cyberwulf

It's not very well known; in fact, this is the first time I'd heard of it.

I wouldn't worry about your GRE verbal score. It sounds like you are at a US university, in which case the fact that you achieved a 4.0 means that you were able to communicate in English well enough to succeed in your discipline. With stellar math grades, that should be sufficient to make you a good candidate.

Quite right; I should have been clearer in what I wrote above about the GRE Verbal that I was referring to students whose first language is English. I personally find that the GRE Verbal provides very little useful information about an international applicant because, unlike US-based students, internationals almost always study intensively for this part and often achieve scores far above their actual verbal ability. I suspect that in most quantitative departments, the average GRE verbal score of international applicants (mainly Chinese/Korean) is higher than for their domestic counterparts; at least that's typically the case in our department.

At good quantitative programs, most 'serious' applicants' GRE quant scores are high enough (160+) that trying to discern between applicants on that basis is pointless. The GRE Verbal is more of a proxy for general intelligence, so a high (say 165+) GRE V could grab a reviewer's attention in an otherwise unspectacular application. Lower scores, provided they're not disastrous, are less of a concern if the rest of your application is strong. As R Deckard correctly points out, these metrics tend to be highly correlated (moreso among domestic students), so the reason good schools don't take many students with low GRE scores is not because GRE scores are vital, but rather because those low scores tend to accompany otherwise mediocre applications.

Thoughts: - Your quant score and math GPA are on the low side; there may be some concern in admissions committees about your ability to handle more advanced mathematical coursework. - The biggest 'reach' on your list is probably University of Michigan; Northwestern isn't a particularly renowned stats program, and may in fact be one of your better bets given the other places on your list. - You might consider doing a Masters first, to demonstrate your mathematical/statistical capabilities. I think you would be a good bet for Masters admission at most places on your list. - You might also think about looking into some Biostatistics departments. Biostat will have a more applied flavor than stat, and your background might play better there. Among Big 10 schools, Michigan and Minnesota have the top biostat departments. UNC is also good. You'd be a great bet for Masters admission at all of those schools, and likely borderline for PhD admission.

My personal opinion is that this is an inappropriate suggestion on the part of the professor. I think everyone would see the problem with a male professor inviting a prospective female applicant to a first-time meeting at their house; why should this be OK for a female professor and male student? If the professor's house is close to campus, the obvious alternative would be to meet there -- clearly a better option for everyone involved.

While this is true, the situation isn't THAT dire. Training grants (awarded by NIH, available only to U.S. citizens) typically account for no more than 20-30% of funding in biostat departments. The bigger issue is that there are a lot of international applicants (plus the domestic ones) competing for the remaining places. OP, your list of schools seems reasonable, though to be safe you might consider adding a couple more places.

I think your math background is ok, though your GRE Q score is a bit low. Your TOEFL score is also on the low side (some departments have an unofficial lower bound of 100). However, I think you can reasonably aim for statistics programs in the 25-50 range, and biostat programs outside the top 10.

While I think biostatistics field is heading towards earlier specialization, I don't think we have yet come to a point where employers are demanding domain-specific skills from new graduates. Rather, searches are typically organized around a set of (bio)statistical skills (eg. causal inference, diagnostic testing, survival analysis, spatial statistics) which can be applied in a range of clinical domains. Specialization comes gradually, after you have worked on specific projects for awhile. My worry is that pigeon-holing yourself as, say, a "neuroscience biostatistician" will needlessly narrow the set of positions you would be competitive for. In academia, for example, the top 20 biostatistics departments each average roughly 1 open position per year; I'd be surprised if more than 1-2 (if any) list neuroscience as a specialization of interest. I suspect that demand (as a fraction of available positions) is even lower in the private sector, where most hiring of biostatisticians is done by pharmaceutical and medical device manufacturers. Outside of a handful of top people, few faculty biostatisticians are "paying the bills" (obtaining grant funding and consulting on funded projects) entirely through neuroscience-related research. So, to the degree that it's possible, I think it's good policy to keep an open mind to other areas of application. Not really. It's still an excellent program, but it's larger than both Harvard and Johns Hopkins, and the applicant pool isn't quite as strong. Further, they also offer unfunded PhD spots (not that you would necessarily want to accept such an offer...)

A few thoughts: 1) I generally caution prospective biostatistics graduate students against getting overly specific in their interests when picking schools to apply to/attend. Most students end up working in an area very different than the one they initially planned on; that is the nature of the discipline. Accordingly, you don't want to pass up an opportunity to attend a better school because it appears that they have fewer researchers in your particular area. That impression might be wrong; faculty biosketches are notorious for being imprecise and out of date, 2) The fact that a school has a good neuroscience and/or psychiatry program should carry little weight in your choice of biostatistics programs. First, I would suggest that virtually all the places you have listed will have "good enough" programs in these areas that, should you want to pursue research along these lines, you would be able to find ample opportunities. Second, you will be working on your primary research with an adviser in biostatistics, so the quality of potential biostatistics faculty advisers is vastly more important than the quality of faculty in other parts of the university (most of whom you are unlikely to work with, or even meet). 3) UNC has a strong neuroscience imaging group led by Hongtu Zhu; if you're truly serious about combining biostat and neuroscience, you might want to give Chapel Hill another look. 4) Your likelihood of admission to the places you list will depend a lot on your undergraduate (and current graduate) institution. Unless you attended a truly elite college, Hopkins and Harvard are likely out of reach. The other places seem more reasonable targets, though I would suggest expanding your list a little bit.

Don't worry about it. While 3.0 is a relatively low score for a student applying from a U.S. undergraduate program, it sounds like the rest of your application is strong enough that the response to that piece of information will probably be along the lines of "huh, that's odd, I wonder what happened there?" before it is essentially disregarded.

With a PhD in physics, there probably won't be too many concerns about your ability to do math. So I think the main question adcoms will want an answer to is "why (bio)statistics"? Maybe it's because you can't get a job; maybe it's because you've lost interest in hardcore physics. Whatever the case, I think it's important that you address your motivation for switching fields (and be honest), and what you envision your career path to be.

I would guess that if you are above the minimum score (especially 100, which is actually a decent score), your TOEFL should not greatly affect your chance of financial aid. Most stat and biostat departments don't care much about your GRE verbal score, so if that's bringing down your total GRE score I wouldn't worry about it.

This is true; the main reason to skip the Masters is because there is typically little funding available for Masters students while most PhD admissions come with a full funding package. One thing to keep in mind is that most programs will consider you for Masters admission if you don't quite make the PhD cut. So, if you're open to starting in the Masters program, be sure to make that clear when you apply (how to do this varies by program; call and ask if you're unsure).

As usual, I'm happy to bang the drum for biostatistics (vs. stats or biomath), but it sounds like you're already leaning that way. Good! I think sisyphus and Noco might be a touch optimistic in their assessments of your prospects for PhD admission, chiefly because of the main concern you cited in your original post (weak undergraduate institution). Coming from a little-known school doesn't "instantly kill" your chances, but it does mean that other parts of your application will have to be pretty strong to convince adcoms that your performance isn't purely a symptom of big-fish-small-pond syndrome. As always, the devil will be in the details: how well/poorly is your school perceived, how outstanding are your letters, what do your grades in key classes look like, etc. Without knowing more, I would guess that you are most likely to be "in the discussion" for PhD admission at biostat departments like Minnesota and Michigan. The above details could push you either towards "longshot" or "likely admit". Washington is probably more in the "longshot"-to-"possible" range depending on the above. You should be able to get into all of the major Masters programs. You might throw apps at a couple of biostat departments from among the following to increase your chances of success a little bit: Emory, UCLA, Brown, Columbia, Penn.

All I really wanted to express is that, as you move down the US news rankings, the bar for what constitutes "excellent" performance (i.e. the performance that will get you into top grad schools) tends to get higher. If you're in the top 5% of your class and have research experience, your school would have to be pretty weak for the typical adcom to be skeptical of your ability. But, if you're "merely" a top 20%er (or programs you're applying to get a ton of top 5%ers), then prestige/selectitivity of your school can matter, even when comparing two schools in (say) the top 50 nationally.

A good thought, but this isn't really necessary. All biostat departments know what two semesters of "Advanced Mathematics" means on a Chinese transcript.

That's simply untrue. I guarantee you that, if they had the same GPA, adcoms would take a (say) Stanford grad over your hypothetical Oklahoma student 9 times out of 10. Now, if the Oklahoma student had a 3.9 but the Stanford student was running a 3.5 GPA, *then* I think there would be legitimate debate about who to accept. Nope! Students from top liberal arts colleges fare very well in graduate admissions across a range of disciplines, indeed probably better than all except the top 10-20 R1 institutions.

Another vote for #2. Many adcoms, even in quantitative disciplines, are concerned about international applicants' abilities to write in English. A letter which explicitly highlights writing/communication as a strength could really help differentiate you from the pack; most adcoms get tired of wading through endless letters saying, essentially, "XXXXX got an A in my class, handing in all the assignments on time and performing well on the exam." These letters tell you nothing beyond what's on the transcript, and so don't really benefit your application.

Your verbal score is basically irrelevant. The Q score is fine, and furthermore, it matters less for you than many other applicants because you already have a Masters (I assume in statistics) from NC State, where your performance will give adcoms a good picture of your math/stat abilities.

This couldn't be further from the truth. Grades are almost entirely relative, so that a student with a 3.6 from an Ivy League institution is almost certainly a better bet to succeed in graduate school than someone with a 3.9 from a local commuter school. That being said, there simply aren't that many above average students at elite insitutions, so going to a lesser known school doesn't preclude admission to good places. But the height of the bar (in terms of GPA and strength of letters) is basically inversely proportional to the reputation of your undergrad institution. Now, with respect to the OP's question, I would suspect that the math GRE is a relatively strong equalizer, in that excellent performance could put you "in the discussion" at better places which might not otherwise give you much of a chance based on the rest of your profile.

I think you should go for the extra year. Because of the nature of Canadian undergraduate programs, virtually all math majors will have had exposure to proof-writing and real analysis before graduating, and these students make up the bulk of applicants to statistics Masters programs. You will likely stand a much better chance of getting into a top Masters program if you have these additional math courses under your belt.

This is more or less right, though I would move 'personal statement' to the bottom of the importance list. Also, yoyolulu, you should make sure that you have taken multivariable calculus before you graduate; this is a prerequisite for essentially all biostatistics graduate programs.

Most programs will consider you for Masters admission if you don't make the PhD cut; I recommend you find out whether that's the case for the schools you're interested in, and apply to the PhD wherever you can confirm that policy. Your list seems reasonable to me. Hopkins is probably the biggest reach because they tend to favor pretty mathy applicants, and their incoming classes are pretty small. Though it's somewhat of a longshot, I wouldn't totally write off the UW PhD program (and in any case, you'd be better off in a funded PhD program at almost all the places on your list than paying for a Masters there). Of the top 6 places on your list, Minnesota is by far the most Bayesian-oriented. An approximate ranking of 'Bayesianism', from most to least, looks something like: Minnesota Michigan UNC Columbia Hopkins/Washington

A CS background is useful in most areas of statistics these days, since the vast majority of modern research incorporates computer simulation. Areas which make extensive use of computation include: statistical genetics / computational biology, graphical models, functional modeling, imaging, and spatial statistics. You might also consider biostatistics departments, many of which also do a lot of machine learning and data mining work. I assume you haven't done a ton of math coursework beyond the usual pre-requisites, and this will be less of a limitation in biostat than stat.