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About icantdoalgebra

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  1. As recommended go check out the mathematicsgre forum, there's a lot more people there involved in math. But I will say that I know many people with applications that are quite a bit stronger than yours (people at schools like Berkeley, Stanford, and Harvard) with higher gpas, 5+ graduate courses (some even reaching double digits!), multiple high-quality REUs, GRE scores in the 90+ percentiles who weren't able to crack programs like Princeton. Your recommendations seems like a bit of a wildcard but I can imagine a situation where if your profs call you brilliant in the rec letters you may get in. If you have some sort of story about overcoming adversity with your low CC GPA and turning it around when you transferred, manage to get a pretty high score on the subject test I don't think Berkeley is an impossibility, but still a reasonable reach. You should take all my advice with a grain of salt though as math is not my area.
  2. I think you should clarify if its a school known for grade deflation or not. If its UChicago or Caltech the response might be different than if it were say Harvard or Yale. I would be overstepping my expertise if I tried to tell you what schools are a target vs reach but I could imagine the answers changing. Maybe others can comment on this perspective.
  3. You still have roughly 5-6 months before you need to apply; you can find a research opportunity in statistics at least by the time the fall semester starts, and doing a semester of stats research would get you a pretty strong rec letter. It shouldn't be that difficult to do considering your grades and the fact that you are at an Ivy league school. Usually domestic students don't have meaningful theoretical research (although this is becoming less and less true) before applying to a PhD program, but international students at the top programs often do (to be fair I've only interacted with those at Berkeley but I'd assume it be similar at peer institutions). If you think that taking a gap year means that you get to apply later and have an easier time getting in due to Covid being over, I'd say just apply this year; there's really no guarantee things will be better. If you can do something in the gap year that would greatly improve your application, like a serious theoretical statistics research opportunity, its not a bad idea.
  4. I would second the Macbook if you aren't that tech-savvy, as having a Unix based OS is much easier to deal with than a Windows one for anything programming related. If you don't like Apple, then an option is to dual boot Linux on whatever Windows machine you buy. For anything that requires intensive compute, your university will likely either have a cluster or provide you with cloud compute credits, in which case you would need to ssh into and having a Unix OS makes this process significantly less painful. The Windows Linux support IMO is frankly quite awful and package management/installing new programming libraries is also horrendous on Windows. Also second the insanely expensive graphics card, think about how fast your ggplots will render with real time ray tracing.
  5. As an aside I think there are benefits to applying later in the cycle (especially as late as possible) if they don't mention that they do any sort of rolling admissions, as you can take the fall semester to improve your relationship with the professors writing your rec letters. I say this because I managed to get a research opportunity late August and the recommendation letter I got from that definitely strengthened my application profile considerably, but it doesn't have to be research. You could try and initiate a DRP with a professor writing you a rec letter or be a TA for one of their courses.
  6. 210B just requires some basic martingale stuff from 205A (sometimes not even). I took 210B without 205A/B and I was fine. The reason I recommended 210B is that you can get 205 anywhere: every measure theoretic probability theory course will cover basically the same material. However 210B is really a Berkeley specific course and you would have a hard time finding a similar course elsewhere; for example Berkeley's Stats is really big on non-asymptotic results and 210B introduces a lot of the tools and techniques for non-asymptotic results so you would get a decent idea of some of the research approach that people in Berkeley specialize in. I think that if you have an opportunity to take this instead of 205, which you can just take the equivalent at any university you go to, you should.
  7. Agree with most of what's been posted above before: you should probably be thinking about applying to PhD programs rather than masters. I'm currently a senior at Cal and you have a pretty similar profile with me and I got into several top PhD programs in statistics. However I'd be a bit careful this coming application season since of the Covid-19 induced recession, it seems like schools are planning for smaller cohort sizes (a rumor that I've been hearing going around but unconfirmed) so it might be tougher to get into top programs compared to last applicant season. I'd swap out 205 for 210 though; primarily because the content you see in 205 you will be able to see anywhere else, but the content covered in 210B is very unique to Berkeley and I think you really would be missing out if you didn't take advantage of the opportunity to take it while you are still at Cal. PM me if you have any further questions.
  8. Actually the criteria for getting into ML are changing somewhat, or at least for Berkeley. Part of the reason why the publishing requirement is so high to get into PhDs is that the bar for publishing in a top conference like ICML or NeurIPs is far lower than publishing a paper in a top stats journal like AoS, JASA, or JRSS-B. ML has this reputation of being a field where you just have really large labs and just churn out as many papers as you can every year (Levine at Berkeley who does RL research submitted 40+ papers to a conference on RL and had I think 24 of them accepted). This creates a issue with the people who review the papers, as there simply is too many to review each one thoroughly. Berkeley to an extent has recognized this problem (one professor even told me that conferences in ML are filled with "garbage" papers) so they now value letter of recommendations far more than publishing record. But again, usually the best letters of recommendations comes from Professors who you've done research with. As for Stats vs ML, I think you should apply to both programs where you can. For example, CMU lets you apply to multiple programs so there's really no reason not to do so (unless the financial requirement creates undue hardship). Even if you have to choose, a lot of schools have faculty in multiple departments because they recognize that the work is very similar and you can work with these faculty members regardless of department. Although it also depends on how strongly you want to do ML, as you would be hard pressed to find statistics faculty that aren't joint CS professors who do deep learning; everything else you would be able to find in a statistics department.
  9. Penn is a very small program; I think their cohort size is 5 a year so their department might not be as strong as say, Michigan or CMU, but getting in might be as or more difficult because CMU and Michigan have cohort sizes of about double that. Also in general, Ivies are harder to get into relative to their departmental strength due to their name outside of statistics.
  10. Agree with the above; your profile seems a bit weird to me. Analysis and biostats are really far from each other although you do have another year to figure out roughly what you want (but you shouldn't try and do both) In addition if you are applying to math programs in analysis, (and want to continue doing PDEs), I think UCLA is a pretty egregious omission I personally didn't go through math graduate admissions, but a lot of my friends did so I can say a little bit through that. You should probably apply to a broader range of schools than just the top few: I know people who took 8+ graduate courses at Berkeley (which is, according to USNews, a top 5 math program) with all As and still had a hard time cracking the Princeton/Harvard/MIT/Stanford tier of schools. I think there is also a tendency for math graduate schools to reject their own undergrads, since its usually encouraged for undergrads to go somewhere else for their PhD.
  11. I think real analysis is a good choice, although I think that Rudin is awful textbook to self-study from, especially for the first time. Rudin really shines if you've already learned real analysis and you want to go over it again (and I think many should do this, because mastery doesn't come from learning something once, but reviewing the material over and over again). For a first time introduction to real analysis, and you don't have a really strong math background, I think Ross is the standard textbook. For an honors level introduction to analysis I would highly recommend Pugh since it is filled with pictures that help illustrate many concepts.
  12. Undergrad: UC Berkeley Major: Computer Science GPA: 3.96 (4.00 major gpa) Student Type: Domestic, Asian Male GRE: 168V(98)/170Q(96)/5.5W(98) (percentiles might be off) Math Subject: 840 (84) Courses: Honors Linear Algebra (A+), Honors Abstract Algebra (A), Honors Real Analysis (A-), Honors Complex Analysis (A+), Numerical Analysis (A+), Grad Analysis I and II (A, A), Probability Theory (A+), Mathematical Statistics (A+), Theoretical Statistics I, II (for PhD Students, A, A), Probability for PhD Students (A-), Intro to Programming (A), Data Structures (A), Discrete Math and Probability (A+), Intro Computer Architecture (A), Databases (A), Algorithms (A), AI (A), Machine Learning (A) Programs Applying: Stats PhDs Research Experience: Currently working with a professor and his students on Bayesian nonparametrics, trying to wrap it up and publish soonish (likely second author, contributed some theoretical and numerical results), also working with another professor on more applied machine learning research, writing code to implement it as well as other potential modifications and applying it to more data Work Experience: Spent a summer at a startup doing some natural language processing, another summer at a quantitative asset management firm Letter of Recommendation: Professor doing research with (hopefully would be decent, but met him twice in person in the past 6 months), another professor that I took Theoretical Statistics with (will also hopefully be decent as I think I stood out a decent amount in that class, actively participating and going to office hours), and the third professor is the also the one I'm doing research with (but I've met with them once) Results: Stanford: Waitlisted UC Berkeley: Haven't heard back, talking to some people on the inside, seems like a waitlist Harvard: Rejected UW: Ghosted, likely rejected Chicago: Waitlisted CMU: Accepted Duke: Accepted Michigan: Accepted NCSU: Accepted (Declined) Columbia: Rejected Miscellaneous Points: I think Duke was really interested in the research I did and that probably helped me out a lot. The main boosts to my application are probably taking a lot of hard math classes, as well as the PhD statistics classes at Berkeley, and doing research with some pretty big names in the field. My main failings probably came from the fact that its fairly hard to establish connections with professors as an undergraduate at Cal, and after trying to get involved in research for about 5 semesters straight I only managed to get it the semester when I was applying to PhD programs. Best of luck to anybody reading this from the future who are also applying!
  13. Usually a strong math background means something like a combination of real analysis, linear and abstract algebra as well as more advanced "pure" mathematics classes. However depending on the program "advanced calculus" might be equivalent to real analysis elsewhere. so you'd need to use your judgement on that.
  14. Echoing some of what bayessays has mentioned, their department is on the smaller side in terms of traditional statistics but he also has a high opinion of Michigan assistant professors; he's mentioned that they've made some good hires over the past few years. Also one piece of advice he gave that might be useful to everybody: when choosing a PhD program and considering potential advisors, it shouldn't be about only one person you want to work with. There should be some degree of robustness for an advisor: you should have multiple people at program you're happy to work with in case things don't just work out.
  15. In some sense, these ranking are also artificially imposed; I had a professor at Berkeley (who shares a name with a famous athlete) who recommended Michigan over Chicago despite what the rankings might seem to indicate. Rankings are a (pretty good?) heuristic but they aren't perfect; if you think that CMU is a perfect fit for you then take it with full confidence and don't feel the need to justify your decision "because of rankings".
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