Lilly187

For Harvard, if we haven't heard anything do we just assume rejection?

Thank you so much for this insight, it was extremely helpful! I was feeling so unsure and was about to email the grad committee for clarification, but I imagine emails of that nature can overwhelm the system. I will force myself to sit tight until the end of February lol.

People are updating admissions for berkeley stats! ? So if we don't hear is this bad news, or does berkeley do batches?

Is it possible to get accepted to Berkeley or Harvard without an interview (for stats phd)? There are a few interview invites on grad cafe, but seems like quite a small amount if they intend to interview everyone they are considering for admission, and seems to be coming from POIs rather than adcom (so maybe certain POIs like to interview and others don't?)

That was extremely helpful, thank you so much!! For whatever reason I found myself feeling really overwhelmed, so I appreciate the informative response I understand if you don't know the answer to this, but would you recommend including courses that are in progress but not graded yet?

The Berkeley app says, "include a descriptive PDF list of upper division and graduate level math and statistics courses you have taken." I am a Canadian student, so my courses are numbered a bit differently, but from google this seems to mean third and fourth year courses? However, linear algebra was a second year course, and I assume they would want that included in the list? Or maybe not? I'm just a bit confused, should I exclusively include third and fourth year courses, or also second year courses like differentials and lin alg, since they probably want to see those (but I guess they're on my transcript either way)?

I plan to apply for stats PhDs. I've never taken a compsci course, but I have experience coding in python via curricular projects/research. Still, I've never taken a course to develop a strong foundation in python, so I decided to enrol in a first year course (Intro to Programming). Thing is, most top Stats programs ask that you be fairly strong in programming. If they see I'm only taking this course now, will it actually be to my detriment? Even if I stress that I've gained significant coding skills through my research, will they consider the course a sign that I actually do not have the necessary coding skills/am not taking advanced enough courses (the other courses I'm taking this year are third and fourth year level plus a thesis that will involve coding in python). Sorry, I just am a little stressed and wanted some clarification. Thank you!!

Thank you for the insight @SPIWizard! That's really good to hear I have a few questions if you don't mind: 1. Did you contact profs at Canadian schools before applying? I used to be under the impression that this was expected, but for stats it seems like it's not? 2. If you don't mind me asking, what school did you attend, and did you enjoy it! My main concerns for grad school are: (1) being in a department which provides a sense of community/support and (2) finding a prof who has some interest in AI safety or at least ML. Do you have any advice on which Canadian schools might meet this criteria? (These qs are a bit long so please don't feel pressured to answer) Also side note: (this ties in with the above and others might be interested to know) Someone mentioned to me that there's almost no difference between masters -> phd and undergrad -> phd time-wise, since the research experience you gain in masters will make you faster as a phd. So if anyone else is wondering about the difference (time-wise), the advice I got was to not really factor that in, it'll be negligible compared to other differences you should consider to figure out which route is best for you.

Thank you for all your helpful advice on this thread! It's really nice to know a reasonable approach/expectation when it comes to the top 20 list and applying as a Canadian Especially when application fees can be a big pill to swallow. I will give the Berkeley/Harvard dream a go with fingers crossed, and then of course apply to good Canadian and US schools (thanks for suggesting University of Washington/Carnegie Mellon, I hadn't looked into them much).

Perfect, thank you so much! This sounds like a great game plan and is probably exactly what I'm going to do It's a bit hard for me not to get my heart deeply set on things, so I'm going to have to really keep my mindset realistic and grounded lol. Thanks again for the advice.

Hi everyone! I'd really like some advice as to whether I stand a chance in applying to some top 20 programs. I will provide as much info as possible. I am Canadian and currently completing my undergrad(will be applying to US unis). I am doing a BSc. with a specialization in math and stats (I have more science courses than a math major would since they were required by my program, not sure if this puts me at a disadvantage compared to someone with more math courses). I am extremely interested in Machine Learning, and more particularly, algorithmic fairness and AI safety. As a result, I found some profs whose work just blows me away, but sadly, they are at fantastic universities . I'd like to know how much hope I can realistically hold without being completely delusional. Student Type: International (Canada) Female Caucasian Undergrad: Canadian University Major: General science w/ specialization in math and stats GPA: 3.9 Relevant Courses (what I have and will have taken): Linear Algebra I and II, differentials, cacl I and II, probability, mathematical statistics, intro to data science, regression, general linear models, real analysis, abstract algebra, time series, complex analysis, computational methods for inference. SIDE NOTE: in my first semester of fourth year I will be taking Intro to Programming (first year compsci). I have previously taken courses working with R, but never python. I used python extensively to do a deep learning project in my third year, but thought a course would be good to learn a foundational and systematic approach to python. Will schools think it's weird I'm taking this course so late in undergrad? GRE: Am taking it in September. Will update when I do! Apologies, I hope it is still possible to assess my application barring this info ? Research/work: Summer after first year: research in a health geomatics lab, worked with Netlogo to make an agent-based model (looking back, it is not an impressive model by any means lol but learned a lot) Summer after second year: was meant to do statistical analysis for a geography lab, but could not get access to a dataset. Ended up doing a lit review (which will hopefully be published this year. Waiting on journal decision). This would be a first-author publication, but in the topic of aging (nothing to do with statistics). Not really sure how this will be perceived. If you're wondering where the math is, I promise it's coming and I really do love statistics, it just took me a minute to find my way lol. Third year: Semester-long application project with supervisor in stats department, used CNNs for image classification Summer after third year: Summer research in an ML lab [received NSERC that funded this] (didn't really do a single concrete project with all the craziness going on w/ Covid, it was more numerous little projects) 4th year thesis: topic undetermined, likely reinforcement learning or something with a generative text network? Supervisor is in the stats department, and I imagine it will be more of an applied project rather than theoretical. TA experience for calc Letters of Recommendation: 2 letters of recommendation from profs I've worked with in math department, 1 from geography department (summer after second year) is the plan (I figured they could speak to my research ability/work ethic, and also I don't have another person to ask in the stats department) Where do I want to apply? (Note: for Canadian schools, plan is to apply to masters and then switch into a PhD (should I meet the criteria)/do a PhD after, for the US I am directly applying to PhD programs) For all I am applying to masters/PhD in statistics (is the plan as of now) - U of T (masters) - UBC (masters) - McGill (masters) - Harvard (I feel silly writing it, but there's a prof who is researching algorithmic fairness that I would be so grateful to work with/am super interested in. Any advice is extremely appreciated.) - UC Berkeley (again, there's a prof researching AI safety that aligns ridiculously well with my interests) - Stanford (masters) [again, interested in the research, but as far as I understand Stanford only offers a course-based masters so I wouldn't have a supervisor? I am thinking of applying to masters since they require the Math GRE and I don't currently have plans to write it, not sure if this is a mistake but I get overwhelmed trying to figure out how I would fit it in) If you've read this far, I can't thank you enough for your time. Any advice on how to strengthen my application/if I have a chance is greatly appreciated. If there are any courses you think I need to take to strengthen my statistics/math background, please let me know!! Any discrepancies/weaknesses I might want to address in a statement of purpose? Thank you kindly!!