shuggie

I plan to hook an old wacom tablet up to my computer; looks like a new one of these goes for about 50 bucks. An ipad seems way too much if you just want to draw stuff with a pen. Do a search for "drawing tablet".

Personally, if you are dead set on Causal Inference I think McGill would be the best fit. Otherwise I think UBC has plenty of research areas to pick from and I think that would also be an excellent choice

To a large extent, this is true, but I want to be very clear about some terminology here which most people only become very familiar with after being in a graduate program, so if you're already aware of this distinction, I don't mean to be talking down.  There are broadly three types of research, applied, methodological, and theoretical.
Applied research is exactly what it sounds like - you are analyzing some data set and want to answer a scientific question.  There is more opportunity to do this type of research in a biostatistics program because there are so many biomedical collaborations.  You might be funded by working on applied research.  But nobody receives a PhD in statistics because they just did applied research.
The majority of what would be considered statistics research is methodological, creating or slightly extending methods to apply to new types of data or in unique situations.  There will probably be some application in the paper, but the focus is on the method, how it performs, etc.  Some of these papers are very "theoretical" in the laymen's sense that they use a lot of math and have some proofs. Scroll through the following papers, quickly look at the math, and decide if this is theoretical enough for you: 
https://arxiv.org/pdf/1910.04291.pdf
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5560424/
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3285536/
This type of research is done in all the top biostatistics departments.  Even biostatistics programs ranked 15th have people doing stuff like this.  And most people in statistics programs are doing research exactly like this too - this is where the large area of overlap between statistics and biostatistics comes from.
When people are talking about theoretical statistics, they are generally talking about *extremely* theoretical, math-heavy stuff you would find in a journal like Annals of Statistics.  Compare the above papers to the two below:
https://arxiv.org/pdf/1705.07382.pdf
https://arxiv.org/pdf/2003.05599.pdf
You will, generally, not find much research like the above in a biostatistics department, outside of 1 or 2 professors at top departments.  Most professors in stats departments are not even doing research like this.
In summary, think of how theoretical of research you really want to do.  If the first 3 papers roughly fit that, a biostatistics department will be fine.  In my experience, the overwhelming majority of students, and especially domestic students, do not want to work on the type of extremely theoretical problems anyways.

If you are interested in machine learning/high-dimensional statistics, don't overlook some of the top biostatistics programs. For example, University of Washington Biostatistics has some excellent faculty for this, e.g. Witten and Shojaie. PhD graduates from good Biostatistics PhD programs that emphasize both theory and clinical applications are able to get jobs in traditional statistics departments (for example, Edward Kennedy at CMU Statistics has a PhD in Biostatistics and publishes a lot in the top stat journals).

Oh yeah I had two semesters of analysis when I went through Kreszig. I met some impressive students who jumped into that with only calculus and linear, but they were very adept learners. Also I can't imagine that many stat students would need to actually take a dedicated course in functional ever. Some will, and personally it's one of my favorite books, but not a pre req for any programs I looked at (and imagine most don't).

Linear algebra is the big thing. These are the topics on the Linear Algebra bootcamp for UNC biostat:
1. Vector Spaces
2. Subspaces
3. Matrices
4. Matrix Properties
5. Matrix Decompositions
6. Projections
7. Vector/Matrix Differentiation
Also this is definitely opinionated but I don't think Rudin is that great for self study, obviously YMMV but we used it in my Real Analysis class this year and without the help of my prof. some of the proofs are rather...obtuse and the intuition is not super clear at times.

Yes, I would expect that there will be fewer funded (mostly Ph.D.) positions available for Fall 2021, but there may not be a major impact on the availability of unfunded (mostly Masters) spots. Indeed, programs might try to expand their Masters programs to try to make up some of the financial shortfall.
The folks who are likely to suffer the most are current graduate students approaching graduation; the job market over the next 1-2 years could look pretty bleak.

I applied to Wake Forest this cycle and had some interaction with the department. They were easily one of the best departments I spoke with, very nice, and clearly have their students in mind. If they had a PhD program, I would have attended in a heartbeat. If you are uncertain about doing a PhD I think the program at WFU is a better option. Being a terminal MS program you might get more support their than at UMN, but don't quote me on that. Also the statistics faculty at WFU is growing and seems pretty diverse for a joint Math/Stat department. I was told there were some sparse RA opportunities in interdisciplinary areas like econ and public health. You'll also have the opportunity to take graduate math courses like Measure Theory and Functional Analysis.
I went to school in a moderately sized metropolitan area and studied in Budapest for a semester. In my experience there are definitely pros to studying in a dense urban area, but it's far more important to make sure you have strong work environment on campus and at home. When the going gets tough, where you live doesn't matter as much.

Were any of the people here in grad school during the 2008 crash? I'd heard anecdotal stories of students being asked to self-fund, and am curious is there truth to that. More relevantly, if those concerns are applicable amid the crisis now.

This outbreak is affecting the entire world, and schools are having to make decisions like this. Adcoms are going to be more than aware of the disruption this causes for all students. Given the prestige of your university and your already excellent background, you have nothing to worry about.

Since the application season is nearing the end for PhD applicants, are people interested in creating a results thread? I created last year's thread, and if there's enough interest I can go ahead and create this year's.

I think you will likely be rejected by all of these schools unfortunately. Your background is simply not very good. Also you are basically throwing your money into water by applying to UofT because they rarely admit international student without personal connection. 

I'm not exactly sure about biostatistics jobs. But in finance, school names carry great weight for master/undergrad level jobs. This is called "target schools". If you are not from "target schools", your resume is placed into trash unless you have some connections in the firm. Even though people laugh at Columbia's master program as a "cash cow" but when I interviewed at banks in NYC, there are always severals students come from there. Some of those guys barely speak English and I suspect they are very good. But they are on equal footing with someone from Stanford, for example, when they got on-site interviews by Columbia name. On the contrary, I rarely see anyone from unknown state schools. It requires a great amount of networking to even get an interview if you go to Pittsburgh, which Columbia name will automatically grant you.

@shuggie I'd try to figure out availability of advisors (is there competition to work with certain professors?). Figure out any questions about funding you might still have, like whether you'll be funded in summers and what type of travel support there is.
I'd also try to get a gauge of the sense of community in the department and whether people seem happy.  Keep in mind they'll likely be showing you the happiest students on your visit and not the disgruntled ones.  Your first two years will basically be coursework.  Do people work together and seem to be friends with their fellow students?  Are people stressed about qualifying exams all the time? 
I'd look at coursework/quals in general a little bit, not so much for their content matter, but for stress/time purposes.  Some departments have 2 years of qualifying exams and 3 years of coursework, some places have 1 year of courses and no quals - big difference. Ask about qualifying exam passing rates.  If you are confident in your math abilities , it shouldn't be a huge issue, but some departments have pretty theoretical qualifying exams that weed out quite a few (usually domestic) students who are weaker at math. 
I would also get a good look at the facilities.  You'll be spending a lot of time in the department's buildings, and it can get depressing if you go somewhere without windows for 10 hours a day.

This is something I've heard a lot and I'm curious if this sentiment is more to emphasize that you can't build your research from coursework,  or if it's a commentary on the role of coursework in a PhD program. With that, what are the intended purposes of coursework in a PhD program? Would you ever advise choosing one program over another based on course offerings (holding all else roughly equal)?  
 
My impression is that the classes you take give you some more interaction with select faculty, inform your areas of interest, provide a level of basic/vocational training for when you are working on research, and they can be a lot of fun. I understand that coursework is not the purpose of a PhD, but sometimes I get the impression that people feel like it was of little importance to their PhD experience as a whole.