Robbentheking

Some programs will not accept you if they think that you wouldn't accept their offer because it will affect their yield.

I wouldn't exactly say this is the case. You get paid a living wage to work very hard and grapple with complex ideas. Reading wiki, while sometimes is a supplement, is certainly not the main activity of a Ph.D. candidate in Statistics. 

I feel that there has been too much aggression and negativity. Technically, you are getting paid to learn stuff, which seems to me a pretty good deal. I used to work. At that time, I needed to do research and read paper after 7pm--I just wanted to sleep and I felt terrible. For my masters, I essentially pay tens of thousands to take same classes with PhD and do research with less support. What are we even complaining about here? I'd still do PhD if I were to go back to my old job after that.

Full disclose: I have not worked in industry as a data scientist and my opinion is just from word of mouth.
I think people that say that PhD's get the same data science jobs as MS students are not totally correct. Certainly this can be the case, but if you are motivated to work hard in your PhD and prove you have more research ability than being a package-calling coding monkey, my impression is that there definitely are roles out there for PhDs that are more 'interesting', and not just at the hyper-competitive facebook, google, etc. For example, I know from a friend that Grubhub has research teams that do different work than he does as an MS graduate. 
More importantly, I think it's certainly possible to enjoy a job like your current one further if you have a deeper understanding of the methods you're using, and still more importantly, I wouldn't care about losing out on 5 years of earning potential if you're fundamentally unhappy at the moment. 
Anyone who goes into a PhD thinking 'this is a waste of time if I don't get an academic job' is really putting themselves in a bad position. Academia is way too competitive for that. 

TTIC is simply excellent for AI/ML. Check out the papers accepted at NIPS, ML's biggest conference. A healthy representation from TTIC. I'd pick them over UCSD or USC for ML atleast.

It really would make more sense imo if people asked questions and had admissions related correspondence in the forum instead of on the results page. there's a lot of non-results there right now haha

Yes, the whole CMU "scammer" speculation and checking of link is very annoying in the results page and can easily be moved to the forum >.>

I just feel bad for the people perusing these results the next year lol

It really would make more sense imo if people asked questions and had admissions related correspondence in the forum instead of on the results page. there's a lot of non-results there right now haha

@StressedOutUser - Bold move there to only apply for one school for PhD. Most top CS schools admit rates are <10% and UW is definitely up there, especially depending on what subfield you want to go for.

I've done a good amount of research on masters programs. It seemed to me like Chicago was the best for PhD preparation in the country, hands down. I think Harvard and Stanford may be better for industry, both based on name recognition and the focus of the programs. Stanford I recall labels their program as a 'terminal masters' and specifically stresses that it is rare for masters students to obtain admission to the PhD program. Chicago seemed a lot more flexible/theory orientated. They've also taken a couple kids from the masters program as PhD candidates the past few years. Just a couple of data points.
I think Harvard was only a year long program? That probably isn't too helpful for you, as won't you application won't have substantially changed by next fall. Think about recommendations...you can't expect someone to write you a great rec after being in their class for 2 months.
To be honest though, I don't know anything about theoretical machine learning. Maybe you should be in a certain place just based on that interest. If it's going to be CS heavy, maybe you should be looking at school with a good CS department as well? Chicago doesn't really fit the bill there, as far as I know.

I think so. If you barely prepared for the test, you should not too painfully be able to increase the quant a few valuable points. 
Getting into a PhD at "top schools" may be challenging depending on your definition of top schools, but I think you have a place somewhere for sure. 

u sure a phd in stat is what you really need for that type of a job?

I definitely think the OP can get into a top-10 stats program as-is. Top-5 public schools include Berkeley, UCLA, Michigan, UNC, and UVA, all of which have good math departments. Secondly, ODEs isn't even really necessary for statistics. I would wager the majority of students applying to master's degrees don't have a lot of math courses under their belt, which is why they're going for Master's than PhD. I seriously doubt the majority of students applying for master's degrees have taken Analysis I and this person has taken Analysis II (getting an A) and Complex.
I wouldn't waste time taking the math GRE. I'm pretty confident you can get into a top-10, but apply to the 11-20 range to be safe.

Of course it's overkill.  OP asked if it would give him a bonus, the answer is yes (I'm sure you'll agree).  Whether he can spare the time is his decision.

Duke is located close to NC's Research Triangle Park region, which has companies like IBM and Cisco. As for the other two, which employer wouldn't want a more serious degree?

Well, my general impression of quantitative finance (disclaimer: I have't worked in it), is that if you want to just jump from theoretical math/statistics to getting hired, you need to be a beast at your subfield. Otherwise, I think it's much better to be able to get shit done (read: be good at programming). 
What is the approximate rank of your undergrad institution? what textbooks did you use for analysis/algebra? what did you cover? I agree that you seem to be better suited to statistics departments than CS departments, but just know that there will be people with stronger math backgrounds as you applying, especially at somewhere like Chicago. 

Based on what I know, probably Computer Science is more suitable for both "research in machine learning ... developing strategies for automated stock trading" and "engineering with some pure mathematics (background)" than statistics. If you are aiming for a job in private sectors, they would evaluate you more on "what you can do" rather than "what is your major". And if you search positions posted on glassdoor or linkedin, actually employers don't care whether you are stat PhD or cs PhD or ECE PhD (and I know a couple of ECE and physics PhDs working in Wall Street or HF in Boston now).
Another practical issue is that most professors in statistics departments are not focusing on financial statistics (which is why I had a hard time searching for potential supervisors). And as far as I know, among these professors, not many of them are implementing machine learning techniques, and vice versa.

It's like Christmas morning when I come on here to a bunch of Cyberwulf laying down his wisdom

If you check the admission profile for UW-Madison's stat department, you will find that for the past few years there were around 250 applicants. They admitted about 80 each year, while only less than a quarter of them will enroll.
https://www.gradsch.wisc.edu/webextras/education/academicprograms/profiles/949PHD.pdf
 
So I guess it is quite common for them to send offers without funding?

If you haven't aready, it may help to ask this same question and provide the same information on the forums at:
wilmot.com
quantstart.com
quantnet.com

Weekends are the worst. 

What even is this process? Seems like a strange way of tackling 300+ applicants 

I am the 2nd Michigan acceptance. I got a personalized email from the graduate chair, Stilian Stoev. The acceptance hasn't shown on ApplyWeb or Wolverine Access yet. Hope that helps. 

I've done a good amount of research on masters programs. It seemed to me like Chicago was the best for PhD preparation in the country, hands down. I think Harvard and Stanford may be better for industry, both based on name recognition and the focus of the programs. Stanford I recall labels their program as a 'terminal masters' and specifically stresses that it is rare for masters students to obtain admission to the PhD program. Chicago seemed a lot more flexible/theory orientated. They've also taken a couple kids from the masters program as PhD candidates the past few years. Just a couple of data points.
I think Harvard was only a year long program? That probably isn't too helpful for you, as won't you application won't have substantially changed by next fall. Think about recommendations...you can't expect someone to write you a great rec after being in their class for 2 months.
To be honest though, I don't know anything about theoretical machine learning. Maybe you should be in a certain place just based on that interest. If it's going to be CS heavy, maybe you should be looking at school with a good CS department as well? Chicago doesn't really fit the bill there, as far as I know.

Dude I know. Maybe more applications than before? Hoping that's not the case