MathStat

I know this has been asked before a little while ago, but I just wanted to gather some more opinions about the new M1 Macbooks. I need to ditch my current Macbook Pro 2018 due to the never-ending keyboard and display issues. So I am looking to see what's out there, specifically for deep learning work. I've been using Tensorflow a lot lately, but I anticipate I will at least need to run / play with PyTorch code in the future too, with PyTorch being so popular in the academic community. I know that the integration of Tensorflow and PyTorch for Macbook M1's has been iffy, but for those who are familiar with this, have these issues been resolved lately (at least to a reasonably satisfying level)? What are some common glitches / library crashes that occur? I've already pulled up numerous guides explaining how to set up these laptops for deep learning work, but I just want to be aware what I'm getting myself into, before I take the plunge. Also, how do the Python basics like pandas and numpy work on the M1 (I've been finding contradicting info online)? and is it true that the PyCharm IDE works slower than before? Thank you very much in advance. I know I asked a lot of questions, but even partial replies would be very helpful for me. And if you guys have any recommendation for non-Mac machines, please do let me know. Thanks a bunch again!

I know so many people at top programs who took 1, 2, 3 or more gap years before starting grad school that I lost count. Some of these people spend their gap years doing either nothing or working underqualified jobs such as barista, crepe chef, etc. Don't worry about it.

You are on good hands at Duke, best of luck.

@stat_guy The "Wharton/Columbia/UChicago/Princeton" placements at Yale come from only 2 professors, one of which is Andrew Barron, a potential advisor I had considered myself when deciding grad schools. You should determine very carefully how active these two professors are currently, how motivated they are, and whether they are willing to advise you for the entire duration of your PhD (i.e. won't retire in a few years). As I mentioned above, Yale does have J Lafferty, a point brought up all the time both in favor of Yale and as a drawback to Chicago ("Chicago lost Lafferty, so it lost some of its value"), but is Lafferty actually actively advising students nowadays? Furthermore, many of the good placements came from David Pollard, but I think he must have retired by now. Having 2 senior profs agree to advise you before you start is indeed very nice, so I completely understand your inclination towards Yale. If you are sure you want to do mathematical statistics, then go ahead. Chicago would indeed offer more diverse research options, including hot areas such as ML and, more recently, causal inference. For math stat we have Chao Gao who is recognized as a clear outstanding researcher and who likely will become tenured soon (definitely during your PhD time). Tengyuan Liang form Booth is also an excellent choice and he will also likely become Assoc. Professor soon enough. Chicago may not promise you a famous advisor from the start, but you can certainly have one later on, assuming there is a match in research interests. Your first year, you could work with a motivated younger professor, then potentially add a famous advisor later on. I encourage you to come to the Chicago visit day on Monday and ask us questions. I can answer questions through PM, as well.

Is John Lafferty still advising?

I think rather than discussing program rankings we should make an advisors ranking, lol, but I guess that's too delicate of a topic even for an anonymous forum.

Is MIT access really feasible as a harvard stats student? If so then yup, huge point taken.

Bryon Aragam from Chicago Booth did UCLA stats and I think he is a pretty great positive example. When I was applying 2 years ago, I also read bad things about UCLA on here as well....

Chicago over Harvard was a no-brainer given my research interests. Of course that Harvard is still an outstanding program, and turning it down is not easy either way. Chicago over Berkeley was a hard and excruciating decision, but I reasoned that there were only a handful of Berkeley professors I truly wanted to work with all of which are absolute superstars and who have millions of students and postdocs...Chicago was an equally good match for my interests, they have TTIC which is at least top 5 for theoretical machine learning, as well as a handful of superstars or rising stars in the Statistics department and the Booth school of business. Also, besides purely academic reasons, between very long winters and very high living costs, I decided I'd prefer the former, haha. But this is a purely personal preference, and I totally understand people who think otherwise. You should also consider Chicago and should come at the visit! We do have the notorious quals, as well as the coursework which take up all of the first year plus summer. This is not for everyone. Research-wise, I am still recovering from that, yet I do have two exciting lines of research going on...I guess I just need a few more months to be able to tell you exactly how it's gonna turn out.

Are Harvard grads getting jobs at Google, Microsoft, Facebook, etc Research? I have not been stalking recently lol, but I cannot recall any examples (I wouldn't mind seeing some if you know any!). And the ones who get the top academic placements (aka berkeley and stanford TT professorships) seem to have worked in causal inference. If that is a strong area of your interests, then sure, go ahead. I turned down Harvard (and had similar interests to yours and even a similar situation, haha), cause I thought there were only 1-2 people with similar interests as mine. another bit of advice when you have to decide between almost all the top programs is to not get hung up on the top 2 ones that are ranked just after stanford. I think other ones you should also consider carefully given your interests are Columbia, UPenn Wharton, Duke, Yale (only if you wanna do pure math stat; imo they're some of the best at that). Make sure you review these carefully as well.

how is harvard a good fit given your research interests? I feel like they're more into biostat/applied stuff..although Cynthia Dwork is there... If you're into probability, deep learning etc, then berkeley and potentially other schools out of those 24 would be better fits.

Hi everyone, I would like to re-ask a question that I may have asked before in some comments. Please indulge me in this repetition. I would be very grateful for fresh/updated perspectives, too, especially from current professors (@StatAssistantProf @cyberwulf). I'm a student in my second year at a top 5 (or top 3?) stat PhD program (according to US news, but who knows). I would like to decide whether I'll pursue the industry or academia path by the end of the year. Since I've always liked theorems and proofs more than anything else, I may have an academic predilection. Now for the issue: I would really appreciate some candid/brutally honest comments on being the first student of a young assistant professor, but who certainly is a rising star. Of course, we all know about the tradeoff between rockstar/influential advisors who are incredibly busy and younger, enthusiastic, friendly, hand-on professors, yet who may not be able to help you that much on the recommendation letters front. Assuming the latter person is what I consider the best fit for me here as an advisor, then is it even worth bothering to pursue academia? Is there a way to feasibly make this work (any examples that you know of) or should I just forget about it, work on something cool for the next 3 years, then move on to something else? Thank you very much.

@stemstudent12345 Sorry you're going through that. A bit of pragmatic advice: Just find a prof or two who support you, that's all it takes; cram the patterns of past quals. Not worth wasting your mental health on this, *really*. You'll be done with quals soon enough and you'll be able to pursue your own exciting and beautiful research, and from then on nothing else will matter. @Stat Assistant Professoris spot on.

I am also starting to discover the connections between probability -> statistical physics -> spin model -> quantum field theory (arrows not necessarily in that order:P). Chatterjee at Stanford is more of a mathematician in my eyes, not really a statistician. But yes, interesting to think of the side of stat that comes from physics. And be able to use it to approach ML from a different perspective.

Basically the title. I know many prestigious finance firms ask for GPA, including major GPA, and sometimes transcripts, even for PhD students. Is that also true for tech companies? Especially for, say, competitive roles such as Google or FB data science? I know many people say grades do not matter anymore after you have passed your qualifying exams and advanced to candidacy, but I would like to double check this, perhaps with people here who have actually been through the process of industry job applications. As a UChicago student, you can all understand why I am asking this ?. Thank you so much!!