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  1. As an aside, I don't think he counts as international since Samao is an American territory (similar to Puerto Rico). He would actually probably be considered an URM American citizen? Although OP should fill out these details, not me.
  2. Be careful with directly contacting ML professors since they usually have somewhere on their website that they won't respond to inquiries from prospective Ph.D. students. However I will throw this out, I think the acceptance for the Ph.D. program in statistics at Berkeley is roughly around 10%; the most recent numbers I have for the EECS admission are from the class entering 2017, where there were 3000 applications for 40 spots, which essentially an order of magnitude lower (however I may be remembering incorrectly since its been a few years since I've heard this number from a professor), and I would imagine that the schools that have strong ML and stats programs (Stanford, Washington, CMU) have a similar story.
  3. To add onto this thread, I had asked one of the professors at my institution (UC Berkeley) what sort of is the most important things in considering an admitting an applicant and he stated (in order of decreasing importance) 1. High GPA while taking many tough technical classes (math being the most prominent, but I got the vibe that courses from physics, computer science and other "difficult" majors are considered fine). He had also emphasized that most of the grades should be A or A-, but a B here and there would be ok. 2. Strong letter of recommendations from faculty members, but having one from industry should be fine (although I think he meant if its a research position that publishes papers). A point made by another professor echoes what cyberwulf has said, that perhaps the strongest reasonable letter they can write (apart from a Nash-like letter saying "this person is a genius") is comparison to either past students that went to strong schools or to current stats PhD students (although this might only apply to schools with already existent strong statistics programs) 3. A good statement of purpose that highlights why you would be a good fit for that specific program and statistics in general. Another professor advises students against writing too flowerly or over-the-top since the people reading them are busy professor members so they would rather see you get straight to the point.(Additionally if you write about how you began to love statistics as a little kid they would not believe it) After interacting with a decent number of PhD students at Berkeley, I've also noticed that its not very common for the PhD students to have prior statistics research experiences (and a professor has confirmed for me that its usually rare to see students with prior theoretical statistics research experience), but they will have had research experience in other fields, and they usually will have a publication by the first year of PhD in their undergrad research (although this is likely factored into the letter of recommendations). A different professor had told me, in regards to GRE scores, that the quant section really should be easy for anybody looking to enter Berkeley (and I can confirm that the first year core classes are very mathematically intense here; the second semester theoretical statistics course makes Casella and Berger look like a walk in the park), but the verbal section does matter and they will still look at it.
  4. Sorry for letting the thread die without properly thanking you. I just had a few more questions I wanted to ask. Updated Test Scores: Math Subject Test: 840 (84th percentile) GRE General: 170Q/168V The first question I have is should I submit my GRE subject test scores to the schools I'm applying too that don't require but recommend it? I doubt anybody would question the rigor of a Berkeley math degree but the score isn't exactly high enough to impress anybody. The second is with regards to a proper school list: I'm likely going to apply to (but still subject to change are) Stanford, UC Berkeley, Harvard, Washington, Chicago, CMU, Duke, Michigan, NCSU, and PSU but I was wondering if this was a bit top heavy of a list. The third is with regards to the personal statement; obviously I should talk about the research I'm currently doing within it since its going to be submitted likely to a top journal and will at least be on arxiv before I apply, but I'm not too sure if I want to continue in doing work in BNP (although I did somewhat enjoy it) when I'm a grad student and would likely want to branch out into other areas. How much should I discuss this within my personal statement? (Albeit this is in some sense a really bad question and probably something people don't really think about often) The fourth is that when I often talk to my professors about which schools are strong within what areas of statistics, they usually only mention peer institutions to Berkeley (namely top 6 schools or so). What are some of the other schools that aren't ranked as highly but are still strong in high-dimensional stuff, nonparametrics, selective inference (like FDR control and stuff), and robust statistics (M-estimators and such)? Thank you very much for your time!
  5. I'm going to be a senior in the Fall and am interested in applying for Statistics PhD (most likely 2020 cycle but a few questions on that). Undergrad: UC Berkeley Major: Math + Computer Science GPA: 3.96 Student Type: Domestic, Asian Male GRE and Math GRE haven't been taken yet Courses: Math: 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) Statistics: Probability Theory (A+), Mathematical Statistics (A+), Theoretical Statistics I, II (for PhD Students, A, A), will take grad probability in the fall Computer Science: 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 some theoretical stuff, pushing to get it published in a top tier journal (I contributed in a somewhat nontrivial way, deriving some lower bounds) 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 one of the big names at Cal who is very busy and I rarely spent time with him so its hard to tell), 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 I don't have a good choice for the third one (that's part of the issue) I have two main questions here: 1) As I still need to take the GRE, and missing a recommendation letter, should I delay applying by a cycle? My thought were that I could spend my final year doing more research (and having some publications), getting a better letter of recs by interacting with the professors some more, and have extra time to prepare for the GRE. I was wondering about the marginal benefit of waiting a year and how much that would help my application. 2) What schools should I consider for my safeties if I do decide to apply this cycle? I want to apply to the top 10ish schools, but I don't know what else should I be considering.
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