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Geococcyx last won the day on June 29 2019

Geococcyx had the most liked content!

About Geococcyx

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  1. I won't comment on your application's strength, but some other folks (and their respective schools) where you might find spatiotemporal modeling: Daniel Simpson and Vianey Leos-Barajas at Toronto (I know someone already recommended Dr. Simpson, just included for completeness' sake), Rick Schoenberg and Karen McKinnon in the UCLA statistics department (not sure how easy it is to do cross-advising between biostat and stat there), Brian Reich at NC State, and Matthias Katzfuss at Texas A&M. Additionally, for wearable technology, I tend to think of Vadim Zipunnikov at Johns Hopkins and maybe Julian Wolfson at Minnesota, but there are probably many folks I just haven't found. This isn't really my area, so I hope these are good enough folks to look at as a start until you can get into the papers and see who's getting cited, and that I haven't accidentally misrepresented anyone's research interests. Most of them do at least focus on environmental applications, which tends to be a good start.
  2. Stat Assistant Prof answered some of your non-teaching questions here: https://forum.thegradcafe.com/topic/116685-good-productivity-benchmarks-for-a-strong-research-advisor
  3. Some of OP's questions seem like they might be answered with the slides and video recordings from eCOTS's workshop on preparing for teaching-focused faculty positions; here is the website to access that: https://preparingtoteach.org/agenda/ (there was recently a review of this posted via Sara Stoudt and Mine Cetinkaya-Rundel, posted here for a quick summary: hathttp://www.citizen-statistician.org/2020/06/preparing-to-teach-2020-what-did-we-learn/). That said, they seem to largely agree with our experienced posters, so this may not be worth your time.
  4. A 30k stipend from VCU seems pretty impressive, given that it also appears to be your highest-ranked PhD program (albeit that doesn't really matter for industry). Maybe Richmond is a lot more expensive than I think? A cursory cost of living calculatory check seems to suggest 30k is still really good, but who knows how valid those are. I should obligatorially note that you don't need a PhD to go into most industry, but I understand that may be more convenient for you to get a PhD, particularly since it looks like you're an international student and there might be visa details to work out. Congratulations on your offers!
  5. Well, NLP and deep learning are often topics that come up in CS departments -- perhaps OP should be looking at CS Master's or PhD's instead (insofar as they're looking at any graduate programs at all)? Granted, I don't know that those open any more doors than a Stat PhD (which we've established to be limited anyways), but maybe OP's unfulfilledness is partially because they're interested in working on different, more CS-oriented problems? (To avoid my only contribution being armchair psychology: @Bacaw, is there a reason you'd prefer statistics over maybe CS or math or engineering if you went back to grad school?)
  6. In the interest of avoiding some possibly unnecessary wait time, this forum has largely morphed into a purely stat/biostat forum, so we don't have a lot of people who could reasonably help you choose a program. We usually direct people to mathematicsgre.com -- I don't see a whole lot of specific threads for choosing between schools, but you may have some luck anyways, and they do appear to have a decision making thread that might prove fertile.
  7. I would assume a joint program is more interdiscliplinary, but you can probably just set up an ad hoc visit with one school at a different time. Some people I know have done this when they couldn't attend a scheduled visit -- it may not be an option everywhere, but feel free to check first. Edit: I misread, so the date may not be the reason you can't attend both, but you still might be able to get some accomodations to learn about one if you ask. Apologies if I've missed the mark.
  8. Just look at the USNews statistics rankings, since they are combined stat and biostat, and then the biostat ones either say biostat or are located in schools of public health/medicine. Top 5 is Harvard, John's Hopkins, Washington, UNC, and Michigan, as you've seen. 6-10 are UC Berkeley, Minnesota, Wisconsin, and Columbia/UCLA/MD Anderson. Obviously these are just approximations: for instance, working with Marc Suchard at UCLA is potentially better than working with the average Prof somewhere else, and some places like NC State do lots of biostat in a stat department, but they're not a bad starting place. Edit: there are other places that are probably top-10 level (Brown I think, I assume Yale biostat as well) that are lower ranked because they are very small programs that don't admit many students each year. Other folks can give better info on that than me.
  9. I can't answer most of your questions, but at Georgia Tech (PhD in Ind. Eng, Op. Research), you can definitely go into statistics and be successful. C.F. Jeff Wu is a big-time professor, and one of his and V. Roshan Joseph's students at Georgia Tech just interviewed at UC-Berkeley, Penn, Duke, Columbia, Michigan, and NC State among other places last year (after a year of postdoc-ing with them, but still) for tenure-track jobs in statistics. I'm less aware of how good the professors are outside of those two, but at least in cases of working with the big name folks, you wouldn't have to compromise on statistics too much, and I'm guessing it would be hard to do much better.
  10. Would we consider TPE2 more advanced than Casella and Berger? A friend of mine at Duke says that their inference class is straight from TPE2 rather than Casella and Berger, and I'm not really familiar enough with both books to judge difficulty.
  11. From Duke's visit day, their situation seems similar to what you've heard about Harvard. A recent poster suggested that some professors may have preferences for students with stronger math backgrounds, but I'm guessing you wouldn't be at much risk of missing out.
  12. NC State had a visit on 2/22 last year as I recall, so I would expect them to have a visit this year as well.
  13. @MrSergazinov They are both good so far as I know. However, I recall Dr. Katzfuss (at TAMU, incidentally an Ohio State alum) was hiring a computational statistics postdoc this past year, so maybe he would be a good first person to start researching. He does Kalman filters and spatial stats, I believe.
  14. I'm not the person to give the full story, but they definitely have some quite strong professors -- look up Cun-Hui Zhang for an example (he does frequentist regression research, if memory serves). You could do well there, although with lower ranked programs who you work with is usually a little more important than at higher ranked programs.
  15. My math background was pretty similar to yours; it was pretty light, and I'd taken linear algebra my first semester of undergrad as well. Bayessays pretty much hit the nail on the head for me, since I got waitlisted and admitted at NC State, and was likely waitlisted and admitted at Duke. You went to a better school, and did better in Real Analysis, so you might end up being marginally stronger. Even so, biostat programs are definitely places to look -- just as an example, UNC biostat has Dr. Kosorok doing machine learning, and lots of statistical genetics folks that might mesh well with you. Beyond that, I'm sure folks can help you with specific fits as needed. I recall Cornell being good at high-dimensional statistics, which meshes pretty well with your topics, so it makes sense that your professors and Bayessays recommended them. Wharton takes very, very few domestic students each year (and very few students in general), so they're a tough for most people to get in, hence why applying there as a reach might not be as helpful as you'd like. As to your other questions, you don't need to retake the regular GRE. Unless you really think you would knock the Math GRE out of the park, and it would be a really clear benefit to you, then I wouldn't bother either (provided you aren't applying to Stanford, of course, in which case you'd need it). The theoretical/applied split is hard to tell, but you can pick up some ideas of it in past posts on these forums, or else just by asking. UNC statistics seems to have a reputation as pretty theoretical, and being good at stochastic processes and whatnot. NC State would tend to be more applied. Places like Columbia and CMU probably fall somewhere in the middle, and Duke might fall a little more applied than those two. Ultimately, it's a case-by-case basis, though, and just because you might go to UNC statistics doesn't mean you couldn't work with somebody on, say, Kalman filtering and data assimilation for numerical weather prediction. (final note: Bayessays has been around rather longer than I -- if they or another older poster argue with anything I say, I'd take their answer above mine)
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