Stat Assistant Professor

You have a very strong profile. Make sure you get great letters of recommendation, and you should be good to go! I would recommend that you apply mainly to Statistics programs ranked in the top 15 of USNWR. You'll certainly be able to get into some (maybe even most) of these programs.

Yes, you should include those classes. You can put the anticipated grade on your list if you have a good sense of what it will be (just put "anticipated" to denote that the grade has not been finalized yet). If you don't know what your grade will be, you can just put "In progress."

I think at UC Berkeley, "lower division" generally means the Calculus sequence and intro linear algebra. Anything after that is considered an "upper division" class. So it's not so much what year you took the upper division classes, but any classes that are more advanced than Calculus and a first course in linear algebra. See here: https://statistics.berkeley.edu/programs/undergrad/major#Prereqs https://math.berkeley.edu/programs/undergraduate/major/applied https://math.berkeley.edu/programs/undergraduate/major/pure If you took any advanced math classes in your freshman/sophomore year *after* Calculus and introductory linear algebra class, then I would include those on your list. Likewise, if you took any statistics courses beyond the introductory survey course (which typically covers descriptive statistics through two-sample hypothesis tests), you could include those.

At my PhD program, we had to take 2 full years of required classes, including two semesters of measure theoretic probability, and each of the first two years was followed by a 4-hour written exam. Then you had to take 4 additional electives, so most students didn't finish classes until their third (or fourth year, if they really dragged out the requirement). But it was typically 3 classes a semester for the first two years, and then 1-2 classes per semester after that. For this reason, the overwhelming majority of students in my program didn't start research until the summer after their second year (one exceptional student started it in his first year, because he did an independent study and actually got publishable results just from that). Most people started after their second year and still graduated within 5 years, so it didn't seem as though the coursework really put anyone "behind." This past year, only one of the fifth year students graduated, while the rest of that cohort staying for a sixth year -- however, that was mainly because of the disruption caused by COVID, the temporary suspension on H1-B visas, etc. I'm sure under "normal" circumstances, most of the 5th year students would have graduated. If excessive coursework is a potential concern, you could ask the graduate coordinator what the mean time for completion is. If the average completion time still seems reasonable to you, then I wouldn't be too deterred by the coursework. As for the value of the coursework... I certainly don't use everything from what I learned (rather, bits and pieces here and there). But I think that it did help indirectly and made me more mathematically mature. I also gained a greater appreciation for probability theory later on when I was exploring the more theoretical aspects of my research, even though I found it very abstract and difficult when I was taking it.

I'm sorry, I'm not really sure about the coursework in Biostatistics Masters programs. My impression was that they are mostly the same as Statistics Masters programs in the first year. Also, to OP: it might be worthwhile to check out a few unranked programs as well. Spatial statistics is a bit of a "niche" field (so some of the best programs for Spatial Statistics are not necessarily the highest ranked in USNWR -- e.g. Colorado State and University of Missouri Statistics are both strong programs for spatial statistics). However, in addition to these schools, there are also a few unranked PhD programs that are very strong in spatial statistics. For example, I would consider University of California-Santa Cruz (they're a new department after splitting off from the Applied Math department) and University of Cincinnati to be quite strong at spatial stats. I do recommend you apply to some higher ranked programs as well, though, because you're good enough to get into them, and it's possible that your research interests might change. I think you might find some Biostatistics departments (e.g. UCLA, JHU, UNC) to be a very good fit for your interests.

I would make sure to follow each graduate school's application instructions to a tee. If only three letters are requested, then only send three. If the applications allow "up to four," then a fourth one is probably okay.

A short e-mail is fine. I've written some letters of recommendation for students for grad school or medical school applications, and I happily accepted a thank you e-mail from them.

Given your interests, I would also recommend that you look more into Biostatistics PhD programs in general. There are some very strong spatial/environment faculty at, for example, UCLA Biostatistics. Dr. Sudipto Banerjee at UCLA comes to mind, and he has excellent academic placement for his PhD advisees. Many of the top PhD programs in Statistics are not as strong in the area of spatial/environmental stats as top biostat programs.

Given your grades and your pedigree, I think you can aim higher in the ranked USNWR ranked list than your current list. The schools on your list make sense if you are interested specifically in environmental/spatial statistics, but I think you have a shot at higher ranked programs as well. Check out UNC-Chapel Hill Biostatistics, John's Hopkins Biostatistics, Harvard Biostatistics, Cornell Statistics, NCSU Statistics. All have great faculty working on spatial and environmental stats.

Given your academic performance and your pedigree, your profile looks very strong. Even if you didn't get additional research experience, I would anticipate that you would be able to get into some very good PhD programs -- you have a great shot at programs in the top 15. With great letters of recommendation, you should be in very good shape. Re: research. Adcoms probably won't expect most undergrads to have research in theoretical statistics (it happens occasionally, but is quite rare). However, your research experience with the Department of Communication at a peer university is definitely a plus. The main benefit of research experience is getting good letters of recommendation. Therefore, it might be beneficial for you to get involved in some applied statistics research or interdisciplinary research in related fields, e.g. epidemiology, public health, etc. That way, you can obtain a good letter of recommendation from a research supervisor. If you can get a good letter from a professor who supervised your work on applications of ML and statistics to political science, then you might not need to do other research.

The experimental physics professor seems like he would be able to write a more meaningful letter for you.

It's probably okay to talk about your research interests broadly. Since you would not be accepted into a specific PI's lab in these programs, you don't need to have a thesis topic -- or even a specific research subfield -- in mind. You can talk about what you found interesting about the papers you read on a daily basis to convey that you do research in your current capacity, as well as your research on applications of ML to risk management and your MS thesis research. You could talk about the directions you are interested in exploring. Since you are preparing a paper for submission, you can talk about that too. The letters will matter a lot more though and should highlight your research *potential*. I think your background is pretty strong. Based on your description, your undergrad pedigree probably enjoys a decent global reputation (e.g. in the QS rankings), and your strong Masters performance from Oxford will definitely be a huge boost to your application. I think you should be in very good shape with good letters, but the very top programs (e.g. Princeton, Courant) might be a very tough to crack (since there will be other applicants who *also* have the pedigree, excellent grades, and strong letters, but more research experience/academic publications than you). Your application would likely be "in the discussion" though.

Yes, you could do that (put up a paper on arXiv). I don't know how closely the adcom members will really read it (there's simply not enough time for that). But you can then list on your CV that you have a preprint (though judging, from your original post, it seems like you have another paper already "in preparation"). You could list this as another paper "in preparation." In any case, your recommendation letters will carry a lot more weight than having some preprints (these don't really "count" as much as accepted, peer-reviewed manuscripts... but for early career PhD students, postdocs, and Assistant professors who don't have as deep CVs, it is often helpful to list preprints on your CV). CV and preprints won't carry as much weight as letters of recommendation, though. You should talk with your letter writers and make sure that they can attest that your contribution to the research/manuscripts was primarily your own work and that you have the strong potential to become an independent researcher in the future. If your industry experience also entailed research (as in reading academic papers and coming up with new methodologies/algorithms based on these papers), you can also ask your letter writers to mention that through your work experience, you have the maturity to read and understand complex papers/

I think the MS from the University of Oxford (a top 5 university in the world) will definitely make a huge difference. That said, I'm just not sure how your profile compares to those who are accepted to Princeton, NYU Courant, and Chicago CAM. It seems like beyond prestigious pedigree and strong academic performance, research experience is much more common these days (for the top programs, anyway). You could take a look at some of the PhD students at Princeton ORFE and Courant and see what their CV's look like... look at the year they matriculated and see if they have any papers that were published in or before their first 1-2 years of study and if these papers were co-written with people from outside their program (if that's the case, then that means they likely had a paper accepted *before* they were enrolled in the program). I think your chances are above average at many schools on your list, including Cornell ORIE, but I'm just concerned that your list is a bit too top-heavy. And you don't want to end up having zero acceptances. Maybe replace two or three of the schools on your current list with some lower ranked programs.

Your profile looks strong but I think your list of schools is quite top-heavy. Those are extremely competitive programs. I'm not sure how your institution is viewed relative to the most renowned Germany schools like LMU Munich, TUM, or Heidelberg. But European undergrad programs in mathematics tend to be very rigorous in general (with PhD-level graduate coursework in the U.S. being undergrad/Masters classes at European schools), so I don't think you will run into much of a problem with adcoms being concerned about your preparation (which is sometimes an issue for American applicants). However, since the competition is so stiff, I would recommend trimming your current list a little bit and adding a few "safer" options. Of the schools on your current list, I do think that you have a chance at Cornell OFIE (given what I know about some of the PhD graduates from that department). You might have a chance at the Stat programs on your list, but because admissions is so competitive, I would recommend adding more "safe" choices.

If your undergrad was from an elite school like HKUST, the Chinese University of Hong Kong, or University of Hong Kong (these three schools in Hong Kong enjoy very strong international reputation, and they've sent their graduates to top Stat PhD programs like Columbia, University of Washington, etc.), then I think you shuld be in very good shape to get into top Statistics PhD programs. That, plus your Masters degree at an elite school, should make you competitive. You can certainly afford to aim a lot higher than UVA, Georgetown (which is an excellent school but less well-known for stat/math), or UMBC. I think your chances at Duke and UNC are above average, and you should be able to safely get into Penn State. If you are more geographically flexible, you could apply to more top stat top 20 stat PhD programs. But if you really are geographically constrained to the mid-Atlantic region, I think you should also apply to: North Carolina State University Department of Statistics (I think you would get in there) Johns Hopkins Department of Applied Math and Statistics JHU Biostatistics University of Maryland AMSC (they're not ranked in Statistics because statistics is part of their math department, rather than its standalone department -- but UMD has a very strong math department, ranked #22 by USNWR).

Right, the PhD advisor and the research area both matter a great deal. For example, it is typically going to be harder for a probabilitist to get an academic job than a statistician, even if the probabilitist went to a "top" school (the demand isn't as high, so if you do go into probability theory, you have to be *really, really* good at it to land an academic job at a research university). There are also some unranked programs that have good people, like University of Cincinnati and University of California-Santa Cruz where there are/were a lot of good professors like Bruno Sanso and Abel Rodriguez (Rodriguez recently moved to UW Statistics) who have a strong track record of academic placements. I just meant to convey that success is not determined only by the prestige of PhD institution (although that does help), but also by a proven record of good scholarship, PhD advisor, postdoctoral experience, research area, etc.

I'm not saying prestige doesn't matter at all. It can make a difference, and there are many benefits to going to a top school (like a greater number of "superstars" and professors who are internationally recognized, possibly more job connections, etc.). But at the end of the day, you make your own success. Above all else, departments want to hire somebody who has a good record of scholarship and the *future potential* to continue producing quality research after they're hired. And you can accomplish that with a PhD from any reputable school (though it might be easier to build a track record at a top school). A hiring committee is *not* going to be like, "This person has two papers in JASA/Biometrika/Annals/JRSS and seven total papers, but their PhD is from the University of Illinois at Chicago? We won't consider them at all."

Given your profile, the advice I gave in the other post remains the same. I think you need to focus on schools ranked lower than USWNR top 60 and also apply to some unranked PhD programs as well. If your ultimate goal is industry, then that is not a problem at all. If your ultimate goal is academia, I would like to point out that there was one person whose PhD was from the University of Illinois at Chicago (an unranked program -- and they combine math, statistics, and computer science all in the same department) who got an Assistant Professor job at the Iowa State University Department of Statistics this past year, which is a really good Statistics department. And the school where I got my PhD (ranked ~40) hired someone whose PhD was from University of Cincinnatti a couple years ago, and he is really killing it. This scenario may not be "common," but it goes to show that your record of achievement is what really matters above all else. In addition, most primarily undergrad institutions outside of the very elite ones (i.e. colleges without PhD programs) care even less about PhD granting institution -- passion for teaching and interdisciplinary research with undergrads is what matters most. So if you are open to jobs at PUIs, that is also something to consider.

Your grad school GPA and the fact that you are an international applicant would probably be a hinderance when applying to any top 50 Stat PhD programs (since admissions for international applicants is much more competitive). I would apply to schools ranked 60-100, focusing mainly on schools ranked 70 or lower, as well as maybe some unranked programs like University of Cincinnati Mathematical Sciences (they have a Statistics PhD program) and UC Santa Cruz. These programs are good too and are not less rigorous than higher ranked schools, and they are fine for finding a job after graduation (e.g. some alumni from U. of Cincinnati and UCSC have gotten Assistant Professor jobs at schools like University of Florida Department of Statistics, University of Chicago Booth School of Business, etc.). And if you're interested in industry, the ranking doesn't matter that much. But admissions is a bit tougher for higher ranked programs, *especially* for international applicants.

Here are a few broad categories: http://www.stat.cmu.edu/research https://statistics.sciences.ncsu.edu/research/research-areas/ https://www.stat.ubc.ca/research-areas

Yes, that Stats 300C class at Stanford is one possibility. I would say that a PhD-level advanced inference class should focus less on topics like UMVUE, Neyman-Pearson Lemma, admissibility, etc., but more on stuff like theory for shrinkage methods, convex/nonconvex optimization, reproducing kernel Hilbert spaces, resampling methods, etc. That's because the latter topics are more of current interest and are active areas of research.

A lot of departments are in the process of revising their PhD curricula, or at least discussing changes to it. I think most programs will continue to require at least one semester of measure theoretic probability -- at some schools, department Chairs/graduate coordinators are also adamant about keeping the two semester requirement of measure theoretic probability. And linear models will probably stay the same. But I think the other advanced statistical inference classes (post-Casella & Berger math stat) will eventually be updated to de-emphasize extremely detailed study of "classical" topics. The issue seems to be that for a lot of the advanced classes, the same faculty have been teaching the same class for many years. It takes a LOT of time to design a new course, and in some cases, requires learning new subjects entirely (if you're accustomed to just teaching the "traditional" topics). But once the new class is designed, I think it shouldn't be that difficult to keep teaching it or making minor tweaks to it. Getting to that point takes time though.

The most "typical" required coursework seems to be: 2 semesters of Casella & Berger mathematical statistics 2 semesters of applied statistics (based on the book "Applied Linear Statistics" by Kutner et al.) 1 semester of statistical computing 1 or 2 semesters of measure theoretic probability 1 semester of linear models theory 1 or 2 semesters of advanced statistical inference Some elite PhD programs like Stanford and UPenn Wharton skip the first two sequences above because the students they admit are fairly advanced already. Anyway: my opinion is that the typical first-year courses are fine for the most part, though they certainly should be updated to incorporate current research topics. If an entering student has not already had much exposure to statistics at the graduate level, then I think it's fine to teach the topics like linear regression, ANOVA, GLM/categorical data analysis, and theory of sufficient statistics, point estimation, hypothesis testing, etc. in detail... though I definitely agree that some of their curricula should be updated. For example, at my PhD program, an entire semester was devoted to different ANOVA/ANCOVA models, including things like split plot design, etc. That seemed a bit excessive to me -- usually, you only need to go over a couple of ANOVA models in detail to get the general gist. So if I were on the PhD curriculum committee, I would probably "modernize" the applied stats sequence (and the statistical computing class) to spend less time on design of experiments and include more modern topics. Additionally, the advanced statistical inference courses (i.e. the theoretical statistics course(s) you take in the second or third year) at many programs do seem to focus on some topics that are dated. For example, at some schools, you learn to cross every "t" and dot every "i" for "classical" topics like UMP tests, UMVUE, equivariance, likelihood principle, etc., which isn't necessarily helpful for modern statistics research. I would probably repurpose the advanced statistical inference classes to cover more 'modern' statistical theory like multiple testing/knock-offs, RKHS and nonparametric regression, convex/nonconvex optimization for high-dimensional regression, graphical models, etc.

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.