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trynagetby

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  1. 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 Uwash-Harvard-JHU might be too tough to crack, but I think an application to like UNC-Umichigan 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-Uwash-Michigan then work your way down to some safeties with the bulk of your applications in the USNR 20-40 range (I think R
  2. No worries, you can scroll down to my post in this thread: Good Luck!
  3. 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)
  4. Harvard, Berkley, Uwash 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.
  5. 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
  6. 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?
  7. 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
  8. 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
  9. 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
  10. 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
  11. 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.
  12. 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.
  13. 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
  14. Word of warning, UT Austin is way more competitive than its rankings would indicate (probably as competitive as like TAMU/UNC/Winsconsin/NCSU).
  15. 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.
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