StatsG0d

I had a master's degree from a top-30 stats program, where I took several PhD level courses (Math Stats I-II, Linear Models, Generalized Linear Models). My friend (who is in a stats dept.) from the same school had all those courses + PhD-level measure theory and probability theory, limit theory, and several graduate-level courses in the math department. Sadly, we both had more rejections than acceptances.

I do not typically recommend taking PhD-level courses, because I think it calls into question why you would not just stay at the university in which you're taking these classes. Plus, I think some departments can be turned off because they want to mold you into the researchers they see fit--they don't want you coming in with *too* many ideas / opinions of your own. This is just speculation, but it certainly seemed to be a case with me (for biostats) and a friend (for stats). This is anecdotal, but still.

IMO, what will boost your application the most is none of the above. If you are able to, it's better to take upper-level proof-based math courses, even at the undergrad level. The biggest doubt of your application is your math ability. Consider taking courses like (in no particular order) Number theory Abstract Algebra Complex analysis

I think it can matter for Finance companies if you're interested in quantitative finance (who typically only recruit from very prestigious universities), but otherwise I don't think it matters at all.

This is a very interesting and atypical profile. First, if you obtained your MS at least 2 years before you will be submitting applications to PhD programs, I highly recommend applying for the NSF GRFP and writing about that in your statement of purpose. Not a deal breaker if you can't do it, but it will tell admissions committees that you're very serious about research and have thought deeply. Based on your background and credentials, I think you would have a good shot. Now, although you did not do well in undergrad, you have very good grades in graduate level stats courses, which is great. The B+ in multivariable calc might raise a suspicion, but can be overcome by a good grade in analysis. I recommend maybe also taking real analysis II (if you're only applying to stats departments) to show adcoms that your math ability has improved. I think you definitely have a shot outside the top-20. I think you'll have a better chance in biostatistics programs, which I feel are more likely to admit students with atypical backgrounds / interesting profiles (likely due to the proximity to public health). I feel like with good grades in high level math courses, you have a good shot at biostats programs like UNC, Michigan, Emory, Minnesota, and a really good chance at programs like Pitt, Vanderbilt, etc. To ease any concerns, hardly anyone has biostats "experience" prior to applying. All the areas you mentioned save for operations research have important and active biostatistics research areas. I'll summarize below, and leave it to you to decide if you're interested in them: Statistical Learning - Precision/Personalized Medicine Develop / utilizing ML algorithms to find the right treatment at the right time for the right person Typically concerned with proving under some assumptions the resulting treatment rule is optimal High Dimensional Data Analysis - variable selection / genomics High dimensional data arises naturally in biostatistics / public health (e.g., genomic data, electronic health records (EHR) Outlier analysis Can't give a specific example, but outliers occur often in biostatistics (and in pretty much any data application)

I think you might have a (relatively small) chance at Harvard, but probably not Hopkins. The other schools seem very attainable. Maybe add UNC/Michigan? They seem like good target schools for you. The biggest detriment to your app is that your real analysis grade won't be in by the time you have to submit. Do you have the ability to take real analysis in the summer? An A in real analysis would be a huge boost to your profile IMO.

It's probably much more difficult as an international student, but FWIW I did an undergraduate degree in Economics and I was admitted to nearly all the schools I applied to in the 10-20 range.

UNC is pretty mathematically rigorous relative to its peers, and you will be taking courses alongside first-year PhD students who will have taken (likely) much more math than you. That said, there are many students who have the minimal math background (i.e., Calculus I-III and linear algebra) and are successful. There will be a master's exam that is only required for master's-level students, and if you pass the exam then it's smooth sailing to get the degree. Also, note that UNC is on a pass/fail system, where the grades received are H (high pass), P (pass), L (low pass) and F (fail). Typically, <10% of students will get an H, almost all the rest of the students will get a P unless they did not do the work or bombed every single test, both of which are rare. I have never heard of anyone receiving an F. You can get two L's and still get the degree, but, again, I have seen very few students get L's.

Yeah, I agree. @cinets you should use the template utilized by others on this forum and give us more details on your profile. However, I think the B+ in multivariable calc and relatively shallow math background in general will be a detriment to your application, even if you've published a good paper. You should consider taking Real Analysis II and getting a really good grade in it. Consider taking other proof-based courses as well.

I think your chances at Harvard, Hopkins, UW, and UNC are slim (especially the first 3). If you'd applied 5-6 years ago, I think you would have a solid chance at these schools, but the competition has gotten really stiff recently. I agree with @Stat Assistant Professorthat MD Anderson and Minnesota would be good options to add. I'd maybe consider Michigan, Berkeley, and Emory as well.

I think this is pretty department specific. At UNC, for example, quals are taken after the 2nd year, so I would say 6 years is probably the average for them. It's definitely also discipline-specific, too. Statgen requires a lot of computation / cluster computing, and this can be a serious detriment to the speed that one can finish a PhD. Waiting hours, perhaps days, for code to run and having to repeat the process is a huge slowdown. @Primadonna are you trying to pursue an academic job? If so, then I feel like the extra year or so at Michigan will be worth the investment. If you're quite set on industry, then maybe UPenn is the better option/

Right. There may be some differences, but I feel like the vast majority of biostat grad students and faculty consider Michigan to be the best place in the country (if not the world) for statistical genetics by a relatively large margin. I simply wanted to point out that it's folly to say that their work isn't as impactful as what's done in a med school. At least measured by citations, which I feel is a decent proxy for impact, it's hard to beat the faculty at Michigan.

Boehnke has a couple Statistics in Medicine papers in the last few years. Min Kang has a few Bioinformatics papers. Overall, it is true that statistical genetics is a fundamentally applied area of research. But even new methods in genetics don't frequently get published in traditionally stats / biostats journals, typically because they have a relatively specific audience (namely, geneticists and doctors).

IMO the fundamental difference would be Med School researchers are typically focused more on applications, whereas SPH is more focused on methods. "More significant"... let's check Michigan Faculty Goncalo Abecasis 230,000+ citations Michael Boehnke 140,000+ citations Hyun Min Kang 46,000+ citations (stopping here--there are many more) The only prof I can find at Perelman on Google that does work in Statistical Genetics is Hongzhe Li (25,000+ citations). I'm sure there are more, but since is the most mentioned AND is the PI for the Lab of Statistical Genetics and Genomics, it's safe to say that he's one of if not the best researchers there, and yet has far fewer citations than the truncated list mentioned above. A note on rigor: Michigan requires a course BIOS801--Advanced Inference, which discusses asymptotic theory and other advanced topics. As far as I can tell, there are no such advanced courses required at UPenn.

I mean I didn’t do it because I was worried about what they might think but I think with COVID they might be more flexible. I guess I wouldn’t provide disingenuous reasons of why you want the extension but if it’s for family reasons or something you could just say that due to your personal situation you strongly desire to live in area X. If it’s just because “UCLA sounds cooler” then I probably would just accept one of your other offers.

Yeah I agree if you want to go to industry it won’t matter that much. But if your question is rigor, the answer is simple. You will find TAMU is more rigorous than NU.

Rankings are typically based on a faculty survey. They ask faculty at universities to rank each department. The rankings are then aggregated. If you want to dig in, the methodology can be accessed here: https://www.usnews.com/education/best-graduate-schools/articles/science-schools-methodology

These two programs aren't really on the same level at all. The only reason I can think to go to NU over TAMU is if you have very strong location preferences. Otherwise, I don't really see a single advantage NU has over TAMU.

Two thoughts: I'm a graduate student in biostat at a SPH and I have personally collaborated with several doctors. That statement sounds sort of arrogant to me. It's preposterous to think that the work done in medical schools is more impactful than that in public health schools, particularly considering the pandemic. IMO, it's a pretty exciting time to be in a public health school.

When I applied to UCLA (several years ago) they told me the same thing. Basically it's a waitlist, and they're going to wait and see if there are spots left after 4/15. If you really want to go there, then I agree the best thing to do is to email the program of the offer you would otherwise accept and ask for like a 1 week extension. Given the current climate with COVID, it's possible they will let you. Otherwise, it's not really worth the risk IMO.

IMO if you're interested in potentially a joint appointment with a social science, economics is probably the better way to go.

Interesting. I don't think it's that clear. MIT for econ is like what Stanford is for stats, and typically econ programs are more competitive than stats / biostats (although for these programs the competition is probably similar).

All really great programs, and a pretty difficult choice! Stanford might be a good option because Wager (stats) and Athey (economics) collaborate often. Both have worked at the intersection of causal / ML and Wager has done some interesting stuff in policy learning under constraints. Don't know much about MIT's econ program other than it's the best one. Overall, I feel like if you're more interested in probability / statistics stats departments are better. If you really, really like causal inference and linear models, then economics might be better (and this might be an oversimplification, but hopefully a useful one). One thing to consider is if you're really interested in statistics / ML, do you really want to take economic theory courses, or would you rather take stats courses?

You can definitely get into top-50. I'd say you have a good chance at the 11-25 ranked programs in the pooled rankings. I think you should take real analysis II if you want to apply to stats PhD (and not biostats).

I'm not sure that I agree with this. Hyde Park / Kenwood are really nice areas in Chicago, right of the lake, good nightlife, etc. The areas surrounding these two neighborhoods may not be nice, but the immediate Hyde Park area I think is really nice. Disclaimer: from the area and lived in the city for 4 years, so maybe biased.