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trynagetby

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  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
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