captivatingCA

First off, I have to say that I haven't read the other replies extensively, so apologies if I'm repeating what others have already said. I'm a new PhD student from a 'diverse' background, and I understand a lot of what you're saying. I've felt a lot of the things you're feeling now (lack of preparation, unease in voicing my concerns) and I'm still trying to figure out how to deal with them myself. I entirely agree that not enough time and effort is put into supporting underrepresented/disadvantaged students. I've also thought a lot about the psychological effects of affirmative action for students who benefit from it; even if you're as qualified as your peers, you often feel as though you have something to prove and have ground to make up. Not to mention how it might affect how other students and faculty view you. You also may feel like a sort of sacrificial lamb, testing the waters for folks after you and trying to make things better, often at your own expense. With that said, I think I want to focus a bit more on your situation. These issues aren't going away any time soon unfortunately, so you'll have to learn to operate within these constraints. With regards to the classes you're struggling in, I think the advice you've gotten so far is about as good as it gets. I really think working with other students is where you'll get the vast majority of your help though. It's cliche, but ask all the questions you need answers to (I'm working on this myself). In theoretical courses I've found that there's a few tricks, theorems, methods, etc. that are hard to know to use if you haven't had a LOT of math experience. Asking people what they did and why helps you start to fill your arsenal. With regards to your actual question, I agree there's a good chance that it'll be difficult to maintain your anonymity while raising your concerns. I think if you want your concerns heard honestly you'll have to take that risk. It's almost like a balancing act; you want to critique but you also don't want to come off as antagonistic. That's hard to do! But I think the best path forward is getting allies. Within in the department, try to find students, faculty, and staff who agree with you and are sympathetic to your concerns. They can exert power where you may have none, take a bit of the load off your shoulders, and give insight into what can change and how to change it. It's not easy to find folks who are willing to help, and you have to be a bit careful how you go about it. Outside the department, look for people and organizations who are already working on the problems you see, for example diversity offices/officers, grad student unions, women in STEM groups, and possibly some administrators. These people will already know how the systems of the university work, and they'll be able to advise you on how to approach your situation. They may even have some knowledge of your department. The separation from your department is also great benefit since you can speak candidly with a much decreased risk of word getting back to faculty. On a final note, earlier today a friend mentioned how one department viewed failure. The faculty said something along the lines of, 'When a PhD student fails, we see it as a failure on our part, not the student's.' I think the sentiment is true for all departments, even if the faculty don't see it this way. The series of women who've left your program supports this idea. So whatever happens, you haven't failed, the department has.

I agree with @bayessays. I think my letters are what pushed me over the edge. It sounds like your letters will be great, so I think you could have a good shot at the schools you mentioned. If you haven't already, I would show your recommenders your list or ask them what schools/departments they think you're competitive for. That definitely guided my application decisions. I also wanted to add that even though your list has a decent range in terms of ranking, the departments you mentioned are all pretty selective either due to size or prestige or both (e.g. Yale). I think you would benefit from applying to a few more big departments. Texas A&M comes to mind since they have a significant Bayesian presence. Additionally, it may be a good idea to focus your applications to one field in particular (if you aren't already).

I think you have a good shot at getting into some of the top programs. I don't think a few B's will tank your application, especially since you have A's in similar courses. It might help to have one of your recommenders speak specifically to your ability in those courses. I'll defer to more seasoned members to speak more in-depth on your chances though. Searching for labs before you're accepted isn't common in statistics as it may be in CS. You're accepted to the department instead of a particular lab. I'm not intimately familiar with all of the research going on at different institutions, but several departments have a fair number of people in ML theory and lots of departments are moving towards ML. If you have a particular topic in mind that may help in giving recommendations.

I'm curious, did most people get lengthy reviews? I suppose it might vary by field. Mine were pretty terse, just a few sentences each. They gave enough info, but it would have been nice to hear more of the reviewers' logic for their scores.

I agree with @bayessays and @statistican. IIRC, the rankings are determined by surveys sent out to professors in stats departments. USNWR averages the scores and ranks departments from there. UChicago has a great reputation among faculty, therefore it's high in the rankings. A lot of things can change between surveys (which I believe happen every 4-5 years), and a lot of important info is imperceptible to people outside a department (e.g. your prospective advisor plans to retire soon). So within 10 or so places, take the rankings with a grain of salt, like bayessays said. W.r.t. to ML research, it seems like most departments have been trying hard to increase their presence in ML. However, ML is only one part of modern stats research. ML itself is broad, so if you're dead-set on ML, I would encourage you to consider how connected the stats department is to the CS department. That could augment you choices of research and advisors.

Not awarded - VG/E, E/E, G/G. I'm an undergrad senior and applied through mathematical sciences - statistics. The first two reviewers really liked my application and didn't really have any critiques. The third thought my proposal lacked 'scope and depth' and that my broader impacts were 'too ambitious to be realistic'. I have a lot of inter-sectional identities that I implemented into my personal statement, so maybe it seemed like I was fishing for points? They all played a part in my experience, so I thought it would be relevant to include them. My future goals were ambitious, but certainly doable. Reviewer 3 also mentioned that I did not have a solid previous research product, which is rare for undergrads in stats (if by product that mean publications). The reviewer may have been a researcher in machine learning (which my proposal is related to), and it's much more common for undergrads to have publications there. When I found out I wasn't awarded I figured I would just learn from the reviews and do better next time. I never expected to have conflicting reviews. Now I don't know what to do. I'm also a little upset because this is the second time one reviewer out of three has tanked an application(the first time was to a conference). At the very least this is a learning experience; now I know firsthand how random the process can be. Sorry for the rant! Tl;dr - The reviewer curse is well and alive.

If you're 85% sure already, go for it! CMU has a great department, so no need to second guess your decision.

First off, congrats on your acceptances! I just want to add my two cents from my perception of these departments. I'm just an applicant and haven't had extensive experience with these departments, but I can share how things seem to me from talking to grad students and professors. In general, these departments are more alike than different. statsnow mentioned a lot of positives to Berkeley, and I think CMU shares a good chunk of them. CMU is really flexible in their requirements, and all of the courses are pass/fail. At the virtual visit for CMU, it was mentioned that the funding structure gives students a lot of freedom in advising. So at both Berkeley and CMU you could theoretically have different advisors for all of the projects that make up your thesis. They both have great connections to industry and ML groups. That being said, there are differences. To me, it seems that the barrier between students and faculty is lower at CMU. That varies a lot between professors though, and I don't think the difference is substantial. CMU also has a few joint PhD programs, but I'm not sure if your experience will be substantially different if you're in a joint program. Berkeley has more high-ranked departments outside of stats, so there could be more potential collaborators. CMU is smaller, but there's no shortage of collaborators, especially since Pitt is down the street. Berkeley's location is an advantage due to the proximity to other big universities and Silicon Valley. Berkeley seem more theoretical (CMU is pretty applied), but there's a lot of applied people there too. In terms of coursework, CMU has the ADA project. For the most part, the differences aren't that vast. The big differences between the two will be lifestyle. Given that CMU and Berkeley share a lot in terms of academics, I think it would be easier (maybe even better) to focus on the kind of life you want to live. UC Berkeley and CMU are two very different universities in two very different cities. Since you'll be there for five years, I think you'll maximize your productivity by choosing the place you could live best. If it's still difficult at that point, just follow your gut, there's no wrong answer!

I don't know the answers to these questions, but I'm sure the graduate coordinator does. If you shoot them a quick email, they'll be able to point you in the right direction.

I wasn't awarded, and I definitely felt a bit of a ding after reading the email. But I've started to look at the things that have gone right for me so far. Personally, I'm just happy that I'm going to grad school. Even if the NSF is indicative of ability (which, as jstop28 mentions, isn't necessarily true), a rejection isn't a death sentence to my scientific career. I have a lot of time ahead of me to learn and develop, and I'm looking forward to seeing the reviews so that I can figure out what areas to improve. While the validation (and money) would have been great, not getting the fellowship doesn't fundamentally change the calculus of the next few years, and it won't stop me from pursuing my goals.

I haven't decided just yet, though I've narrowed it down a good bit. I started with more obvious things like location and research fit, and used visits (in-person and virtual) to get a better sense of the departments. I also talked to people who know me well about my choices. I feel like most of it is based on personal preference not objective fact since all of these schools are great places to be. I don't want to clog up the results thread, but feel free to message me if you'd like to know more.

Undergrad Institution: ~Top 100 (according to US News) Major(s): Mathematics Minor(s): Computer Science GPA: 3.85 Type of Student: Domestic Black Male GRE General Test: Q: 163(~85%) V: 164(~90%) W: 4.5 (~85%) Programs Applying: Statistics and Biostatistics PhDs Research Experience: I did research during my freshman and sophomore years, but it wasn’t relevant to stats. I worked with a math professor at my university on a bunch of random stuff (e.g. data visualization) over the past two years. This wasn’t research per-se, mostly just learning the material in a more intimate setting. I did summer research programs after my sophomore and junior years. Both were in well-respected departments. One was in a stats department, and the other was in biomedical informatics. I worked in a stat professor’s lab when I was on exchange. Letters of Recommendation: Mentors from my past two summers of research and a professor who I have taken a few courses from. Math/Statistics Grades: Calculus II, III (A, A), Intro to Linear Algebra (A), Proofs and Problem Solving I, II (A, A), Differential Equations (B), Intro to Analysis I (A), Numerical Analysis (A), Probability and Statistics I, II (A, A) CS Grades: Intro to Computer Science (A), Computer Science I (A), Computer Science II (A) I spent a semester on exchange at a top 3 university (according to US News). Here are the relevant courses: Modern Algebra I (A), Discrete Math (A), Essential Data Structures (A) Applying to Where:Harvard - Biostatistics PhD / Interview invite-1/7 / Accepted-2/11 NC State - Statistics PhD / Accepted-1/7Texas A&M - Statistics PhD / Accepted-1/19UW - Biostatistics PhD / Interview invite-1/24 / Accepted-3/4Duke - Statistics PhD / Accepted-1/31UW Madison - Statistics PhD / Accepted-1/31UW - Statistics PhD / Accepted-2/7UC Berkeley - Statistics PhD / Accepted-2/11U Michigan - Statistics PhD / Accepted(funded Master's)-2/14CMU - Statistics PhD / Accepted-2/21Cornell - Statistics PhD / Accepted-2/24UChicago - Statistics PhD / Waitlisted-2/26 Takeaways: I thought it would be good to share some of the things I've learned from this application process. I'll try to keep it short! First some straightforward, actionable advice. Apply for the NSF GRFP, it makes the rest of the application process so much easier. Study for the GRE; it's not hard but it's easy to get caught off-guard by some of the questions(at least it was for me). Turn in your applications early; it's a huge weight off your shoulders during a very stressful period. Trust your recommenders, advisors, and mentors. I applied to so many places because I was unsure of my chances at any of them. However, one of my recommenders told me at the beginning of the school year that I'd be successful in my applications. It's definitely important to hear multiple opinions, but if your advisor is experienced in the field, take their advice to heart. Don't compare yourself too much. Obviously the whole point of this thread is to compare yourself to others, just don't stress over it too much. GradCafe is great to see some of the commonalities between successful applicants. As long as you cover your bases though, missing one or two aspects that someone else had may not make as much of a difference as you think. Not to mention all of the factors you don't see on GradCafe. Don't give up if you didn't go to a brand-name school! I met lots of people on visits who didn't go to the Harvards and Stanfords of the world. There are other ways to stand out besides going to a big name school, so don't give up just because you go to Directional State U. Hopefully someone finds this useful!

Past threads: 2013, 2014, 2015, 2016, 2017, 2018, 2019 Here's the thread to submit your profile and results for stat and biostat programs for Fall 2020. You only have an hour after you post to edit, so it is best to post only when you have all of your results or have made a decision. Give as much detail as you feel comfortable with! Below is the template: Undergrad Institution: (School or type of school (such as Big state/Lib Arts/Ivy/Technical/Foreign (Country?)) Major(s): Minor(s): GPA: Type of Student: (Domestic/International (Country?), Male/Female?, Minority?) GRE General Test: Q: xxx (xx%) V: xxx (xx%) W: x.x (xx%) GRE Subject Test in Mathematics: M: xxx (xx%) TOEFL Score: (xx = Rxx/Lxx/Sxx/Wxx) (if applicable) Grad Institution: (school or type of school?) (if applicable) Concentration: GPA: Programs Applying: (Statistics/Operation Research/Biostatistics/Financial Math/etc.) Research Experience: (At your school or elsewhere? What field? How much time? Any publications or conference talks etc...) Awards/Honors/Recognitions: (Within your school or outside?) Pertinent Activities or Jobs: (Such as tutor, TA, etc...) Letters of Recommendation: (what kinds of professors? "well-known" in field? etc.) Math/Statistics Grades: (calculus sequence, mathematical statistics, probability, real analysis etc.) Any Miscellaneous Points that Might Help: (Such as connections, grad classes, etc...) Applying to Where: (Color use here is welcome) School - Program / Admitted/Rejected/Waitlisted/Pending on (date) / Accepted/Declined School - Program / Admitted/Rejected/Waitlisted/Pending on (date) / Accepted/Declined School - Program / Admitted/Rejected/Waitlisted/Pending on (date) / Accepted/Declined

Since the application season is nearing the end for PhD applicants, are people interested in creating a results thread? I created last year's thread, and if there's enough interest I can go ahead and create this year's.

Congrats to those who received the fellowship! I got an email this morning to check the portal and was notified that I did not receive it. My reviewers were pretty helpful, and one gave specific advice. I applied under Physical Sciences. I'm curious how applications are matched with reviewers in terms of fields. The NSF is specific enough for reviewers to know some of the intricacies of the applicant's field (publication rate, required experience, plausible research ideas, etc.). I'd imagine that there could be some issues with a mathematician reviewing a chemist's application for example. EDIT: I applied for the predoctoral fellowship btw.