in the U.S. (and some Statistics programs too if you're interested in that). Admissions is very competitive for international applicants, but if you completed your degree at any of the top four universities in South Korea, you stand a very good chance.

Your profile looks quite strong. I wouldn't worry too much about the B/B+'s in Algebra or Fourier Analysis -- a deep knowledge of these subjects is not needed for most statistics research, and you got A's in the Casella & Berger Masters level statistics sequence and an A- in measure theoretic probability as an undergrad (which is nothing to sneeze at). Your undergrad institution will also help a lot. The B's might keep you out of Stanford but you should be in the discussion by the adcoms at most other schools. I would try to ensure that your letters of recommendation are not simply "standard," but strong too. If you can secure great LORs, I anticipate that you will be able to get into programs like Duke, NCSU, and University of Washington. But you could add a few lower ranked schools for good measure, and I would also recommend trying your luck at a few schools like UC Berkeley, Harvard, and UPenn Wharton.

Agreed that UNC-STOR seems like a better fit than NCSU and UCLA based on your research interests. I think your chances at University of Washington are also decent, and that program would be a very good fit for you as well. There are other schools that have some faculty working in your stated areas of interest (e.g. Tienfeng Jiang at University of Minnesota and Garvesh Raskutti at University of Wisconsin), and I think you would be a competitive candidate at UMN and UWisc.