Stat Assistant Professor

Given your exceptional performance at ICL and Oxbridge (which both rank among the top 10 schools in the world) and the fact that you were able to publish a paper with a statistics professor, I anticipate you will have no difficulty getting into a top program in Mathematics or Statistics in the U.S.A. I think you are actually in the top tier of the students most likely to be admitted to these programs: having already taken a handful of classes that would typically be taught only at the PhD level in the U.S. *AND* having published a paper in your BS/MS program. The vast majority of PhD applicants cannot claim both of these feats, so your profile is very impressive. For the top PhD programs in Statistics in the U.S., I think you are in good shape. Past mathematics coursework counts for a lot more than statistics coursework in PhD admissions for Statistics (similarly the case for PhD programs in fields like Economics and Finance). I think the rationale is that someone with a strong math background can learn the Statistics/Econ/Finance part at the PhD level no problem, but this may not be true of someone who has a degree in one of those fields but who has a light mathematics background.

Just make sure your letters of recommendation are very strong. Stanford is tough for just about anybody, so it's hard to say if you are a "sure" thing there. But I think you have a reasonably good chance for Berkeley. I would recommend taking some more advanced/graduate-level math classes, since the UCB Statistics PhD application asks you to submit a list of your advanced math/statistics courses, course descriptions, and textbook used. Since you got an A in graduate-level probability, you should be able to do well in a graduate-level math courses like measure theory and functional analysis -- looking at the UCB Math Course descriptions, this appears to be the sequence "Math 202: Introduction to Topology and Analysis." Classes like those are sure to make your application stand out. Starting in the fall of next year, I would also recommend taking the Masters level mathematical statistics sequence (looks like this is STAT201A-STAT201B at Berkeley). It seems like you should be able to perform well, given your past performance.

I think you have a very strong profile, with a good GPA from Berkeley, research experience, strong grades in math classes (including an A in graduate-level probability! That's not an easy class!), and a high score on the Subject GRE. Research in AI is definitely relevant to Statistics. I wouldn't be too concerned about awards. Assuming you have very strong recommendation letters, I anticipate you will be able to get into a program like University of Washington or University of Michigan. Berkeley isn't out of the question either. Stanford is a difficult one for most to crack. Do you have any geographical preference? I think you have very solid chances at schools like UMN, University of Wisconsin, Texas A&M, and Penn State. And you could afford to try a few other top tier programs like Carnegie Mellon, Penn Wharton, or Columbia.

Math, statistics, and CS should all be okay as far as industry career prospects... even a lot of those who studied pure math (which doesn't have as many industry applications) can move into successful careers. A large number of the PhD graduates in Mathematics from the department where I got my Masters went on to become software engineers/developers at big companies like Google, Bloomberg, etc. For academia, the math job market is very competitive, with many PhD graduates needing to do multiple postdocs to get a job (it's not unheard of for math PhD graduates of schools like MIT and Harvard to be postdocs for 4-5 years before landing their first TT job). The academic job market is better in Statistics and CS, where one postdoc is more the norm. I think it is a good idea for you to figure out what you like first and then approach professors for research opportunities. For math and statistics, the undergraduate research opportunities seem to be mainly summer REUs, whereas for CS, professors will let undergrads work as lab assistants. In either case, it is probably a good idea to get a flavor for research to see if you enjoy it enough to pursue a PhD. The PhD is a long grind and if your ultimate goal is to work in machine learning in a non-research setting, it may be better to get a Masters and relevant work experience.

A Masters in Statistics would be more helpful than a Masters in Applied Math for machine learning. In either case, you could probably enroll in a Machine Learning class though (either taught by a Statistics dept or a Computer Science dept).

1) You can learn the basics of Python just by doing Code Academy, edX, or one of the free online course providers. I have found personally, however, that the best way to learn programming, software packages, etc. is to use them regularly. I had limited experience with LaTeX and R before graduate studies, but I was able to pick up on them fairly quickly just by using them regularly. 2) For research opportunities, you will have to ask a professor if you can work as their Research Assistant. 3) A Masters in Statistics or a Masters in Computer Science gives you ample opportunities in industry. A PhD might be preferred for some of the bigger companies like Google and Microsoft, but you can still get a decent job in Data Science/ML with just a Masters, sometimes with only a Bachelor's if you get the right experience. I have a friend who only has a Bachelor's in Biochemistry but now he's the Head of Data Science & Engineering for a health care startup. To the best of my knowledge, he got his first job as a data scientist out of college (no graduate degree), but he had to teach himself how to be a good "hacker" (Python, R, etc.) to get that job. After he had the relevant experience, his academic credentials didn't really matter. I wouldn't say that getting a PhD is necessary to become a data scientist if you are a U.S. citizen (the bar is higher for non-citizens -- if they want to work in industry in the U.S., a PhD often makes it easier for them to get these jobs). You can get by with only a Masters and sometimes only a Bachelor's. Only do the PhD if you think you will like doing academic research.

As for being out of school for three years, I don't think it matters too much. If you look at UC Berkeley's Statistics PhD Alumni and Current Students, you will see that there is one alumnus who obtained his B.A. in 1997 and completed his PhD in 2012, another who obtained his B.A. in 1985 but completed his PhD in 2013, and yet another current PhD student who completed his B.S. in 2002. Age and time spent out of school are not things that are considered relevant for PhD admissions for math or statistics. However, having a solid math background is crucial for getting admitted.

Agreed with bayessays that you would have a decent shot for some Biostatistics programs if you complete the math prerequisites. It's not impossible to switch to Math or Statistics for a PhD. But in your case, you would probably need to get a Masters degree in one of those areas first. I studied Econ/Political Science in undergrad and then got a Masters in Applied Math after completing several university math courses at local universities. Then I did a PhD in Statistics later, but I probably wouldn't have been able to get into a Statistics PhD program without having a Masters first. There are also several alumni from my PhD alma mater who did not study math/statistics as undergrad (they got BA's in Journalism, Economics, and History) but a few of them are now professors at R1's and one is working as a senior data scientist. But all of these guys needed to get a Masters first. For Mathematics, there are also a few accelerated post-bac programs for "non-traditional" applicants that are very good and that have good PhD placement. I don't think your chances for a Computer Science PhD are very good without BOTH taking a number of advanced undergrad CS classes AND performing research in a CS professor's lab. Whereas research experience isn't strictly necessary for Statistics and Math, it is crucial for CS admissions, and you won't get in without any research experience. I have some friends in CS who took a gap year between finishing their BS in Computer Science and enrolling in a PhD program *just* to get more research experience by working as a CS Research Assistant and to prepare/size up their PhD applications. So if you want to go the CS route, keep that in mind that you'll need to do a bit more than just ace classes (research actually matters more than straight A's for CS PhD admissions).

I am doing some research on Bayesian nonparametrics and kernel-based regression right now and have been mostly learning it on my own "on the fly." Agreed with bayessays to take the class that interests you most. A PhD and postdoc teaches you to learn things "on the fly," which means: a) learning just enough about tangentially related areas to answer your research question (like the basics of functional analysis and reproducing kernel Hilbert spaces -- I do not need to become an expert in these areas), and b) you will probably learn "just enough" just by having a well-versed person explain it to you and by skimming a lot of papers, one section of a textbook, or lecture slides (the latter is especially helpful). When I do look at theorems and proofs, I only carefully read the parts that are needed for understanding the "general" technique and that I think would be useful for solving my own problem (not every lemma or technical detail is going to be relevant/useful).

For Computer Science, there is a bigger emphasis on "fit," i.e. whether you can fit well into a research lab. This is because much of your PhD support will be from a professor's funding (you may be supported as a TA for a few semesters, but most of the time, you'll be supported as RA). So in this case, it would be best to send follow-up emails to professors expressing your interest in their lab (whereas these types of e-mails to individual profs are typically ignored in Math and Statistics). For this reason, it is harder to assess your chances in CS than in math or statistics, because there is so much consideration given to individual research fit. Re: papers. It seems to be more common nowadays that top applicants have a conference publication (not necessarily as first author, but sometimes second or third author), but it may not be strictly necessary except at the top CS programs. Having research experience and three papers submitted is a very positive point for your application. For Applied Math and Statistics, your funding will usually be by the department, so research "fit" is not given as much attention. Admissions is more based on "hard" numbers, like GPA, math grades, and test scores. I have found that the Results page on thegradcafe.com, past years' admissions results posted on thegradcafe's "Mathematics and Statistics" sub-forum, and past years' admissions results posted on mathematicsgre.com give a pretty good picture of your chances. Strong recommendation letters also matter. If you are concerned about how much the subject GRE score affects your chances, perhaps you can talk with your math professors who might be more familiar with the admissions process. They can tell you whether or not your academic performance in graduate level math courses and your research experience will be enough to mitigate the low score.

I think you need to have a clearer idea of what you're looking to get out of in a doctoral program, as admissions for PhD programs (and the PhD programs themselves) in each of those fields is quite different. What research do you see yourself doing? Theoretical computer science, stochastic processes, or statistical machine learning? I would recommend first deciding what you would like to focus on (CS vs. applied math vs. statistics) and then targeting mainly those programs. 1) For Computer Science PhD programs, you'll likely need to have a Skype interview with a PI (only a few math and stat PhD programs conduct interviews). It is okay if you don't have publications (though that may have changed now, with many of the top candidates having at least one conference paper)... but your application needs to be quite "research-dense" to have a shot (a slightly lower GPA seems to be a lot more forgivable in CS if you have strong research experience to make up for it). You need to demonstrate a clear research focus in your application, and so the statement of purpose/"research statement" matter a great deal. See Philip Guo's advice here about how he reviews PhD applications in computer science: http://www.pgbovine.net/PhD-application-tips.htm 2) For Applied Math PhD programs, you will need to score reasonably well on the Math Subject GRE to have a chance. There is much greater emphasis on grades in advanced math classes, and the strongest applicants will have already taken graduate-level courses in mathematics (which you have done and seem to have done well in). The statement of purpose isn't as important, and it is acceptable not to have a well-defined research focus. As it stands, however, your Subject test score is too low for most programs and you'll probably need to retake it if you go the Applied Math route. I would recommend spending a great deal of time preparing for it (most students forget a lot of the stuff they learned freshman year). Prior research experience isn't considered as essential, but letters of recommendation describing research "potential" are crucial. 3) For Statistics PhD programs, you don't need the Subject GRE for most places. The emphasis for admissions is also on grades and mathematical preparation. Statement of purpose is not a big deal (unless it's absolutely terrible), so adcoms won't expect you to have a very concrete idea of your research interests. But strong letters of recommendation describing research "potential" are crucial. Your overall GPA is a bit lower, but your math GPA is quite a bit better, and you've also taken a lot of grad level math classes, so that is a plus. Your chances also heavily depend on how prestigious your "big state school" is. If you could give an idea of how well-regarded it is (e.g. what range of USNWR rankings), that will give us a better idea what range of schools you should target.

From your description, it sounds like you did make some progress on your project though, so it might still be a good idea to ask this supervisor for a letter of recommendation. PhD programs aren't looking for already "successful" students per se (that's what the PhD program is for! To train you to conduct research and publish at a professional level). They really want to recruit those who are a) mathematically advanced, and b) who can *learn* to do research and successfully publish. It sounds like you already have a very good idea of what research is all about -- that is, trying to make extensions to existing work, playing around with a problem, attacking it from multiple angles to see what works, and possibly failing along the way. And that is a definite plus. Good luck!

If you studied math at the #1 or #2 university in the UK (and thus, also one of the top universities in the world) and finished in the top 2 of your class, then you should be able to get into some of those PhD programs on your list. I would honestly be shocked if you did not get admitted to one of those on your list. For these programs, math preparation is much more important than coursework in statistics, and the math curriculum in the top UK schools is known to be very rigorous (i.e. the advanced undergrad classes there are at about the same level as the first-year PhD level classes in U.S. math departments). Publications are a plus, but not essential.

It should be okay to just submit your regular resume. If it were for a PhD application, it might be better to submit a CV (even if the CV is really short for many statistics PhD applicants, a CV signifies your interest in potentially pursuing an academic/research career). For a Masters degree, the regular resume is fine... if they even look at it that closely.

It's not academic dishonesty. If the department asks you about the withdrawal or the fact that your transcript goes from "Fall [year]" to "Fall [next year]" with a missing spring semester (though I seriously doubt that they will ask), you can explain the situation to them. They might have concerns if there were multiple WD's on your transcript and especially if they were in math/statistics courses, but if it's a one-time thing and it had no effect on your overall GPA, it should not be an issue.

I'm not sure it is worth drawing attention to the WD or the semester off. One or two WDs is no big deal, especially if they are not in math, statistics, or other quantitative classes.

My mistake. Thanks for correcting. I knew most undergrads had not done publishable research in statistics, but I assumed it would only be considered a bonus if it was in a STEM area or a quantitative discipline (like quantitative economics or something). Good to know it helps even if it is in humanities. OP: please disregard my previous comment.

It probably won't have much bearing, since it is not relevant to PhD research in Statistics.

I think it is fine to upload them as supplementary documents. You can also make note of them in your SOP, i.e. "I have uploaded these papers as supplementary documents." Ideally your LOR writers will describe your contributions in some detail, so adcoms have a good sense of the content. With hundreds of applications to read and score/rank, it may be the case that some admissions committees will only scan these extra documents.

Also, do not put the acknowledgement on your CV. In any event, the CV is likely only to be skimmed by the adcom (as the majority of applicants do not have any publications -- even the majority of those admitted to top schools won't have them... most of the info on a CV would already be visible in the application packet). Adcoms will probably not read the arXiv submission either, so if you do have a paper or two, be sure to have one of your LOR writers discuss the paper(s). It could also be described in your SOP, but it will carry more weight coming from a strong recommendation letter.

It is appropriate to put your submission on your CV under a section for "Submissions and Works in Progress" or something like that. Put "(Under review)" after the article title. This section could also include papers that are "Under Revision for [journal name]" or "In preparation." You *cannot* list your submission under "Publications" until it has been officially accepted for publication by the editor (even papers still under revision don't count since revision does not always imply a final acceptance). Once it has been accepted, then you can list it under Publications with "[Journal name] (Accepted) or [Journal name] (In press)." Many CVs have two sections: one for works that are under review/under revision/in preparation, and one for publications that have been accepted or have been published.

I second the suggestion of going on vacation. Or teach a class if you need extra money. You could also ask your graduate coordinator for summer suggestions. In math and statistics, most people don't start research until after the qualifying exams and the bulk of coursework (minus a few electives possibly) are finished. As long as you meet these milestones, you should be able to graduate "on time."

That is a very good score and it won't hurt to submit it.

Will you be finished with all your qualifying exams before next summer? Most PhD students don't start research until they have completed all their qualifying exams. The first summer after their first year, most PhD students in math/statistics teach a summer class and/or prepare for their qualifying exams. If you want a "headstart" on research though, I guess you could start reading some papers to get accustomed with the academic jargon. But I would definitely prioritize studying for your qualifying exams over anything else.

The "diversity statement" is not only for those from disadvantaged backgrounds, it's to determine "underrepresented" applicants (who may or may not be economically disadvantaged), i.e. domestic female, URM - underrepresented minority, or first generation student. At other schools, this information (gender, race, ethnicity) is readily evident from the application packets even without the diversity statement, and it does get factored into admissions.