Stat Assistant Professor

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.

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.

Check out Philip Guo's blog post about how CS admissions typically works: http://www.pgbovine.net/PhD-application-tips.htm

@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.

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.

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.

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.

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.

This is program-specific. Not all schools that reject applicants for the PhD program automatically consider them for admissions to a Masters programs.

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.

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.).

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.

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).

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.

Unfortunately, I think your GPA is too low for the PhD programs on your list, even with the grade deflation/unusual grading scale that you mentioned. It may be somewhat forgivable if your math GPA was much better than your overall GPA or if you attended a school known for its grade deflation (e.g. UChicago or Reed College), but I have a hard time seeing you getting admitted into any of those schools. Your list is far too top-heavy and I would advise you to apply more widely.

Is there any particular reason why you do not want to apply more broadly than 9 top schools and 2 "safety" schools? Admissions is still competitive for international students at the level of UF and UIUC. It may help to apply more broadly to a range of schools in the USNWR top 40 rankings to improve your chances of being admitted to a good program. You don't need to go to a "top-tier" program to land a good job post-graduation (though it certainly helps). For industry, the reputation of the school is not so important as long as the doctorate is not from an obscure regional/directional program. And in order to get good postdocs and faculty jobs, your publication record and the reputation of your PhD/postdoc advisors (and other recommendation letter writers) are what matter the most.

That is a respectable score, but I would caution that many other international students will have higher math subject GRE scores, even at schools like UF and UIUC. A lot of the Chinese students at UF and UIUC in particular will have very high math subject GRE scores (95+ percentile), and your application will be compared to theirs more than it will to domestic applicants. I would keep that in mind. As these schools do not require submitting the subject GRE score, it might not be worth submitting it.

I think you have got a pretty good shot at UF and UIUC. UF is pretty renowned for MCMC and has great placements. One guy from UFlorida got a faculty job at University of Minnesota this past year (a well-regarded program) without any postdoc, because he worked on cutting edge stuff on MCMC: https://cla.umn.edu/about/directory/profile/qqin The job placements at UF and UIUC in general are also pretty good if you are interested in faculty jobs. You'll see PhD alumni from these schools in postdocs at places like Columbia, UPenn, and Carnegie Mellon, and there are professors with PhDs from these schools at places like Duke Statistical Science (https://resteorts.github.io/), Harvard Biostatistics (https://www.hsph.harvard.edu/brent-coull/), and UT-Austin Statistics & Data Science (https://cns.utexas.edu/component/cobalt/item/19-statistics/4025-linero-antonio?Itemid=349).

I think if you are not a Canadian PR/citizen, it may be especially difficult to be admitted to a Canadian program, though it is not impossible (for example, Yves Athade has a PhD from University of Montreal). Your chances are far better at the general tier of schools that I listed in your original post -- that is, solid mid-tier programs that will look favorably at strong performance in a math Masters program from an R1 in the U.S.A.

With your article submission to a respectable journal (Statistics in Medicine), that excellent subject test score, and a strong GPA from a top university in Canada, I would definitely recommend applying to Stanford and some other top-tier schools. Competition is very stiff for international students, but I would rate your chances as above average. If your interests are mainly in causal inference, I would suggest trying UPenn Wharton and Harvard.

No, I don't think it is worth retaking the GRE since you got a perfect score on the Q section. If you did very well on the Math Subject GRE test, then you should submit it with your application. This may or may not help, depending on how much weight the programs put on it (some very good programs don't pay any attention to it all), but it certainly won't hurt your application.

If you have a strong publication record and strong letters, this should not be an issue. For example, Yves Atchades (a professor at University of Michigan for awhile before recently moving to BU) has a PhD from University of Montreal. You may need to do a postdoc, but even the majority of graduates from the top programs in the U.S. (who pursue academic careers) end up doing postdocs these days.

I think your list of schools sounds very reasonable, and I think you should be able to get into some of those Biostat programs, particularly the lower-ranked ones who will highly value your research and the fact that you submitted three methodological papers to reasonably good journals. I think the B+ you received in Linear Models I is partly offset by your A in Linear Models II. It may be worth applying to UW Biostat for good measure.

Re: your chances at top-tier programs. Your profile looks quite strong, and I think you have a definite chance at the top Stats programs in the U.S. If you went to a top university in Australia like Australian National University, University of Sydney, or University of Melbourne, you should be fine. These schools are regularly ranked among the top 50 universities in the world, and they are also known for rigor in their mathematics programs. I have seen Australians from the aforementioned universities graduate from the likes of Stanford Statistics and UPenn Wharton Statistics (e.g. Giles Hooker). For someone like you, I would recommend applying to most/all of the top programs in the U.S., including UC Berkeley, UChicago, Columbia, Washington, etc. and a few large state schools like Texas A&M, Minnesota, and Purdue for good measure. I think you should definitely be able to get into University of Washington, and you have an excellent shot at a place like Berkeley as well. Re: letters of recommendation. You should aim to get the strongest possible letters of recommendation from people who can speak to your research potential. You do not need to have published any papers to have a chance at Statistics or Math PhD programs (that's what the PhD program is to help train you to do!), but you need to display evidence of research potential in your application. I think you have pretty good evidence of that. Your supervisor who is the editor of an academic journal would be fine as an LOR writer, as would the supervisor in applied health research. Ideally they would mention in their LORs that they work in a research capacity and that they conduct research that is published in academic journals. At least one letter should also point to your excellent performance in advanced mathematics and your strong mathematical aptitude. Re: subject GRE. You will need to take the math subject GRE if you want to have a chance at Stanford. Otherwise, you could probably do without it. Re: your research interests. Off the top of my head, University of Washington is strong for TDA, and Carnegie Mellon is strong for both TDA and differential privacy. I would say that for your specific interests, UW and CMU are two of the strongest programs in those areas among the top-tier programs in the U.S. (though it's possible that other top-tier programs are also particularly strong in these two areas, I just may not be aware of it).

Yes, PhD admissions in Statistics will prioritize performance in advanced undergraduate math classes, and the PhD programs themselves are more mathematical n nature (at least for the coursework portion in the first two years, which is very much of the "theorem/proof" variety). Masters admissions is in general not competitive -- as long as you have decent grades in Calculus I-III and Linear Algebra and a decent GRE Q score, you should be able to get into some program. I'm not sure if your undergrad degree is that important for CS PhD admissions. I have seen people with biology and linguistics degrees get admitted to PhD programs in computer science, provided they have relevant research experience that aligns with their PhD advisor's lab. It seems like you do seem to have relevant research experience, having published in a paper in a conference that was in the top 10% and having submitted a paper to another conference. I would do research on professors in Computer Science departments who match your research interests and reach out to them to see if they are accepting PhD students. Admissions decisions in CS are often made in accordance with lab PI's who accept new students into their labs. Read this article from Philip Guo on how admissions decisions are made in Computer Science, and note the sentences: "the property that best separates good from bad applications is research density," and "GPA: warning sign if too low, but usually don't care. It's rare that someone with strong research credentials has a dangerously low GPA, and even if that were the case, I wouldn't care much." http://www.pgbovine.net/PhD-application-tips.htm