Stat Assistant Professor

That said, I think it would be fine to ask them if they are taking new students, like @galois said. That's perfectly acceptable. But I wouldn't ask them if they are open to supervising your thesis.

I would have to disagree with your professor who suggested this. That seems kind of presumptuous to ask someone if they could potentially be your thesis supervisor when you haven't passed your qualifying exams yet. Usually you have to pass your qualifying exams before you can start research. Moreover, it seems highly unlikely that a newly admitted student has a good enough understanding of the professors' research area to make an informed choice about advisor. I don't mean that you need to understand the advisor's research in depth before you ask them to advise you. But in order to make a more "informed" choice, one could refine their research interests by attending departmental seminars, taking a "Readings in Statistical Science" class (like the mandatory reading classes that the first-year students at Duke and Stanford have to take), and talking with several different professors about their current research to gain a much better understanding of what it entails (these conversations would still be at a fairly general level, but they would certainly make you more well-informed than just looking at/browsing through papers on their personal webpage).

Ah okay, yeah, I think I know which SLAC you are referring to as well. I checked and the average GPA at this school is under 3.2, so I definitely do not think the lower GPA will hurt your application -- as this particular institution is known for lower average grades and has a strong track record of sending its graduates to top grad schools. OP: I do think you would be more competitive for slightly higher ranked PhD programs with a Masters (considering most of your upper division math classes were taken at a regional state school), but the schools you listed should be attainable for a PhD with your current profile.

Yes, scoring the math subject GRE would be helpful but only if the score is 70th percentile or higher or so (otherwise I wouldn't send it). I could see a mid-tier or lower ranked school possibly taking a chance on you, but I also think you would be well-served by getting a Masters degree at a flagship public university or a more reputable private university. That would certainly improve your chances of getting into a more reputable PhD program. It seems as though competition has gotten a lot more fierce since I first applied to grad school (just over 5 years ago). If you could ace the Casella & Berger theoretical statistics sequence and one or two additional upper division math courses (e.g. some math departments teach first-year graduate/Masters students real analysis from the Rudin textbook) in a respectable Masters program, then that would certainly position you well to get into a Statistics PhD program and assuage possible concerns about your undergrad performance or the rigor of the school where you took your non-degree coursework. Also, you would be a lot better prepared for doctoral study too (in my personal experience in Statistics programs, there is less attrition among domestic PhD students who have Masters degrees than those who only have a BS/BA). The downside of this obviously would be cost and two additional years of study before you start your PhD. But if you *really* want to get a PhD in Stat, then I think you should also strongly consider Masters programs as a "gateway" to that eventual goal.

There is no way to really answer this without looking at the whole package "holistically." If the statistics student has very strong research experience (even if it's not in statistics specifically, but in something like computational biology or computer science) and stronger letters of recommendation, then the statistics student would certainly look better. If the Statistics major went to say Stanford, while the math major went to an unknown school, then the former would also be preferred. GPA in upper division major classes is also a factor. However, assuming all other things being equal besides the coursework (alma mater about the same level of prestige, similar overall GPA and GRE scores, similarly strong letters of recommendation), then the applicant with a strong math background but only 1-2 stats courses would typically look better to adcoms than the applicant with a lot of stat classes but minimal math classes. A lot of Statistics PhD programs have something written on their websites' FAQ's to the effect of "Although some exposure to undergraduate probability and statistics is helpful, skills in mathematics and computing are more important."

What does your comment "not playing the rankings game" mean exactly? As it stands, you could probably get accepted into one of the schools that you listed, or at a similarly ranked school like UFlorida or UIowa. I wouldn't say it's a "sure thing," but if you can get good recommendation letters and emphasize the strong performance in upper division math classes, I could see you getting in somewhere around that tier of schools (and lower-ranked ones too). It may be helpful to mention in your application somewhere -- or have one of your LOR writers mention -- the textbooks you used in your courses (e.g. if you used Casella & Berger for the Math Stat I&II classes, Rudin for Analysis, etc.). Otherwise the adcoms may not have a good sense of how rigorous the coursework was at "directional state university." Scoring well on the math subject GRE would definitely help your application too, but in my opinion, your effort would be better spent strengthening other parts of the application.

I am not sure it will help being URM and female. Just to clarify to everyone, this only really helps in admissions if the applicant has a strong profile irrespective of race/gender (high GPA, good grades in math classes, reasonable GRE Q score). *Then* the underrepresented status is a plus. And C's in Calc III and Linear Algebra are a red flag. If feasible, I second the suggestion of retaking Calc III and Linear Algebra -- and possibly DiffEQ as well -- to demonstrate you can handle the math in a Biostatistics MS program. Have one of those professors write you a letter of recommendation. Finally, aim for lower ranked schools like bayessays suggested (not unranked regional schools but ones that have a lower minimum GPA requirement). You may need to go through some administrative hurdles though if your GPA doesn't meet the minimum threshold. But I have heard of people being accepted to PhD programs with sub-3.0 GPAs (caveat: they had to have something *very* outstanding in their profile, like a publication and years of research experience, and these were labs where the PI decided to take a chance on them).

If you have the math background, then you don't really need to take additional classes at a local university. This is only advised to do if you have no or a weak math background or if you need to take care of some prerequisites (e.g. real analysis) before applying. And you certainly have the requisite background if you've taken a bunch of proof-based math classes. A lot of statistics PhD applicants have backgrounds in pure math and are fine taking the Casella & Berger sequence with no prior background in the subject. That said, if you have been out of school for some time, it would definitely be helpful to review some Calculus, linear algebra (including proofs), and basic real analysis before entering your PhD program. But since you are applying this upcoming fall, you have plenty of time. See these two threads for a suggestion of things to review (the below applies to both incoming statistics and biostatistics grad students): https://forum.thegradcafe.com/topic/117420-general-reviewpreparation-prior-to-ms-biostats-program/ https://forum.thegradcafe.com/topic/117365-best-probability-textbook-for-self-study/

I recommend that you take the mathematical statistics sequence. In your Statistics graduate program, you will most likely need to pass a qualifying exam based on statistics theory, but not one on measure theory. If you do well in the graduate mathematical statistics sequence, you could potentially take the first-year exam upon arrival at your PhD program and get this exam out of the way (and even if you repeat the Casella & Berger sequence, it will be a lot easier for you since you'll have learned the material previously).

I do not think you would be at a disadvantage if you attended UT Austin if you are interested in academia (and if you change your mind, there should be no difficulty finding a job in industry with that degree). The Statistics department at UT Austin has placed graduates in postdocs at UC Berkeley (in Michael Jordan's lab no less) and Princeton: https://stat.utexas.edu/people/phd-alumni That is encouraging for someone interested in academia. You also mentioned in your original post that you like UT Austin's research focus and smaller size. Those are quite important factors to consider.

I don't think it is that probability/stochastic processes/etc. isn't as favored in the academic job market, it's just that it is much more competitive to get an academic job if those are your primary research areas (you may need to do two postdocs, and you need to be *really* good at it). As for theoretical statistics, there actually is a great deal of interest in statistical theory (as well as methodology and applications), but the subject matter that you do research on matters. Just as with computer science, the field of statistics changes pretty rapidly these days, so if you are researching something that is relatively obsolete, that has been "beaten to death," and/or is not of much current interest in the statistics community, then it will be hard to publish on it in good journals (and thus harder to get good academic jobs).

For linear algebra, specifically review stuff from matrix algebra (like determinants, singular/nonsingular matrices and inverses, eigenvalues, diagonalizable matrices, trace, rank and nullity of a matrix, nullspace, column and row space, symmetric positive definite/semidefinite matrices) and vector spaces (basis/span, orthogonal decompositions, orthogonal projections, orthonormal bases). You don't need to memorize the proofs for "big" theorems, but you should be comfortable doing proofs using these concepts (e.g. proving that all the elements of the diagonal of a symmetric positive definite matrix are positive). That sort of thing.

Your profile is likely fine for the Masters programs you're targeting. Best of luck.

Just about every Statistics and Biostatistics Masters program will have a theoretical component (at the very least, a full year of Casella & Berger statistics theory and one course on Theory of Linear Models), unless you do one of those Applied Statistics Masters programs. And I think a lot of employers view the "Applied Statistics" Masters as less rigorous than just a plain Masters in Statistics.

Linear algebra: review "Linear Algebra Done Right" by Sheldon Axler. It's a good textbook for a second course in linear algebra (proof-intensive). Real analysis: review "Understanding Analysis" by Stephen Abbott. Rudin is a good book, but if you haven't taken real analysis before, the Abbott book is a much "gentler" introduction to the subject. And also, reviewing analysis at the level of Abbott is probably sufficient before entering graduate school in Statistics (Masters *or* PhD). I wouldn't bother studying measure theory, unless you have already learned it before, are very interested in the subject, or plan to focus on theoretical probability in grad school rather than statistics (even in mathematical/theoretical statistics, the amount of measure theory you need to know in-depth is generally minimal and at a fairly basic level). Learning R: It might help to get a book and work through some exercises or to do a free online course (does Coursera offer one for R?). I've found the best way to learn it is to use it regularly and to search Google when you need help on something specific. If you've never used R period, then I would try to find some exercises to gain familiarity with basics like vectorization, lists, and writing functions (vectorization helps a LOT in speeding up code). I don't have a particular book in mind for this though, unfortunately. Calculus: You can basically skip anything with trigonometry and polar coordinates, but you do need to know how to do derivatives and integrals of other common functions (polynomials, exponentials, logarithms, etc.), including partial derivatives and double integrals. And you need to know some fairly common rules, like integration by parts, how to solve improper integrals, etc. which will certainly come up in homework problems from Casella & Berger. If you're comfortable with it, you probably only need to do enough practice problems to reacquaint yourself with the method of integration/differentiation and leave it at that. If you're also reviewing analysis, then you can probably also skip anything on series from Calc II, although it would certainly be helpful to recall how to find the infinite or finite sum of a geometric series (this comes up sometimes for problems on discrete random variables).

It wouldn't hurt to reach out to the universities in your hometown and ask about opportunities. You could also apply to work as a data analyst or research assistant in one of the departments (a lot of biostatistics departments and medical schools in particular have full-time data analysts whose job is to help clean data, offer consulting services, and support professors in their research, and these analysts are added as third authors on publications). Biostat departments and departments in medical schools may be the best ones to apply to with your background. Another thing to consider doing is taking online coding boot camps (such as Hear Me Code classes) or online data science academies to get up to speed on the machine learning/programming stuff. I know people personally who have Bachelor's degrees in history and biochemistry who are completely self-taught and now working as programmers or data scientists. It doesn't really matter what your degree is in for industry -- what really matters that you can pass the technical interviews and that you demonstrate clear proficiencies (which can be done by showcasing programming/ML projects that you did independently). Those are the best ways to convince an employer to take a chance on you if your degree was not in CS/math/stat.

Just ask one of your LOR writers to mention in their letter that you performed very well in upper division math/stat classes and received nearly all A's in them and also that you managed to raise your overall GPA from [freshman year GPA] to [GPA at the end of your junior year]. In your statement of purpose, this upward trend and demonstration of mathematical ability should also be emphasized. Most of your LOR writers will probably ask for a transcript and other materials (e.g. class projects, etc.) and things you would like them to mention in their letter. The letters of recommendation are pretty crucial -- much more so than the statement of purpose, and evidence of mathematical ability and research "potential" are the key ingredients to a strong LOR for statistics PhD programs.

I second this. Your PhD program should teach you measure-theoretic probability, and there is not really any need to know it before starting (although I've found that a handful of PhD students, especially international ones, have already taken this class before). It may help to review undergraduate probability though. For Calculus, I would also recommend reviewing u-substitution and change of variables (for both single and double integrals) and derivatives for univariate functions (like chain rule, product rule, etc.) and partial derivatives. It's not at all necessary to review things like washer methods or any Calculus involving trigonometry or polar coordinates. You should be able to find "cheat sheets" for common differentiation and integration rules online, and it might help to do some practice using those (skip anything with trig).

I wouldn't infer any causation here (i.e. attending one of the three schools causes students to go into industry). There may be self-selecting bias, where the students who enroll at these programs had a preference for industry to begin with. Even at some higher ranked programs like Harvard and NCSU, a majority of the PhD graduates go into industry. That said, everybody needs a "Plan B," especially those who go the academic route. If you can't get an academic job after two postdocs, you should definitely start planning your exit strategy to industry or government.

Your list of schools for your profile is very reasonable. I think your strong performance in upper division math classes can compensate for your lower overall GPA, which seems to be mostly due to your grades from freshman year (you should have a LOR writer emphasize this trend and make it clear in your application that your GPA is due solely to a rough start your freshman year, not due to inconsistent performance throughout all of college). As long as you are showing an upward trend and can secure very strong recommendation letters, I think you have a good shot of getting into one of the programs you listed (of those, NC State and Penn State may be a bit of a reach).

Second the comment about Cun-Hui Zhang who has done a lot of great work on high-dimensional regression (both parametric and nonparametric). He's the inventor of a popular alternative to the lasso for penalized/regularized regression called the MCP (minimax concave penalty). Hans-Georg Müller at UCD is also prominent for functional data analysis, a field I have been doing some research on in recently and which has some interesting applications. I think all of programs you listed have more graduates going to industry than academia, but it doesn't really matter if your PhD research was theoretical or applied if you want to go the industry route. If you want to go into academia, it usually helps to be at least somewhat theoretical, even if your primary interest is methodological/applied. This is the case for both Statistics and Biostatistics. For Biostatistics, unless your field is something like RNA sequencing, medical imaging data, etc. (which would lend itself to publication in mainly in journals like BMC Bioinformatics, Nature Methods, etc.), a publication in a statistics journal like Biometrika, Biometrics or JASA gives you an edge in the hiring process (and papers in those venues frequently contain at least one or two theorems).

It sounds like a Masters in Statistics or Biostatistics is the best choice for you at this time. You don't need a lot of math to get into an MS program -- just Calc I-III and basic linear algebra. If you decide later that you might want to do a PhD, you should take real analysis in your Masters program.

Permanent residents and green card holders are considered domestic applicants since they don't require student visas to study in the U.S. And there is less competition among domestic students for sure. Either way, though, "Asian male" is definitely not an underrepresented group, so it wouldn't help. It does help in admissions to be a highly qualified domestic female or underrepresented minority, though. As it stands, the OP's math background is still too light for Biostatistics (you don't need a *ton* of upper division math for biostat, but you do need real analysis -- and the amount of upper division math/stat needed on the transcript seems to be inversely proportional to the undergrad GPA. If your undergrad GPA isn't stellar, then you certainly need more math as evidence of mathematical ability to compensate for that).

I think what bayessays said is right. You can mention your research projects (briefly) if you want in your email. The most important thing, however, is to reiterate your interest and make it clear you would accept the offer if you were admitted.

OP: Unfortunately, I think the above poster is correct in his assessment of your chances. However, I don't see any real analysis in your list of classes you have taken, and the course titles for the classes you took in your MS program do not give any indication whether you had to take theoretical/mathematical statistics (e.g. "Data Mining," "Medical Statistics," "Designed Experiment," and "Statistical Method" suggest to me that these courses were more on the applied and computational side and not of the Casella & Berger flavor). I think you may also struggle to get into a Biostatistics program without real analysis and a few more mathematical classes. Even if you ultimately decide to do research that is heavily applied/computational, you still need to pass theoretical courses like Theory of Linear Models, Statistical Inference, Large Sample Theory, etc. in a Biostatistics program. And the adcoms willl have concerns about this with your record. I don't think a stellar Math Subject GRE score will help much (since Biostat departments don't seem to care much about this, and scoring decently on this standardized test can be done through a lot of practice). I think your best chance may be to either enroll as a non-degree seeking student and take a handful of advanced math/stat classes and do well in them, OR obtain a Masters in Math or (Bio)statistics in the U.S. In some Biostat MS programs, you can transfer directly to the PhD program if you perform well in the Masters-level classes and the first semester of PhD-level classes (you could take one or two of these in the first semester of your second year).