Stat Assistant Professor

Yeah, I can't imagine customizing letters of recommendation for every place. I have written a few letters of recommendation and always just sent the same one everywhere. Even for faculty positions, my letter writers sent exactly the same letters to every place I applied to. I also did not customize the CV, research statement, or teaching statement. I did customize the cover letters, however (just FYI, for anyone who may be interested: the cover letters are especially important for faculty applications to PUIs because they really don't want to hire someone who will jump ship to a research university the second that opportunity arises). Those cover letters took awhile to write, because I spent at least 45 minutes looking through each department's webpages, faculty profiles, course catalogues, etc.

In my PhD cohort of 9 students who finished the program, there were two of us that got TT positions (one is doing a postdoc now and the other 6 went into industry). Both of us applied to between 50-60 TT positions. Usually people focus their search on either research universities OR on PUIs. Some apply to both, but it's usually tilted more towards one or the other. In our cases, we only applied to jobs at research universities. The number of TT faculty positions to apply to also depends on your profile. If you have several JASA/AoS/JRSS/Biometrika/Biometrics papers (including the applied stats journals like JASA-Case Studies & Applications or JRSS: Series C), you can afford to be a bit more selective -- but not *too* selective. But if there's a location that you absolutely cannot see yourself living in, you can probably safely exclude it from your list of job applications if your CV is impressive. This isn't the case with pure math, where even a PhD from MIT or Harvard doesn't preclude you from ending up at in a very remote location.

What was the curriculum in your "Applied Statistics" MS program? Depending on the rigor of the program, the Masters may not help your chances much (it wouldn't hurt them though). That said, I think CMU, Washington, UChicago, Michigan, and Duke are reaches, with the first four on this list being "high reaches." There is also a somewhat large gap in your range of schools -- you go from Duke to OSU. I think schools in the range Ohio State through Pittsburgh are reasonable to apply to for your profile, and you might also have a shot at some schools ranked between Duke and Ohio State. I would recommend applying to fewer of the first 6 schools you listed and adding more schools in between Duke and OSU.

I often find that the best way to learn a new field/subject is to watch video lectures, read review articles and read select chapters from textbooks. So when I wanted to learn about variational inference, the first thing I did was watch a few video tutorials by David Blei and Tamara Broderick. After establishing this "baseline," I kind of just pick up on things as I go -- i.e. I just read the papers and try to figure out what the authors are doing as I go. This gets easier to do as you gain more experience and as you read more papers (in the beginning, I might annotate the papers a lot more). Realistically, when you are doing research, you won't know (or need to know) *everything* there is to know about, say, convex or nonconvex optimization. But you can pick up what it is you need as you go, and if you encounter something you're not familiar with, you get better at knowing WHERE to look and fill in those gaps.

1. In your SOP, you should definitely mention your research experience in preventative medicine, but say you are interested in delving more into statistical methodology that is *motivated* by problems in medicine. Also maybe ask one or two of your letter writers to mention that in addition to your research experience, you have solid mathematical training, having taken linear algebra and real analysis. 2. This is my suggestion. Apply to 2 of the top 5 biostatistics PhD programs in the U.S. (Johns Hopkins, Harvard, University of Washington, University of Michigan, UNC-Chapel Hill) as your "reach" schools. I believe these programs are ranked in the top 15 combined stat/biostat rankings of USNWR. Don't bother applying to any Statistics (not biostat) programs in the top 15, since at this tier of school, your pure math background is not competitive when compared against other international applicants at these programs. Then apply to a combination of stat and biostat programs in the rank of 15-50 (but probably more stat than biostat), *except* for the Ivy League pure Statistics programs like Yale, Cornell, etc. Those are actually very difficult to get into and have just as high math expectations as the top 15 programs. I think the larger programs at public universities like TAMU, Purdue, Penn State are probably accessible for your profile.

1. Yes, if you attended one of the top 3 universities in South Korea, you have a very decent shot at a top 40 Statistics PhD program in the U.S.A. A 3.8+ GPA from a school like Yonsei, SNU, or Korea University will make you very much "in the discussion." Since you also have some papers under review, that should also help your application (even if these aren't in statistics). 2. With your profile, you may be able to get into Statistics PhD programs ranked 15-40 (e.g. North Carolina State University, Texas A&M, Purdue, Penn State all seem like a reasonable bet for your profile). Above top 15 might be a little hard for you because your pure math background isn't as deep as other applicants from Asia -- these other students will likely have taken multiple semesters of analysis, measure theory, measure theoretic probability, sometimes classes like abstract algebra or Galois theory as well, etc. But you do have real analysis, so it's not completely hopeless for schools like TAMU and Penn State. I would apply to schools broadly from 15-50 maybe, and you can try one or two "reach" schools above 15. 3. At this point, if you are already taking classes in the fall, then there's not much more you can do to improve your profile. Your GRE score and your TOEFL score are both perfectly fine. I would speak with your recommendation letter writers and ensure that they can write strong letters for you -- with a particular focus on "research potential" and mathematical maturity. If you can ensure strong letters and if you apply broadly (for you, I would mainly focus on schools ranked 15-40 by USNWR), I could see you getting into a decent program. As to whether you should apply to biostatistics programs too... are you interested in biostatistics and public health/biological applications of statistics? That should factor into your decision. But I've also heard that Biostatistics PhD admissions tend to be more difficult for international students because of limited funding (e.g. a lot of the NIH training grants are only for U.S. citizens/greencard holders). You could try a few Biostat PhD programs, but Statistics PhD programs will probably be easier for international students from prestigious universities in their home countries to crack. If you go the Statistics route but are interested in biostat, you could always find a PhD advisor who does biostatistical applications. I know at the school where I did my PhD, some of the students in my department (Statistics) had Biostatistics faculty as PhD co-advisors. And even more theoretical departments like Stanford and UPenn Wharton have faculty who have a biostatistics tilt. For example, Susan Holmes at Stanford and Nancy Zhang at UPenn Wharton do a lot of stuff with complex biological data and statistical genetics.

Well, depends... if you're a domestic student, then you might be able to get one of those jobs without a PhD -- and sometimes with only a Bachelor's. I have a friend who has BS in Biochemistry but he taught himself programming/hacking/etc., and with the "right" connections, he was able to enter the field of data science. Now he has been working in the field for quite some time, and managing data science/engineering teams. So if you manage to get your foot in the door and obtain the right experience, your degree may not even matter that much. But if you're an international student, then it is *much* easier to get an industry job in the U.S. with a PhD. This is because it is easier to get an H1B visa with a doctorate rather than only a Masters.

You can feel free to reach out to professors if you have genuine questions about their *research* (not about admissions). It seems like in your case, you have legitimate research experience, so you could certainly discuss that with them. However, keep in mind that admissions decisions are made at the departmental level, so it's not as if a professor accepts you as their PhD student right away (indeed you would also likely need to pass qualifying exams *before* you are can even choose a PhD advisor). Additionally, don't take it personally if you don't hear back from them. Some professors may actually not be able to respond to inquiries from applicants who have not yet been accepted into the program. Either way, reaching out is unlikely to affect your chances of getting admitted. If you are really interested in working with a faculty, you could mention that in your statement of purpose. "Name dropping" doesn't usually help either, but if you have meaningful research experience, that could be one of the few exceptions. Your research experience should certainly enhance your application. 2-3 papers is very impressive for an applicant to a Stat/Biostat PhD program. Have you taken real analysis or any other advanced math/stat classes? Depending on your academic pedigree and the depth of your research (which sounds pretty legit), these things may be able to compensate for a slightly thinner math background (within reason). Since you have a degree in a STEM/quantitative field, that should also provide some assurance about quantitative abilities (it's not like you're going from something like religious studies to statistics). Finally, if you are a domestic applicant, then you can possibly get by with a thinner math background. In my opinion, this is not so much an issue once research starts. Though international students usually are more "advanced" than the average American PhD statistics student, these differences go away over time, and everyone is more-or-less on the same level once courses are done and the focus is on research (actually, some students who ace the classes/exams/etc. might struggle a lot with research, and vice versa -- so it kind of averages out).

If you are contemplating getting a PhD in Statistics and your profile is competitive enough *without* the Masters, then I would recommend just applying directly to PhD programs. But if you do insist on going the Masters route first, then the Masters in Statistics (or in Math/Applied Math where you can take the stats classes) would be the best preparation for a Statistics PhD program. For one, it might save time later as far as fulfilling coursework requirements -- you might be able to place out of all the first year classes. I have a MS in Applied Math but I took 4 statistics classes in my MS program, including both semesters of Casella & Berger and the applied statistics classes. As a result of this, I decided to try my PhD department's qualifying exam upon arrival (after spending maybe hundreds of hours practicing old qualifying exam questions), and I passed it so I was able to skip all the first year classes. That saved some time as far as degree completion. But even if you do repeat the first-year classes (applied stats and theoretical stats sequences) once you enter a PhD program, you will be completely prepared because you will have seen the material previously.

No, I think you are fine with either stat or biostat. There are plenty of Biostatistics PhD graduates who work at tech companies like Amazon, Facebook, Google, Microsoft, etc. (conversely, there are also a lot of Statistics PhD grads who work in big pharma and government agencies like FDA, etc.). If you want to get a job like that, you really just need to perform well in their technical interviews and challenges (which often includes brainteasers and probability/statistics questions at the advanced undergrad level). A PhD is a long commitment though, so you might want to consider what kind of research you would enjoy doing more, and if prospective programs have enough faculty working in your areas of interest.

Oh, I didn't realize you were from ISI. ISI enjoys a very strong global reputation, and adcoms will be familiar with the grading situation there. So pedigree is not an issue in your case. That said, most Statistics PhD students from ISI have a Masters degree in Statistics from ISI, so it's hard to give advice to a math major from there -- though there are a lot of international students in Statistics PhD programs in the U.S. who studied pure math too, so I don't think that would be a handicap. It's just that I'm not sure how your profile would be compared to other ISI students who have actual BSc and MS degrees in Stat. I think (?) you might have a chance at schools like schools in the range of University of Florida through Michigan State. MSU in particular has some great probability theory people. But it might make more sense for you to talk with an academic counselor at your institution to see how you stack up against your ISI peers who have BSc and MS in Statistics.

I don't think I would call any PhD program in Statistics particularly "easy." Many require a year of Casella & Berger, and the professors might make the exams very tricky. You have to practice a lot in order to learn how to be "clever" enough to pass the qualifying exams. That said, maybe somewhere like Stanford or UPenn Wharton would be considered "hard" because they start you out in measure theoretic probability and asymptotic theory your first year. But the students that they admit have typically already taken Casella & Berger-level statistics, linear models theory, etc. (and often, other advanced courses like stand-alone measure theory) before entering. I don't think I would distinguish programs as "hard" vs. "manageable" vs. "easy." I would call some programs more "accelerated" or more "comprehensive" than anything else -- "accelerated" in the sense that what are second-year classes at most schools are first-year classes at these particular schools. And they may also be more "comprehensive" in that they might cover more material (e.g. some top biostat programs teach measure theory in their curriculum, but a lot don't). But it's not like students at other programs wouldn't be able to manage this coursework too if they were required to learn that stuff.

Most Stat PhD students from India are from Indian Statistical Institute or University of Calcutta, and occasionally some are from IIT (though in that case, they usually do a Masters in Statistics somewhere first). Since your profile is not "typical" for a Statistics PhD student in the U.S. who is from India, it might be hard to gauge your chances. Does your program have a track record of success in placing its students in PhD programs in the U.S.? Have graduates from your program successfully been admitted to Statistics PhD programs in the U.S. in the past? The GPA may also need some context. At ISI, it is known that anything over 80 is considered very good. Is a 74-ish considered "above average" at your institution?

It might be the case that there are a few Masters programs that are very competitive, like the ones that DanielWarlock mentioned (Harvard, Stanford, Princeton OFRE, and this may possibly be the case too with other small MS programs like Yale's). However, I am not sure that this is true in general. I think competitive Masters admissions is the exception rather than the rule. Even at 'elite' schools such as Columbia and University of Chicago, it does not seem to be very difficult to be admitted to Masters program in Stat. A *lot* of Masters programs in Statistics and Biostatistics will admit anyone who has a reasonable undergrad GPA and GRE Q score, and who meets the minimum math requirements.

Funding for Statistics grad students is typically from tuition paid for by undergrads (and Masters students). In fact, I'd say well over 50 percent of the funding comes from the large survey courses (e.g. Introductory Statistics), which might have over 1000 students enrolled in a given semester. These classes are taught online for the most part anyway (with a possible small in-person component), and they have a lot of remote distance students even under 'normal' circumstances. Some "big shots" in the field also have a lot of their own grants that they can use to support PhD students as RA's. But most students are supported through TA. I think funding should be reasonably safe for Statistics. Not sure about Biostat, as there aren't typically undergraduate Biostat majors or undergrads taking biostat classes.

If that is the case, then I would recommend the OP look into Masters programs where they can take two semesters of NON-measure theoretic real analysis in their first year (along with the usual two semesters of Casella & Berger statistics and applied regression/design of experiments). If they have *no* experience with mathematical proofs, they should certainly not be taking measure theory. However, looking at the undergrad major for Statistics at UW, it looks like they actually do require a semester of Real Analysis as part of the major? https://stat.uw.edu/academics/undergraduate/major However, the OP is a "data science and statistics" major so that might be different than the BS in Statistics.

Just to clarify, domestic applicants may be able to get away with only one semester of analysis beyond the minimum math requirements and then mostly statistics courses. But since the most competitive international applicants typically have like 3 semesters of analysis (including measure theory, complex analysis, etc.) and the OP does not have other advanced math classes like abstract algebra, etc. that could signal mathematical maturity, the OP should take two semesters of analysis. In order for OP to be competitive against other international students, I suggest they take three upper division math classes minimum.

You can likely get into most Masters programs with your current profile. It would be better to do it at a reputable R1 university rather than a regional Masters only school, just so the PhD adcoms will be familiar with the rigor of the Masters institution. And it is best to ensure the program isn't an "applied statistics" professional Masters degree, but a Masters program where you are required to take two semesters of Casella & Berger mathematical statistics and theory of linear models. The "doing a Masters first" route isn't necessarily ideal, but in your case, your math background is insufficient for most PhD programs in Stat (it might be okay for lower ranked PhD programs in Biostat).

No, as it stands, I think you would struggle to get into any top 40-ish programs. Even top 50 (e.g. Michigan State-type schools) are a reach for you. Your chances would improve (a lot) if you got a Masters in Mathematics or Statistics, where you also take a few math classes, e.g. 2 semesters of real analysis, plus maybe one other math class (e.g. proof based linear algebra, numerical analysis, optimization).

I do not see real analysis as a class in your coursework. Have you taken this class? UW is a great school, but as an international student, your math background seems a bit light compared to most international applicants to Statistics PhD programs.

Your list seems reasonable, but I don't think you need to add Northwestern. I think UCLA and UCSB seem like good bets. I would recommend removing Northwestern and replacing it with a school like University of Minnesota or NCSU to be 'safe.' I think you have a great shot at UW and Duke as well. The Ivies are hard to say because they admit few domestic students. But since you have a great GPA from UC Berkeley, you should be "in the discussion," provided you have strong letters of recommendation. However, since some of them are super competitive (for example, UPenn Wharton only has 5 incoming PhD students each year, and only one or two in each batch is domestic), I would maybe recommend removing one of the Ivies from your list and replacing it with Carnegie Mellon.

If you're planning to apply to pure PhD statistics programs, then I think you are okay with what you have. A proof-based linear algebra class might be helpful. Of course, more proof-based classes is always helpful insofar as they give you more practice writing proofs and mathematical reasoning (so you'll be well-prepared to take classes like measure theoretic probability, advanced inference, theory of linear models, etc.), but this is not strictly necessary. If you aren't going to apply to very top programs, I think you're okay. Many PhD programs recommend that you can retake (non-measure theoretic) real analysis in your first year, in order to refresh your brain on proofs.

In that case, I think you could afford to aim a bit higher than the schools that are ranked lower on your initial list. I would recommend applying to more top 10 Biostat programs, like Michigan, Minnesota, UPenn Perelman. I think you have a great shot at those, and you might be able to get into UNC too. It might be more competitive to get into JHU, but you can certainly try your luck.

Your list seems very reasonable (though JHU may be a reach). I think you will be able to get into University of Florida, as well as other schools in the range of 37-50 (i.e. Ohio State through Michigan State). Purdue and TAMU are possibilities but may be tough because they also get a lot of applications from ISI, Peking, Fudan, SNU, Yonsei, etc., and they could easily fill their incoming class with just students from those elite schools in India, China, South Korea. However, you do have a publication and another paper under review which is a very good indicator of research potential. So that might make it possible for you to compete with these other international students. In addition to Purdue and TAMU, I think it might be worthwhile to try one or two of the following "reach" schools too: NCSU, University of Minnesota, UC Davis. You may also have luck with Biostat PhD programs ranked at or below MD Anderson. I know there was a PhD student in Biostats from my PhD institution who was from the top university in Bangladesh.

You probably won't ask professors from your undergrad for letters of recommendation, but I would make sure that you ask at least one of your LOR writers to make this point about grade deflation at your undergrad clear. It might also be prudent to mention it as well in your statement of purpose, just in case the adcoms are not familiar with the grading at your undergrad. Most Statistics PhD adcoms will be aware that a low 80 from ISI is actually very good, for instance, but I'm not sure if such familiarity will be similarly the case for your school. The non-traditional background should definitely be addressed in your SOP and letters, but of course, frame it in a positive light -- i.e. how you were motivated to change careers and study statistics, how you got all A's in math/stat classes, and how you are doing very well in a grad program for Statistics at present. These are all positive indicators of your ability to succeed in a Statistics doctoral program.