Stat Assistant Professor

Your profile looks very strong. However, I would strongly recommend that you take the General GRE test (not the Math Subject test), only because many of the schools on that list require the GRE. You don't want your application to be discarded just because the department requires the GRE General test but you didn't submit GRE scores. The GRE acts as more of a filter in graduate admissions, so as long as you score above 163ish on the Quantitative section, 150ish on the Verbal section, and 3ish on the Writing section, you should be in good shape. A little bit of prep/review is probably all you need to ensure you get those scores.

You could ask the Graduate Coordinator at Princeton OFRE to see what they think. But I suspect that your MS grades may be a bit low for the Princeton OFRE PhD program. You might be able to get into other lower-ranked PhD programs in FE though, given that your MS is from a very prestigious university. However, an important question you should ask yourself is: Why do you want to get a PhD in ORFE? Is it really necessary to advance your career in quantitative finance? Are you trying to switch gears from trading to being a senior quantiative researcher? Even if that's the case, doing a PhD seems like a long road just for that...

Since the OP mentioned having a few B/C's in the upper level math classes, that's why I 'guessed' that UCI and BU might be slight reaches. However, those two universities seem like more plausible targets for the OP's profile (unlike Stanford, UC Berkeley, Columbia, and USC, which I doubt -- USC because they accept like less than 5 PhD students every year with 2 or 3 matriculating). In addition, if the lower grades are only one off-semester and not a continuous pattern, then it can be explained very briefly in the application. Again, the way that it's explained matters -- you need to "pivot" quickly to explaining that after you overcame the family issues, every other semester was a 4.0.

You can apply to as many schools as your budget allows, but I would not expect amazing results at the schools you listed with only a Bachelor's. This is because you're competing against a lot of applicants from the top universities in China, India, South Korea (plus a few from Canada, Australia, the U.K., and some other European countries), and these applicants often have taken a ton of advanced math classes on their transcript like measure theory, functional analysis, probability theory, etc... and many of them also have Masters degrees (e.g. the applicants from ISI and SNU typically have them), as well as research experience and papers that they published in or submitted to journals. You might have a slightly better chance if you got a Masters from a very reputable program first (like U. Chicago, U. of Washington, Duke, Stanford, etc.). Even with an MS, I would probably not apply to Stanford, Columbia, or UC Berkeley, since the chances are slim, IMO. You could try UCLA, UC Irvine, and Boston U. though. If you are going to apply mainly to PhD programs (without doing an MS first), then I would focus mostly on schools at the level of UCI and lower. If you are less picky geographically, you can also improve your chances of getting into some PhD program with just a BS -- it just may not be in a large city like you desire. Another option is also to apply to statistics PhD programs that are in Math/Applied Math departments like SUNY Stony Brook Applied Mathematics & Statistics. I would explain the grades in the third semester briefly but not dwell on it too much -- it's better to just briefly mention what happened and then immediately explain how you overcame the issue and went on to earn a perfect 4.0 every semester since then.

I am afraid that those are all reach schools. University of Southern California Department of Data Sciences and Operations is in the business school, btw, so they accept very, veru few PhD students each year. The competition among international students (even those with degrees in the U.S.) is extremely stiff, and your profile may not be competitive for those particular programs when compared with the top applicants from Tsinghua U., Peking U., SNU, ISI, among 20 or so other schools. A lot of the top applicants from these universities not only have meticulous grades but also substantive research experience including co-authorship on papers that have been submitted to reputable journals. International students who earned a degree in the U.S. are usually more competitive if they have first earned a Masters degree from a reputable program in the U.S. (e.g. University of Chicago) or if they earned their degree from an elite American undergrad institution. So if you are willing to do a Masters first, then you might be able to get in somewhere. Given the competition, I would suggest that you also apply to Masters programs and that you broaden your list of programs to include other universities that are not in big cities on the coast. You could also look at lower ranked Biostatistics programs.

I think bayessays is spot-on. But without further context about how 'elite' your undergrad institution is and what math classes you've taken, I would probably add Illinois to the "reach" list. UIUC has risen tremendously in the ranks in recent years (really strong faculty and excellent academic job placements -- their PhD graduates are getting TT jobs at Penn State, Purdue, Texas A&M, Florida State, etc.). They are quite selective now. I will echo that research experience, e.g. REU and/or co-authorship on papers, is becoming much more common in statistics PhD applications. The department I work for is a mid-tier program, and we routinely admit students who are co-authors on papers (some submitted to very reputable outlets like Annals of Applied Statistics or Journal of Machine Learning Research). For PhD applications, we don't care that much about the reputation of the outlet, so it's okay if the research was published in an undergraduate mathematics research journal. Prestige of the journal is not a strong consideration in admissions. But having some substantive research -- with the potential of getting published somewhere -- really does enhance an application, and in some cases, can make up for other deficiencies in the application (like a few B/B- grades). So you should make sure that the summer research is substantive in some way and at least have the potential to be turned into a paper. Your letter writer should make this clear. As a final note, I might suggest that you not ask your internship manager to write you a letter of recommendation. For Statistics PhD applications, the best letters will convey: 1) the applicant's mathematical ability, and 2) potential to succeed as a PhD student (e.g. in research). I would suggest you get a Math professor to write you a letter who can point out your strong performance in math classes like real analysis.

Many programs have a target number of students they want to enroll for the upcoming fall. Based on historical yield, they make more offers than they expect to accept. So if a department wants to admit 6-8 new PhD students and expects around 30-40% of the admittees to accept their offer, they might send out 18-20 PhD offers. Of these offers, if more than (say) 14 of them decline, then they will go to the waiting list. This scenario happens to most Statistics departments, btw. Their top ranked applicants typically have several comparable or higher-ranked programs to choose from. So most departments have the majority of their top 10 ranked applicants decline but can also get at least a few of those ranked 11-20 to accept their offer. A few applicants may still have to be pulled off of the waitlist in late March to mid April though. I expect that the yield for Stanford Statistics is higher than at most programs, so they might not pull a lot of applicants from the waitlist. However, I would still wait until April and keep in regular touch with the Graduate Director to convey your interest. May as well wait and see what happens.

If you really want to go there, then it is worthwhile to wait until April for waitlist movement. You could periodically check in with the Graduate Director and ask for a status about your application. It doesn't hurt, and it indicates your continued interest in their program. This won't guarantee final admission if the program has met their yield, but it does create a good impression by the Graduate Director and the admissions committee. It's worth a short. Even among those admitted to several "top" programs, a majority of these applicants won't make their final decision until late March/early April.

It could vary from department to department. At my department, we do not reject anyone that we interviewed until early April. But UC Berkeley might expect a higher yield so they're comfortable rejecting applicants on the initial long list shortly after the interviews. From my personal experience: Overall, the rankings change slightly or not at all for the majority of applicants on the long list after the interviews. However, for some applicants, a mediocre/bad interview can make them drop out of consideration for first-round offers. For those that are considered borderline, a stellar interview might also push them into the first round of offers. A great interview typically won't push these "borderline" applicants into the very top tier (i.e. the top rated applicants being considered for fellowships and graduate school topoff awards)... but they could get pushed up a few ranks and secure a first round offer.

My department conducts interviews with a "long list" of applicants that we are considering admitting. The way that we do it is: 1. In the first half of the interview, we (one of the faculty in the department) typically ask the applicant some questions based on their application. So if we see that the applicant is the co-author on a manuscript, we usually ask them to explain their contribution to the paper, what challenges they faced and how they overcame them, etc. If the applicant has teaching/tutoring experience, wrote or is writing a thesis, worked as a Research Assistant or did an REU, or mentioned some research interests in their statement of purpose, then we often ask about that. We don't "quiz" the applicant about their knowledge of their stated research interests -- it's more like, how did you become interested in this area? If there was one or two semesters of weaker grades, the applicant also has an opportunity to explain this. 2. In the second half of the interview, we ask the applicant if they have any questions for us, and we answer their questions to the best of our ability. This is probably one of the most important parts of the interview, as it conveys your interest in the program and shows that you have done some research about the program. If an applicant does not have any/a lot of questions or if they say something that comes across as a "red flag" (like the applicant confusing our program with a different one -- it's happened before!), then it might give us pause and cause them to be rated down a little bit. But if the interviewee asks very thoughtful questions, then it can definitely help improve their rating/ranking. After the interviews are conducted, the admissions committee meets again and re-scores/re-ranks all the applicants, and then the top [x] ranked applicants are sent first-round offers. Everyone else on the long list (typically about half of the long list) is kept on the waiting list and has to wait to see if spots open up.

So, I work in a department where we also interview a long list of graduate applicants. I am not sure if Columbia U. does it differently, but the way our interview works is basically like this. 1. In the first half of the interview, we (one of the faculty in the department) typically ask the applicant some questions based on their application. So if we see that the applicant is the co-author on a manuscript, we usually ask them to explain their contribution to the paper, what challenges they faced and how they overcame them, etc. If the applicant has teaching/tutoring experience, wrote or is writing a thesis, worked as a Research Assistant or did an REU, or mentioned some research interests in their statement of purpose, then we often ask about that. We don't "quiz" the applicant about their knowledge of their stated research interests -- it's more like, how did you become interested in this area? If there was one or two semesters of weaker grades, the applicant also has an opportunity to explain this. 2. In the second half of the interview, we ask the applicant if they have any questions for us, and we answer their questions to the best of our ability. This is probably one of the most important parts of the interview, as it conveys your interest in the program and shows that you have done some research about the program. If an applicant does not have any/a lot of questions or if they say something that comes across as a "red flag" (like the applicant confusing our program with a different one -- it's happened before!), then it might give us pause and cause them to be rated down a little bit. But if the interviewee asks very thoughtful questions, then it can definitely help improve their rating/ranking. After the interviews are conducted, the admissions committee meets again and re-scores/re-ranks all the applicants, and then the top [x] ranked applicants are sent first-round offers. Everyone else on the long list is kept on the waiting list and has to wait to see if spots open up.

It sounds like you may (strongly) prefer University of Michigan. If you find that you are still interested in variational inference, normalizing flows, and the like, then I note that there are some strong researchers at UMich who have expertise in these areas (e.g. Jeff Regier and Yixin Wang). And it sounds like you are open to other areas as well. Duke is world-class for Bayesian statistics, of course, but you seem to have some reservations. You should go with your gut!

Letters from research advisors are certainly helpful. LORs don't necessarily need to all be from undergrad professors (some applicants only have letters from professors who taught them in a Masters prorgram and from research supervisors). Are you saying that you won't have any letters from professors who have taught you in courses, though? That might be a bit unusual -- but possibly not disqualifying, depending on the overall strength of the application. It would be good to have letters that highlight your math/quantitative ability. You could ask your letter writers to highlight your grades in relevant courses. Even just having one generic letter from a prof that confirms you got an A in their course and were ranked in the top 5% of students that semester is often very helpful.

The two posters above are correct. A 4.0 from GPA from a top school UCLA and excellent grades in those math classes definitely make you qualified for a PhD program in Statistics. Research experience is a plus, but not having it won't necessarily hurt your application that much. There is no need for you to get Masters, but if you have the bandwidth and the funds, you could potentially take a few additional upper division math/stat courses as a non-degree seeking student (for example, you took probability but did you take mathematical statistics?). You could take mathematical statistics, optimization, and another math class at a local university. This might further shore up your application as well, but it isn't strictly necessary. Your letters of recommendation and your personal statement should emphasize your math ability and your grades in math classes. In addition, you might want to give some explanation for your motivation for wanting to get a PhD in Statistics after exiting law school. At least two of your letters of recommendation should be from math professors who can speak to your ability to succeed in a Statistics PhD program. I have sat on graduate admissions committees, and we really pay attention to math background and letters from professors who can speak to that. Good luck!

JHU used to be ranked in the Statistics USNWR rankings, but I guess they were removed this past year. Their ranking under USNWR Best Mathematics schools is probably where they were moved, and their ranking there may be indicative of the reputation of the program. Even so, the Statistics group within the broader Department of Applied Mathematics and Statistics department at Johns Hopkins is a very strong group, and they have had pretty good academic placements in Math/Statistics departments (their PhD students have ended up at University of Maryland, UW-Madison, UIUC, just to name a few). So I just want to clarify that JHU AMS is highly regarded in the statistics community (irrespective of their lack of presence in USNWR rankings). If you are leaning towards industry, it probably doesn't matter that much (MSU vs. JHU). You can weigh personal factors that are important to you, like you mentioned.

One B+ in functional analysis (not relevant to most subfields of statistics) likely won't be a dealbreaker when you have mostly A's. Your profile looks quite good, and your academic pedigree will also help you a lot in the admissions process. I think you are underselling yourself a bit. I have seen applicants with profiles that are not as strong as yours get into the likes of PSU and NCSU (i.e. international students with a BS GPA below 3.7 got admitted into those schools... they did have research experience though, which you also have). UIUC has really risen in prominence in the past few years with the excellent academic job placements of their PhD aluni (their grads are getting TT jobs at TAMU, Penn State, Purdue, etc.). I think that UIUC is pretty selective now. I would put UIUC in the same tier as UWisc, NCSU, and UMN at this point. IMO, your "reaches" are actually targets, while schools like ISU, MSU, and OSU are "safe bets" (but if you want extra assurance, you could apply to a few more schools in the 30-40s USNWR rankings range). You can also afford to apply to some top 10 schools as your "reach" schools just to see how you fare -- I would pick a few of these top 10 schools based on how much they appeal to you, and apply to thse as well. Best of luck.

In your original post, you expressed some interest in going into academia. One thing to note is that if you are lucky to get a tenure-track job in North America, the most likely outcome is that you will end up in a small or medim-sized college town. Most of the universities with statistics departments are in such locations (though I suppose that some faculty do commute 1-3 hours from the nearest `big' city a few days a week).So you would have to kind of "get used" to being in this sort of environment. I don't think you will have a huge advantage in the academic job market coming from Iowa State vs. Ohio State. There is certainly a pedagogical advantage for schools at the level of (let's say) University of Michigan and higher (in that graduates from these schools may have some advantages in the academic job market over those at lower ranked schools). But I doubt there is a big leg up coming from ISU vs. OSU. Getting an academic job (at a research university) depends mainly on your publication record and your PhD/postdoc advisor(s). The connections that your supervisors have matter a great deal, and your publication record needs to be strong as well. If you are keen on industry, then academic pedigree seems not to matter that much for the overwhelming majority of jobs (going to a very prestigious program can help for a very tiny subset of jobs, e.g. quantitative researchers at hedge funds and certain financial institutions). Summer internship experience, personal connections, the ability to "ace" the technical interviews are much more helpful than academic pedigree. If you want to have a better basis for comparison, you could investigate the recent job placements of PhD graduates from ISU and OSU (if this information isn't available on the department websites, you can ask for it from the Graduate Directors at these departments). I suspect that they are pretty similar.

I think most of those ranked 20-30 (UFlorida, Purdue, UT-Austin, UC-Irvine) will be pretty tough, TBH. One way to enhance your profile would be to have a paper completed and under review. Your SOP should explain the undergrad performance and why this does not reflect your ability to succeed in a PhD program, and how you have improved greatly since then to prepare yourself for doctoral study (this is where you emphasize your strong Masters performance, your grades of A/A- in measure theoretic real analysis, graduate probability and statistics, research experience, etc.). Often times for graduate admissions, one of the key concerns of the admissions committee is whether or not the applicant can pass the PhD Qualifying Exam. So you need to remove as much doubt about this as possible. I think you may just need to apply to a very wide range of schools. I think your profile could get you into mid-tier PhD programs at least. Note that rank does not correlate to competitiveness (admission rate) either -- some schools that are ranked in the 30s by USNWR are actually extremely competitive because they do not accept many applicants (e.g. NYU Stern and Northwestern do not accept very many applicants. NYU might only accept 1 or 2... last I checked, they only have 4 Statistics PhD students total). But mid-ranked flagship state schools may be more amenable to your application, especially since you went to the top university in South Korea and "star" international applicants won't apply to as many of those programs (they may apply to only a couple of them as a "safety").

Top 20 might be very tough due to the undergrad cumulative GPA, but you could try a few schools (I probably wouldn't apply to too many, however). However, your greatly improved Masters performance from a prestigious university in South Korea and your excellent GRE scores are things that could work in your favor, though. In your statement of purpose, it is important to explain your undergrad performance and stress your improvement in your Masters. I would recommend that you apply for a wide range of schools (USNWR ranked 11 to 63), but certainly limit the number of schools that you apply to that are in the top 20-25 USNWR schools. I could see you having a shot at some places like Virginia Tech, Georgia, and below that (admissions is still competitive, but the "superstar" international applicants often do not apply to mid-tier schools, so the competition willb e slightly less fierce and more in your favor).

I don't think vicinity to industry opportunities is necessarily an issue. My PhD students are doing summer internships out-of-state this summer and they are moving pretty far away from my university (one is doing their internship in the Midwest, the other one in the Northeast). When you apply for summer internships, you apply for them all over the country and hopefully get an offer that you like. Quality-of-life and faculty research interests are certainly important factors to weigh, though. You could reach out to current students and faculty in these departments and find out more about them, or if feasible, you can try to visit them before April 15 and get a "feel" for the town/city that they are located in.

Hi GradCafe frequenters! It has been over 7 months since I last logged in (apologies to anyone who may have messaged me and I didn't respond!). I wanted to let y'all know that the new USNWR rankings have been released, and this year, the Statistics and Biostatistics graduate programs have been ranked SEPARATELY: USNWR rankings in Statistics (101 schools): https://www.usnews.com/best-graduate-schools/top-science-schools/statistics-rankings USNWR rankings in Biostatistics (65 schools): https://www.usnews.com/best-graduate-schools/top-science-schools/biostatistics-rankings I believe this is more helpful to separate the two, because depending on interests, some prospective PhD students may only want to apply to one type of PhD program or the other.

Apologies for misgendering! Yes, you could aim for Masters programs in Statistics, and you could probably get into most Masters programs. There aren't really a lot of "tiers" for MS programs. Outside of a few very selective MS programs like Stanford, Yale, and Harvard, most MS programs in Statistics are not difficult to get into, provided that you meet the minimum GPA and math requirements (even Masters at some "elite" schools like Columbia and University of Chicago are not very selective). If you really want to do a PhD in Stat, you could still apply to PhD programs -- in fact, it's better to skip the Masters if you can, since most MS programs are not funded. Your cumulative GPA from UVA and the school you transferred from is a bit shy of 3.7, which isn't bad. And your math grades aren't terrible (although B's in undergrad probability and real analysis are of course not ideal), so I would think you can still apply to schools mainly in the range of 40-80 of USNWR. If you are interested in Biostatistics PhDs as well, I could see you getting into some good Biostat programs (e.g. on the level of University of Pittsburgh and possibly some ranked above it and definitely ones below UPitt).

UVA is the 4th best public university in the country and ranked 26th overall by USNWR. It's not Ivy, but it's also considered a pretty good school. I think since the OP is domestic, he can aim as high as ~40 but shouldn't bother with too many schools ranked higher than that (on account of the math grades).

You could ask your question over at https://mathematicsgre.com/. I am sure they will be able to give you better advice about specific programs. My hunch is that you'd also have to aim mainly for mid-tier to lower-tier programs in Applied Math (i.e. I would definitely not apply to Caltech, NYU Courant, or the like, with your profile), because of things like the B in Real Analysis, no research experience, and sparse graduate coursework (a lot of the applicants for the "top" programs will have already taken a few grad classes such as measure theory, etc.). You'd probably also have to take the Math Subject GRE, though I am not sure how important this is for Applied Math (as opposed to Pure Math). For Stat PhD programs, you don't need the Math Subject GRE is most cases -- it appears that even Stanford Stat is no longer requiring it.

I'm not sure if any Statistics program will specifically be a pipeline for federal jobs. I do know a few PhD alumni from different programs who have ended up at places like national labs (e.g. Los Alamos National Laboratory), as well as federal agencies and government-sponsored enterprise like the FDA, the NASS branch of the USDA, Department of Defense, and Freddie Mac. If you go to any Stat PhD program and you are an American citizen, then I don't think it matters a whole lot where you got your PhD. It is possible that your research area matters though. Some of the federal jobs require a "technical talk" as part of your interview, and if you have particular expertise in an area of interest to them, you could get hired just on that basis. For example, there used to be a professor at the department where I got my PhD who left academia to work as a Director of R&D at the NASS. I'm pretty sure this was largely because this professor's research focused a lot on spatial statistics and ecological/environmental applications. I think maybe Biostatistics sends more alumni to certain types of federal jobs, e.g. at the FDA.