Anonymous Dino

Research.gov is still up but GRFP related portals (GRFP Login & Award List) are down. This might be it! From the relevant pages: “The NSF GRFP website will be unavailable for scheduled maintenance. We apologize for the inconvenience."

These are all super helpful responses. Both school’s preview days are coming up soon, so I’ll definitely make sure to look into these follow-ups more. Thanks for the thoughts!

Hello I have recently received acceptances from some Biostatistics PhD programs (a surprising result, although admittedly, there were maaaaaaany rejections along the way) and am having a hard time deciding between (UC) Berkeley and (U of) Michigan. My topics of interest surround Stats, Optimization & TCS for biological applications so topics like causal inference, high-dim-data and algorithm development is of strong interest. I lean slightly towards academia over industry, but not much (maybe 55/45?), so I wanna keep both paths open. I’d say that my current strengths vs. limitations list goes as follows: Coursework: Michigan - Rigour, challenging coursework & Quals don’t particularly faze me and I actually appreciate the growth and learning that comes with all three (alongside the necessary hard work). This said, I don’t have a Masters, so I’d have to take the MA coursework + the PhD coursework at Berkeley. I see this as a plus to my skillset in the long run and like Berkeley’s PhD coursework & flexibility more, but am worried that if a PhD ended up taking 6 yrs instead of 5 because of this, that I wouldn’t get funding the last year, whereas Michigan’s trajectory doesn’t seem contingent on having a Masters. I don’t want to feel like I’m rushed from day one. Financials: Michigan - Michigan’s 38.976 a year (including summer) seems to go a comparatively long way in Ann Arbor, as opposed to Berkeley’s 41.000-44.000 a year (including summer) Location: Berkeley - Weather appeals to me + I vibe with California/Berkeley culture more Prospects: Equally good - From online forums, it seems I’d have a slight edge applying industry from Berkeley, and a noticeable edge applying to academia from Michigan in Biostats, but since I’m less into stat-gen and have closer ties to theory, I don’t think coming from Berkeley would turn many eyes away (correct me if I’m wrong). Research Fit: Berkeley - The Biostats dept. at Berkeley has stronger ties to the Stats dept., and I’ve found stronger fit here based on my research interests. Service Requirement: Equally bad - Both seem to require ~20 hrs/week time commitment throughout graduation. I see teaching as a valuable experience (which I enjoy and actively make time for), but grading wouldn’t really add much value to my skill set, so I’m not excited about positions that are mostly about grading, and the associated time-sink. Luckily, these are well-known schools, so hoping that ppl can add their opinions / insights on how you’d weight these factors or things I didn’t consider. I feel like outside of funding (funding amount, and potentially, funding duration), it seems like Berkeley is a better fit for me but it seems most people would prefer Michigan, so was also wondering if there were advisor suggestions I should check out based on my interests. Also, I’m not excited about the idea of struggling to make ends on top of working towards a PhD, so was wondering if ppl had some perspective on Berkeley’s stipend. Berkeley’s first-year guaranteed housing if applying before April 20th does seem like a slight help tho. Thanks for your advice!

Speaking in no way authoritatively, but I also have had diverse research experiences (biology, computational neuroscience, computational statistics) and am applying to Biostats / Stats PhD programs. Applying for the first time this cycle and I’ve gotten interviews and even an acceptance thus far to show that you can get your foot in the door / it can work out, but I can also tell you that in my most recent interview, my interviewer directly asked why I was applying to their program and not a Biology PhD per say given my research experience, so just be prepared for this. Overall: I think you can still be a competitive applicant, but might need to address how these experiences have helped you grow and how this growth will relate to a stats PhD. You might also want to make sure that your interest in the field (stats) is well established in your SOP.

I just got accepted to be an Employer Fellow at GEM for a PhD. Neat 😅. However, reading the terms and “benefits” I have to ask: How would I benefit from accepting the GEM fellowship (for PhD specifically)? I understand that money isn’t everything, but 💰 I want to focus primarily on financials 💰, since I already have excellent mentors, so I wouldn’t make use of additional mentorship. In fact, I would find it distracting to have to report to more programs. It seems that being a selected employer fellow ensures you will have a funded tuition and that you will receive a stipend of at least 16 thousand dollars. However, PhD programs already tend to have funded tuition and (in STEM at least) stipends I’ve seen (and received 😄) exceed 16 thousand a year. Furthermore, GEM does not exempt you from TA/RA duties, so it’s exactly the same funding package you would receive without GEM (I confirmed at the webinar that even though GEM uses the word “stacks” regarding other fellowships, we GEM applicants would NOT receive any of the stipend, and instead, it only goes to the university to subsidize the cost to the university). Therefore: what do we get out of GEM? Assuming I could get the internship on my own, I’m worried that the fellowship just means more strings attached on the money I would have received anyways. Thanks, and I’m grateful to your perspective and insight in advance 😄.

Initiating 2023-2024 application thread protocol for Statistics/Biostatistics… Algorithm executed. Use this topic to - Ask questions - Post admissions results & funding, or the stage you’ve reached in the recruitment process - Discuss your thoughts in the process - Anything else! Wishing everyone the bestest of luck!