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.