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trynagetby

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Everything posted by trynagetby

  1. There's nothing wrong with an LAC! I think your list is still reasonable , if pretty top heavy. The PhD courses definitely help a lot and I think that's a reason why you have a good shot at those programs, but generally the rule is the more math you have as an undergrad the better. Honestly, the biggest factor will be the strength of your recommendation letters. If you think your letters are top-notch your list is probably good. Take my advice with a grain of salt as I only applied to 2 biostat schools, and got into 1.
  2. List seems pretty reasonable. Just posted to say that if you're interested in doing research/pursuing PhD afterwards DO NOT even apply Columbia Stat MS. Also your GRE is probably fine.
  3. If you've had a lot of advanced mathematics background (Real Analysis I, II, Proof-Based Lin-Alg, + one more advanced class like Analysis of Algs, Abstract Algebra, Numerical analysis etc.. ) those schools are definitely all well within reach. If you've just had Real Analysis or even no Real Analysis you have a dece chance at all those schools but a clean reject from all of them is very well in the realm of possibility (I don't know anything about BU). Given your locational preference those schools pretty much cover the northeast. I'd throw in JHU too if Maryland isn't too south for you.
  4. I think you have a pretty good chance to get into them. I'm don't know much about those programs, but in general for domestic students the competitiveness of PhD programs falls drastically once you get out of the union of top 30 Stat Schools and top 30 general schools.
  5. If you got A's in Real Analysis/Applied Analysis (which I'm guessing is like baby functional analysis?) I think you should apply to the range of NCSU/Wisconsin Madison/UIUC/Rice/Texas A&M and below. NCSU/Wisconsin might be a little of a reach but its pretty possible :). I'd also think about applying to programs like Umichigans Biostat Masters (which is funded) to give yourself a leg-up before going PhD. If Biostats is an option, UNC biostats and below should be a good chance. Good Luck!
  6. To be honest, I think applying to Finance Departments/Business schools/Operations Research will be better of a fit.
  7. I think you're tremendously underselling yourself. That's a whole lot of math, and not having life sciences coursework doesn't matter at all (I got an offer from Harvard Biostat and I had zero sciences background). Biostat at UW-Harvard-JHU might be too tough to crack, but I think an application to like UNC-Michigan would be worth it and I think you are very competitive at a place like Minnesota. For Statistics onwards I'd apply to a few schools at the level of Duke-UW-Michigan then work your way down to some safeties with the bulk of your applications in the USNR 20-40 range (I think Rice is
  8. No worries, you can scroll down to my post in this thread: Good Luck!
  9. Check out my profile from way back which I think is similar to yours but less OR focused. I also applied having no idea what OR was like and I got into Northwestern and GaTech ML (ISYE) with special fellowships for both. I only applied to the two because my main interests was Statistics PhDs. I think you have a good shot at all of them except MIT because, well, MIT (still worth applying IMO tho)
  10. Harvard, Berkley, UW Stats all have at least 3 very top/rising star type people doing causal inference research. An important distinction you have to make is whether you want to do "classical" causal inference (propensity scores, average treatment effects, instrumental variables, potential outcomes framework) or "modern" causal inference (dags,judea pearl causal discovery, reinforcement learning, adaptive designs etc...). Both are pretty hot right now but the flavor of research is extremely different.
  11. If the OP has taken the equivalent of Math 55 he/she should definitely go for it. Harvard's the odd case as their undergraduate pure math first year math class literally has its own wikipedia page lol https://en.wikipedia.org/wiki/Math_55
  12. I'd say depends on the professor/class. If your Real Analysis class was super basic and your Complex Analysis class was like Stein and Shakarchi or Papa Rudin, I'd pick Complex Analysis. But if they're similar and you got into a competitive REU, you know apriori that the rec has to be somewhat decent right?
  13. I'd recommend watching this NIPs lecture from Robert Tibirishani https://www.microsoft.com/en-us/research/video/invited-talk-post-selection-inference-for-forward-stepwise-regression-lasso-and-other-procedures/?from=http%3A%2F%2Fresearch.microsoft.com%2Fapps%2Fvideo%2F%3Fid%3D259617 to get an idea of the type of problem that Statisticians are interested in and the inference/prediction ML distinction. It just seems you're not interested in statistical inference. There's nothing outdated about inference. It's just a goal that the statistics field is interested in, and the tools to achie
  14. Idk man, if you think you can get what you want out of a Statistics PhD then go for it. But it really doesn't sound like you'd enjoy the curriculum or research focus. Im quoting the first line out of the syllabus for the last course in Stanfords Statistical Inference Sequence: Testing problems in high dimensions: sparse alternatives (needle in a haystack) and nonsparse alternatives, Bonferroni's method, Fisher's test, ANOVA, higher criticism. Even CMU which is really MLey out of all the statistics departments requires to review topics like simple linear regression, ordinary
  15. Both would be important especially for NYU datascience, especially a formal AoA class (Dynamic programming, Graph Algorithms, basic NP-Completeness proofs) because professors need to know that you can reason rigorously about the complexity and correctness of novel algorithms. It seems like you're more interested in developing bioinformatic pipelines that use ML techniques than developing specific ML methods (at least in statistical sense). In modern statistics, optimization and modern techniques are important, but ultimately you have to prove that these techniques give good inf
  16. Parroting @StatsG0d point, I think you're really on the wrong forum. The people in this forum are fundamentally interested in statistical inference and probabilistic modeling. NYU DS (I have researched the department extensively, and even wrote a specific SOP for it and then I realized I wasn't a good fit after I realized how bad the SOP was) and what you seem to be interested in are more in developing computational tools that push the bounds of what is learnable. Rather than being concerned with proving consistency/convergence or statistical estimation problems they're more interested in solv
  17. NYU DS admissions tends to be more focused on research track record or if you go the mathematical maturity route, you have to be extremely mathematically mature. Just a note, NYU DS is very computational NLP, Neural Network type stuff, (or super theoretical ML/Computational theory). This is very different from your standard Biostats PhD/Stats PhD track.
  18. Depends on your matsers institution. If your masters at was like UIUC/UCDavid/UTAustin/Rochester type of schools and your recs are good I'd say apply to most of the Top 10 why not, you have a great math background. If it was at like wakeforest, you should be more conservative.
  19. I think for biostatistics you just have to be more discerning when on the job market. There are definitely a lot of advanced pharma/biotech jobs that do super cool things. But I am 100% certain that out of the jobs that require biostat PhD there are waaaaaaaaayy more jobs that are your "check the pvalue/regulatory mess" than your Apple Health research AI type stuff. The sinecure statistics jobs are probably less prevalent for standard PhD statistics positions because getting a Statistically trained PhD to perform simple methods is only profitable and necessary in pharma/biotech. Th
  20. Word of warning, UT Austin is way more competitive than its rankings would indicate (probably as competitive as like TAMU/UNC/Winsconsin/NCSU).
  21. I'd add an opinion that on some things UCLA is stronger than UW, so I don't think its as one sided as the forum would say (this forums tends towards the standard statistics crowd). If you're interested in the more CSey side of Statistics (ML, Bayes Nets, HMM, Computer Vision, Generative modelings etc...) I think UCLA is stronger, especially now that Emily Fox left for Stanford. Still for the record I voted for UW.
  22. Marrying a Upenn Med School doctor while in PhD could effectively quintuple your stipend, so I would seriously consider this perk if you got game. In all seriousness though, if you're sure you want to do genetics Michigan is the best program hands down. I've talked to friends at JHU Biostats, visited Umich Biostat as part of an REU, and talked to Profs at Harvard biostats visit day. Every one of them mentioned Michigan Bio-statistics Department as being the dominant program in statistical genetics. About working with Doctors in Med-School and such, I wouldn't worry about it as the on
  23. Your tier of choices are way above my pay grade, but I'll throw in my two cents that it seems that Stanford doesn't seem to be the best choice. I'd recommend taking a look at their stats phd dissertations to see if you're interested in that type of work. Stanford posts dissertations publicly.
  24. I think you can check out my profile and results for a comparable example. I had a little more math and a lot more research than you, but also a lot of Bs, plus your school's reputation is slightly higher than mine. I also really only had 1 strong rec letter and a rec letter where I just asked a prof who taught a class I did well in. I still got into the lower top tier range of Stat PhDs (UMich, UW, Duke,etc..) and I suspect that you would too (at least one of them if you applied to all). Cracking Harvard/Berkely/CMU will be hard but you should definitely apply. In terms of classes, I'
  25. As someone who applied to both programs and got into some of both, I would pay more attention to the type of work that professors in Bio-statistics and statistics programs do and the coursework rather than the label. Biostatistics programs generally do more applied/methodology work that is focused on being immediately applicable to biomedical problems (hypothesis testing, Causal inference, etc...). Statistics programs generally do a wider range of stuff and if you want to do crazy theory like crazy asymptotic statistics or high-dimensional theory then you'll likely only be happy in a statistic
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