insert_name_here

This is good advice.
I'd encourage you to apply to 1 or 2 "dream" schools, even if your odds aren't great. Mix that with some Canadian masters (where you'll have no trouble), and 5+ lower ranked schools, and you're set.
My personal approach would be to stay in Canada unless you get into a top (Berkeley/Harvard) program. Admission rates are ~3x lower for international students at top schools in the US, so you're likely to get into a worse school there than in Canada. Places like U of T are great, probably better than a 10-25 ranked PhD program in the US. Plus, America isn't the best place to live at the moment, especially compared to Canada (coming from a Canadian who moved to the US)

I think the top 5 stat programs are reaches for you, but it's definitely worth applying to a couple of them. I suspect you'll find more success in the 10-25 range. Your math background is solid, though McMaster is probably perceived by most as a little less prestigious than UBC/UofT/McGill/Waterloo. Great research experience, but unfortunately that can be a little hard for admissions committees to evaluate. The primary value of those experiences is that it hopefully allowed you to build strong connections with faculty who will write you glowing letters.
If you're set on going to a top-shelf PhD program, one approach might be to do a Masters at a top Canadian university, then re-apply. 

I think you can be a bit more aggressive in picking schools. If you're interested in US schools, I'd throw in an application at some top places that interest you (maybe Berkeley/UW/CMU). I wouldn't expect you to get into those places, but wouldn't be completely shocked either.

Being in state makes you cheaper for only one year (not the whole degree) vs out of state  (~$10-20k difference), so it may help a bit, but not a ton

This is bad advice. It's very unlikely an extra year would get you into Stanford (without blowing my anonymity, I can say this with high-confidence)
Berkeley/Stanford level schools will be somewhere between very unlikely and impossible (the fact you're international makes this much harder, unfortunately). 
I don't know as much about less competitive schools, but you are fairly strong, and I wouldn't be surprised if you got into some schools in the 5-15 range.
I wouldn't suggest taking a gap year, unless maybe you had something exceptionally cool to do for that year. COVID or not, life goes on...
I'd focus all your time on your research between now and application deadlines, that's what is likely to make the biggest difference.

Your profile could become much stronger than mine. But most of your hardcore classes as well as research have not come out in time. And you have not done math GRE which is another problem. Gap one year will get you into Stanford level assuming optimal performance in those 3 graduate series (real analysis, probability, stats theory), math GRE (90%+) and research (to the point where your supervisor finds impressive). If no gap, then it is really hard to say. But still, you could get into some solid schools even with no gap. It is just hard to say if you need to get to the Stanford level. So I would personally choose to gap if I can.

It is not satire and it is a collaboration effort. Actually they may have eventually resolved to buy like 10 of these and put them in the prof's office for safekeeping because it is much cheaper if you buy a bunch at a time.

I think you have an ok profile but you look like a pure math guy to me. Are you sure you want to do biostatistics? If I were you, I'd take more graduate level math courses and drop that applied PDE research for something more theoretical (such as abstract algebra which you seem to be really passionate about). Harvard, Princeton, MIT, Stanford, Caltech Cambridge, Brown all have top pure math programs. Why aren't you applying to pure math program at the schools you have mentioned? I really do think you have the potential to get into one of those and thrive there instead of settling for other things. You should also consider other top schools such as UCBerkeley, Columbia, UCLA for good measure. 

Yes that sounds very good. In this case, you should continue your research with the mentor and apply to statistics programs with a theory focus: Stanford (Dembo) for instance. Who is your advisor and what kind of research he works on? In general, you should be clear about what exactly you want to do and what it is. In general, you should go to a pure math program to get a top-notch education in theoretical research even though your intended field is probability. You should not apply to statistics because you think it is less competitive than pure math programs. 

ML is more like a buzz word. What is exactly the kind of thing you want to work with? Jianqin Fan is a top stats prof at Princeton. I think what he did now mostly classify as "ML". Tracy Ke, who is now a prof at harvard, was his student. I went to her lecture a while ago and it was quite packed because it was considered ML. But she worked on high dimensional lasso stuff which is very different from say CNN or other neural networks. To give you some perspective I took a "ML" class with Leslie Kaebling at MIT. She also specialized "ML". But what she did is RNN/MDP in robotics. This again is very different from what Jianqin Fan (as a statistician) is doing. It seems that your interest mostly lies in CS. Why aren't you apply to CS PhD in computer graphics? It is true that some students in stats do work in the kind of stuff you are interested in but not very prevalent.
 

Of course the OP has his eye on Stanford (especially with such a magnificent profile). And yes, a sure way to secure a spot there as a PhD is to attend their master program in statistics. Your chance is superb. Good luck.

I don't understand it. Are you applying for a second master after the first one? 

I think OP wants to move into "data science" which is hot now. But either way, I don't think Stanford will admit given there are only 8 admits GLOBALLY. I don't think your background is good for other programs either, at least not typical. UChicago - MACSS may be your best bet yet. If you did BA, then applying to an actual job (e.g. in a bank) should make more sense. 

Most data science programs are also professionally oriented. But there is lots of flexibility in the Harvard program, which I went. I successfully got into several good PhD programs this year. I would recommend the Harvard program if money and time is not a problem for you. But it is scary, really. You will be basically taking next 7-8 years of your life in order to do a PhD and you have already had a master--10 years in total! Are you married?

They like to admit people with interesting background. I myself specialized in Monte Carlo algorithm and they thought I would be an interesting addition to the cohort. There are people who did astronomy, fashion design, environment protection, Italian language and all sorts of things that can apply data science techniques in novel ways. I thought you have good chance given your background is kind of unique although it is not guaranteed--you should make case of how you would apply stuff such as neural network or other ML things to your specialty (i.e. BA).
Graduates from my cohort is highly successful which actually exceeds my expectation-- most of them went to Facebook, Google, Apple, Uber or top finance firms such as JP Morgan, Goldman Sachs, Mckinsey, Bridgewater etc., and several including myself remained at Harvard for PhD in CS or stats. I also got offers from Berkeley Duke etc. Now that I'm graduating, the outcome (both my own and my classmates) indeed exceeds my original expectation of what a master degree can achieve. Two years before I worked as an IT-type of guy in an unknown company maintaining spread sheet and emails etc. and never thought I would have progressed this far. This master degree really paid off (meanwhile it also almost bankrupts my family financially and put me in a hard spot for the future 5 years so you need to think twice if you are married). In the case where you have money, you should apply and do a master here. 

Harvard is committed to be open. No one knows anything other than that. We are all very worried. 

You should be in the conversation wherever you apply, I'd choose schools mostly based on research interests + fit. Schools like Ohio State and Penn State should be very, very strong safeties, I'd swap them out for some higher ranking schools like Columbia/Berkeley/UW/Duke.

You're overthinking it, just stick with ML. The difference between you having a research area you're excited+prepared for (ML), as opposed to not having a well-focused background is going to outweigh any of the effects you describe (if they even exist). FWIW, I've observed the opposite - lots of people put ML in their SOP and end up doing other things.

Don't put acknowledgements in your CV, maybe mention it briefly in your SOP if you did something interesting (if you have a letter from the professor involved it doesn't matter).
You should have a letter from the professor on the paper you're an author on - that's the important part. If it's online, e.g. arXiv, you should put a link. In your publications section, you can put "Paper title" (under review at Journal X)

If you were sick, had a death in the family, or a reason like that, mention if off-hand in <= one sentence. Otherwise, don't bother.

I don't think it matters what their degree is in. Especially if you're doing ML type work, practically the gap between a CS/applied stat/ML PhD can be very small/non-existent.

I graduated from Berkeley stats PhD - the courses are rigorous, but not crazy. I haven't really heard of PhD students dropping classes because they couldn't handle it.
We do have a pretty laid back set of requirements - no written qualifying exams, only an oral exam you take sometime between your second and fifth year which students never fail.
Anecdotally, I've heard Gelman is very difficult to work with (a Columbia PhD student volunteered that on my visit day)
Berkeley is really great, but I wouldn't stress too much - you can get a great education at any of those schools. Just find somewhere that you'll be happy!

In top 10 programs, I'd ballpark that ~10-15% of people drop out, so it is uncommon but by no means unheard of. PhD's aren't for everyone, and people get that.
Basically, if you're not happy and don't think that will change you should probably drop out. Otherwise, you'll end up in your late 20s, poor, with a mediocre PhD (if you don't enjoy it, it won't be great), and a ton of residual stress/anxiety to work through. A few caveats
- If you are far enough along - say 1, maybe 2, years from graduating, there may be an argument for gutting it out - there is a tangible benefit to graduating.
- If you don't get a PhD, you won't work in academia, so I wouldn't worry about letters of rec (side note - you should have a frank conversation about this with your advisor if you haven't already)
- If at all possible, you should do the work to get a masters. If you've stuck around for a year or so, I think most schools will give you one without too much work (maybe a couple extra courses).

I'm a Berkeley grad, but I won't reiterate the pros that have already been mentioned (size/quality of ML profs/postdocs, overall university quality, higher ranking). A few points:
You're worried that "you make a commitment early in the program as to your research area". Functionally, you're expected to find an advisor by the end of your second year (some people do take longer, but not recommended). Idk how CMU works, but 2 years is typically enough to explore. Some people commit as early as first semester if they want.
Berkeley is more expensive than Pittsburgh, but it's also an otherwise awesome place to live. Sunny, not too hot/cold, beautiful nature, spitting distance from SF, wine country, Yosemite, Big Sur. Plus fresh produce. I always hear that Pittsburgh is "nicer than you think", but still...

I would just apply for a PhD if I were you, you've got plenty of research experience, especially if you do indeed take+do well in the Berkeley PhD core courses 205A/B, 215A/B. Sounds like you'd have a shot at the (stats) PhD programs for those schools (Stanford may be a stretch), so getting in for a MS shouldn't be a problem.
I'd really just talk to the PhD students/professors you've been working with - they'll know how strong of a letter they will write for you, which noone else does.