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icantdoalgebra

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  1. Upvote
    icantdoalgebra got a reaction from bayessays in Profile Evaluation for Fall 20201 Biostat PhD/MS   
    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. 
  2. Upvote
    icantdoalgebra got a reaction from Casorati in Profile Evaluation For Stats Phd 2021 fall   
    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. 
  3. Upvote
    icantdoalgebra reacted to jelquiades in Laptop suggestions for math/statistics grad schools   
    I highly recommend the above. A $28.5k video card will be necessary for rendering ggplot outputs.
  4. Like
    icantdoalgebra got a reaction from lpruj in Applying Early to Programs   
    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.
  5. Upvote
    icantdoalgebra got a reaction from bayessays in Applying Early to Programs   
    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. Like
    icantdoalgebra got a reaction from harry_stats in Machine Learning - Statistics vs CS PhD   
    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.
  7. Upvote
    icantdoalgebra got a reaction from ZNtheory in Machine Learning - Statistics vs CS PhD   
    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.
  8. Upvote
    icantdoalgebra got a reaction from bayessays in Machine Learning - Statistics vs CS PhD   
    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. Like
    icantdoalgebra got a reaction from distopianmathgirl in Fall 2020 Statistics Applicant Thread   
    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".
  10. Like
    icantdoalgebra got a reaction from Stats MS to PhD in Does extracurricular activities matter at all for PhD admissions?   
    You can check out this article: https://stattrak.amstat.org/2016/02/01/gradadvice/
    In it Professor Banks provides the answer: 
    "Sadly, many undergraduates think having multiple majors and minors is impressive, as is leadership in various university clubs, participation in sports, summer research experiences, and volunteer work for the community. But the admissions committee only cares about whether the applicant will thrive in its department’s PhD program in statistics.
    All the other résumé padding is at best irrelevant, or perhaps even evidence of distractability."
  11. Upvote
    icantdoalgebra got a reaction from noinim in Fall 2020 Statistics Applicant Thread   
    Duke released decisions.
  12. Upvote
    icantdoalgebra got a reaction from ENE1 in My two cents tips on PhD application for STAT   
    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. 
     
  13. Like
    icantdoalgebra reacted to J456 in 61% GRE Math Subject Test for PhD in Statistics   
    Hi,
    I took the GRE Subject Test and got 61% (710). I’m disappointed as I was aiming for a high 700’s, which would have gotten me around 75%-80%, and the difference between that is only 7-8 problems (the curve is very steep in this range). I definitely could have cracked the 70% on this version of the test, but this test, by its nature, isn't meant for someone like me.
    Do you think this is a respectable score for the Statistics PhD programs? I know only top programs either require or recommend it. I’m going to sit for the test again in a couple of weeks though and see if I can take it up to 70%. Only top schools like Columbia, Chicago etc. recommend it, and all no one but Stanford requires it. I have a 4 top 20 schools in my list.
    Here's my profile:
     
     
    Schools on my list:

    Berkeley
    UChicago
    Columbia
    UNC Chapel Hill
    Minnesota
    UC Davis/Purdue
    Ohio State
    Boston University
    Michigan State
    UConn
    UPitt
    Texas A&M
    University of Iowa
    Colorado State
    UMass Amherest
  14. Like
    icantdoalgebra reacted to bayessays in Profile Evaluation: PhD in Statistics   
    I'm sure you'd get into a top 10 program if you applied to all of them. I'd also probably apply to a couple of the bigger state schools ranked 10-20 like NCSU, PSU as relatively safe options. You won't have to go lower than that.  I wouldn't worry about the third letter.  If you have two strong ones from people who know you well, a letter just saying you are good at math won't hurt you.
    If you're dead set on going to Stanford, you'll need to take the math GRE, but even schools like Chicago don't really require it, so I don't think there is any reason for you to delay applying unless you would like to take a year off for fun. 
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