Stat Assistant Professor

Your chances of admission for the top 60 or so Statistics programs in the U.S. are slim, since your university is relatively unknown and your GPA is not great. Competition from international Asian students is fierce. As an example, at my program (ranked ~40 by USNWR), most of the students were from the top universities in China, South Korea, or India. At my program, there were a few students from places like Bangladesh and Iran, but they had to be the *very*, very top students in that case (Summa Cum Laude, ranked 6th on the national entrance exam for Masters programs in statistics in their home countries -- stuff like that). To improve your chances, you would probably need to obtain a Masters from an American university and earn all A's there (then you *might* be able to get into a PhD program, but even then, it would have be a mid-tier or low-ranked one). But that would entail going into a lot of debt, which seems to be an issue for you. I think you have a better chance at Computer Science admissions with your current profile than Statistics. PhD admisisons committees for CS care a *lot* about research experience and usually are more forgiving about lower GPA's (unless it's sub-3.4, they will probably overlook this if you have strong research -- and even when it is sub-3.4, they are likely to forgive it if your research profile is very strong). It seems like you have a decent amount of CS research, since you have a first author paper that ranked in the top 10% of a conference. As @omicrontrabb pointed out, your stated interests also seem to be more aligned with computer science than statistics. There was actually one student in my PhD program who ended up transferred to a Computer Science PhD program because he discovered that Statistics (particularly all the statistical and probability theory we had to learn) was not at all what he was interested in.

Most Mathematics MS programs allow you to do a concentration too. I would suggest the OP do a mathematics Masters degree with a concentration in statistics. This way, he can take the Casella and Berger mathematical statistics sequence, while also taking the usual real analysis, abstract algebra, and topology classes. Getting A's in all those would help mitigate the B's he previously received and show that he can do proof-based work.

The most important parts of the faculty application are the letters of recommendation and your publication record. If you have a strong publication record and can secure great letters from famous people (especially from people who are not your PhD/postdoc advisor or members of your thesis committee -- that actually helps a lot since it means your work is getting noticed by people who don't have a personal stake in your success), you should be in a good position to find a faculty job. Prestige of PhD granting institution is certainly correlated with both of the above (partly because the top programs have more famous professors who are more prolific at publishing in the top journals), but a PhD from a mid-tier or lower ranked school seems to be much less of a hindrance in STEM than it is in humanities/social sciences. For example, I have seen faculty at Harvard University (including Assistant Professors and Department Chairs) in computer science, biostatistics, and physics whose PhDs are from UMass Amherst, University of Florida, and UC Boulder. The Associate Chair of the Harvard Biostats Dept did his PhD at University of Florida. My advice is to do good work, secure a good postdoc, and network with the top people in your sub-area. The rest will follow.

@playingstats Thanks for the clarification. As others have mentioned, your biggest hurdle for PhD admissions will be convincing adcoms that you can handle the rigorous mathematical coursework. Apart from possibly some applied regression, categorical analysis, and statistical computing classes, the majority of classes you take in your first two years of a Statistics PhD program will tend to be very mathematical and proof-intensive (particularly classes like advanced statistical inference, measure-theoretic probability theory, theory of linear models, and large sample theory ). Later when you do your dissertation research, you can choose to focus on something more methodological/applied, but to get to that point, you have to pass written qualifying exams on proof-intensive courses. And even if your research is more applied, you still need to have advanced knowledge of the math behind it (and some basic knowledge of theory is also helpful). Just as an example, if your research is on Bayesian nonparametrics (say), you could write applied papers that use a Dirichlet process (DP) prior without any theorems/proofs, but you still need to understand the mechanisms that make the DP prior work. Since you are currently employed by a university, I assume that they give you some sort of benefits to take courses there at a reduced tuition? In order to strengthen PhD applications down the road, you could take a few advanced undergraduate math classes and get A's in them. For example, you could retake Real Analysis and take an Advanced Linear Algebra (with proofs) class. If you can get A's in these courses, that would greatly assuage Statistics adcom's concerns that you cannot handle the math, particularly the mathematical proofs component. Masters degrees at reputable universities are also something to look into, and strong performance there would certainly help your profile a lot. Since you are a veteran, you may be able to get some educational benefits and not go into a ton of debt to obtain a Masters degree. If you are unable to retake Analysis or take advanced LA now, you should definitely retake Real Analysis in a Masters program (it can be real analysis at the undergrad level, it does not have to be the PhD-level measure theory class) and maybe a few other upper division math classes. As the others above me mentioned, you need to get A's in these courses to have a chance at some Statistics PhD program. In your case, a Masters in *Mathematics* may actually boost your profile more than a Masters in Statistics for admissions to Statistics PhD programs, because this would prove that you can get A's in math classes and partly mitigate the B's from your undergrad. And Mathematics MS programs are more likely to be funded than Stat ones, since they often need TA's for large college algebra/trig, pre-calc, and Calculus courses. So this is something to also consider.

@omicrontrabb nails it. To the OP (and other prospective applicants): It is very important for prospective Statistics PhD applicants to know that Statistics PhD programs (much like math and computer science PhD programs) are designed to train you more in the spirit of a mathematician/scientist, not as an analyst or programmer who implements ML algorithms and does routine data analysis. If doing mathematical proofs and designing new methodologies and algorithms that require heavy knowledge of the underlying mathematics are not things that sound appealing to you, then I would recommend just getting a Masters. Just as you do not need a PhD in CS to become a software engineer, you don't really need a PhD in Stats to work in data science (with a few rare exceptions like research scientist positions at places like Microsoft Research, Google.ai, etc. and obviously, for international students, it is easier to obtain run-of-the-mill data science jobs with a STEM PhD, but this shouldn't be an issue for you).

If it is for a PhD application, letters of recommendation from industry would probably not be a good idea. You need letters from people who can credibly evaluate your academic research potential. In general, I would recommend at least two strong letters of recommendation from tenured or tenure-track professors (not postdocs, grad students, or non-tenured professors) -- professors who have academically evaluated you in upper division classes or supervised you on some research project(s) and who can write glowing LORs on your behalf. The third letter can be somewhat generic and/or from someone who is not tenured/TT faculty without being detrimental to the application as a whole (as long as it is not terrible) -- though of course, if you can get three excellent letters that all come from professors, that would be the most ideal situation.

I've read and heard in many places that it is best to get letters from TT or tenured faculty whenever possible, and some are quite adamant about this, e.g.: https://www.math.uh.edu/~tomforde/KissesOfDeath.html I think to be safe, the OP should go with at least two letters from TT or tenured faculty. One letter from a statistics lecturer who is not a TT/tenured professor may be okay, but I would say that it is best to err on the side of caution and try to get at least two letters from TT/tenured faculty. The assistant professor who is supervising the OP on computer vision research would be an excellent choice for a LOR, in my opinion.

Your profile is outstanding. UCLA has one of the best math departments in the world (#7 in USNWR), and your grades and test scores are outstanding. The lack of research experience should not pose an issue, but just one question: you say that you are getting two letters of recommendation from "lecturers." Are these not tenured professors or tenure-track Assistant Professors? It would be far more preferable for you to get strong letters from tenured or TT professors (not lecturers or adjuncts) who can credibly speak to your research potential. Also, you do not need to submit more than three LORs, and the adcoms may only look at three of them (given time constraints), so I would recommend submitting three of the strongest ones you can from professors. They do not need to come from stats professors; they could all be from math profs. Anyway, if you apply to all the top 10 programs AND get strong letters of recommendation, you will probably get into at least a few of them.

The SOP doesn't have to be amazing, just competent (i.e. you can detail your academic and research experience and state a few potential research areas of interest). The letters of recommendation, grades (especially those in upper division math classes), and the general GRE scores are the most important parts of the application. The GRE is mainly a filter though, but a high score on it can sometimes lead to on-campus fellowships. Your GRE scores are exceptional -- even better than a lot of domestic applicants (the GRE V score is certainly very impressive for someone whose first language is not English).

In that case, I would apply to more Statistics schools in the range of 15-50 of the USNWR rankings, to be on the safe side, and not to Biostatistics programs (apart from maybe UW and UNC). There are a lot of great schools in between Penn State and UIUC and ranked slightly below UIUC that you would have a good shot at.

I wouldn't worry about it. Acing the math subject GRE does not guarantee acceptance to the top PhD programs for international applicants -- many of the students from my PhD program (which is ranked ~40th the last I checked) did very well on the subject test but did not get into the very top tier programs. Some top-tier programs like Duke don't even consider it, and you will hear of past applicants on this forum who got admitted to UChicago and Berkeley without submitting the subject test score. Focus on making the rest of your application very strong, including making sure that you can secure strong letters of recommendation.

I think your GPA is probably a bit too low for many PhD programs, especially as an international student who isn't coming from somewhere like UChicago, MIT, CalTech, etc. where a slightly lower GPA could be forgiven. The B in undergrad Linear Algebra might also be an issue. Is there an upper division linear algebra class offered that you can take and get an A in? I would focus on Masters programs at well-regarded schools if I were you. In your MS program, you should aim to get all A's and also possibly take an advanced linear algebra class with proofs to show that you can get an A in this class (if you aren't able to take advanced LA at your current institution) -- then you can point this out in your statement of purpose, or have one of your letter writers point out that you got an A in advanced linear algebra. Looking at your list, I think some of those MS programs might be more on the selective/competitive side (e.g. Duke and Yale). I would expand the list of Masters programs to apply to.

Being from one of the best schools in Canada should help you a lot. I think students from U of T, UBC, Waterloo, etc. are evaluated similarly to those who attended top schools in the U.S. (whether they are domestic or international). Question: are you a Canadian citizen/PR or an international student from elsewhere studying in Canada? If you are in the latter category, then it may be tough to get into the Canadian PhD programs. I would recommend applying to a few more big state programs for stats (think Minnesota, Purdue, Texas A&M) and a few other Biostat programs ranked in between UNC and UCLA.

For Masters in Statistics/Biostatistics, you mainly need to have good grades in Calc I-III and linear algebra and a solid GRE Quantitative score. Other stuff, including relevant internships, research, etc., doesn't matter that much. I think with your current profile (and contingent on a good GRE Q score), you should have no difficulty getting into the vast majority of Statistics Masters programs. I think picking up a second major in mathematics would be highly beneficial in case you want to apply to PhD programs down the road. It seems you should be able to handle this, given your current profile.

Given that you are from one of the top 3 schools in South Korea and your overall grades in math/stat classes are pretty good, your list of schools is very reasonable. You do have a few B's, but most of your other grades are excellent. I don't think you need to worry so much about the fact that you switched from business to stats. I know people from South Korea whose undergrad major was business (they only took math courses in their last few years so they could apply to Stats graduate programs in the U.S.) UNC-STOR may be hard to crack, though. This department is tougher to get into than the others you listed, and furthermore, it is also more probability theory-focused than others. I would recommend you apply to NCSU.

I think it is probably fine as long as you mention in your SOP that you took real analysis in your "Advanced Calculus" class and have one of your LOR writers mention this as well. As for withdrawing and taking 5 years to finish, I don't think any of that will affect your chances, and it is not worth mentioning. The grades (especially grades in math/upper division stats classes), letters of recommendation, and test scores are primarily what adcoms look at.

It could possibly help your application to ask one of your LOR writers to mention that you plan to take certain advanced math classes in the spring semester of your senior year (like Real Analysis II and whatever other advanced math/stat classes) to ensure that you are prepared for graduate school. But the most important things are the grades on the transcript you submit with your application (which won't have your spring 2020 grades) and the letters of recommendation. I think you should do well in admissions for most of the schools ranged 16-40 and your chances are above average at Duke and UW (conditional on good performance on the GRE and in Real Analysis and Advanced LA). Penn State has a good reputation, and physics majors who have also taken some advanced math (namely real analysis) are usually looked favorably upon by Statistics adcoms. In fact, an alumnus who just graduated from my PhD alma mater got a TT job at University of Minnesota Statistics (without a postdoc) and his undergrad major was Physics.

Assuming that you score well on the Quantitative section of the GRE and get A's in Real Analysis I and Advanced Linear Algebra, I think you have a definite shot at NC State and possibly Duke. Conditional on strong performance there, I think you could even try applying to a school like University of Washington (though this is possibly a reach). Physics is a hard subject, and your GPA is pretty good. I'd recommend adding a few more schools like Wisconsin or Minnesota too.

OP: The others have already nailed it in their comments, but I would also reiterate that it is not really necessary to get a PhD in Statistics if you want to go into data science (in a non-research role) and you are a U.S. citizen/green card holder. I know people who have gotten DS jobs with only a Masters in Data Science, Computer Science, or Statistics. There are a lot of Stats PhD's who end up going the DS route, but they are typically either: 1) international students for whom a STEM PhD is the most viable way for them to get a work visa in the U.S., or 2) American students who decided that academia was not for them (you'll find a lot of people with not just Stat PhDs, but also CS, math, industrial engineering, and physics PhDs working in this area). If you are certain you do not want to get a research-based position, you probably don't need to get the PhD. If this applies to you, you mainly need to get relevant work experience and possibly a Masters degree (though one of my friends got into data science with only a Bachelor's in Biochemistry -- he did later get an MS in Computer Science, though, which raised his earning potential and allowed him to become a Head of Data Science division at a health care AI company).

I am not really sure what criteria these international rankings are taking into account, but for example, USTC ranks No. 1 in number of papers published in journals with high-impact factor among universities in China (i.e. it has the most papers published in journals like Science, Nature, PRL, JACS, etc. of all Chinese universities). So while its overall ranking may be lower than Peking and Tsinghua, it is particularly renowned for its math and science programs and has a strong reputation for producing successful PhD students in American STEM programs. I suspect that in general, admissions committees in Statistics are far less familiar with reputation and academic rigor from University of Malaya than the other schools that I mentioned, and hence why your chances may be very slim for the very top Stat programs in the U.S. (all of these top programs could easily fill their entire incoming class with just students from the top universities in China and India if they wanted). You can try a few "reach" schools, but I would recommend NCSU as the higher end of the range of schools and generally schools in the top 20-40 of the USNWR rankings for Statistics as the main ones to aim for.

Second the advice above. With your GPA from University of Chicago and those math classes, you should be competitive for most top Statistics PhD programs. If you score well on the Subject GRE, you would have a definite shot at Stanford, but as it stands, I could see you getting into UC Berkeley, Columbia, etc. Having two strong LORs should help your application a lot too. I would recommend only applying to PhD programs.

Is "Advanced Calculus" a course on introductory real analysis? You may want to make it clear in your application. In any case, your profile looks pretty strong, but I would say your chances at CMU, JHU, Michigan, Washington, and UNC are slim, mainly because of the stiff competition from international applicants -- and all the ones from Asia who are accepted into programs at this tier are typically from Peking, Fudan, Tsinghua, USTC, Zhejiang, ISI, SNU, KAIST, KU, and Yonsei. NYU is also a tiny program (less than 10 students total), so it may be very selective as well. I think you should apply to mainly programs at large state universities like North Carolina State University and Texas A&M University (which I didn't see on your list). I think you have a shot at NCSU, TAMU, and Minnesota. I think you also have a decent shot at schools like University of Florida, Florida State University, University of Illinois Urbana-Champaign, University of Connecticut, and Michigan State University. These schools have been known to accept outstanding students from the top universities in Africa, Bangladesh, Vietnam, etc., so I imagine they would also be willing to accept good students from the top university in Malaysia.

Yes, I would also say that it's very possible to get an industry job without any internship experience at all. There are graduates from my PhD program who got jobs at Amazon, JP Morgan, etc. without any internships. If you are leaning towards industry, it might still be beneficial to do one the summer before graduating though.

In my experience, most PhD students in Statistics/Biostatistics do not do internships every summer, nor is it necessary to do one every summer. I have found that it is a lot more common for PhD students to do one internship the summer before they intend to graduate, since satisfactory performance can lead to a job offer for after graduation (and if they decide not to take the job offer, the internship experience can still give them an advantage for the job search elsewhere in the fall). The internship can also clarify if they want to stick with academia and go the postdoc route, if they were on the fence about academia vs. industry. The summer before graduating is also the most optimal time, because most PhD students have finished or are close to finishing at least one project by this point. I don't think the departments care much what you're doing, but I will say that it seems difficult to juggle both research and work. In my opinion, it is better to spend the first few summers studying for the qualifying exams and getting research done (which can take awhile to get started on -- you have to spend at least a few months just reading and familiarizing yourself with the literature of your PhD advisor's research area).

I did a Masters in Applied Math. From what I could gather about PhD admissions at the department, it did seem as though the letters of recommendation were super important, as one poster above said. Grades in math classes were also very important. PhD programs in mathematics also like (domestic) candidates who have already taken a few graduate-level classes (most international undergrad math majors will have taken classes like measure theory, topology, etc. as part of their undergrad curriculum). Since you're from a top school and are taking such courses, you should be in great shape for PhD admissions, provided you score decently on the subject test.