optimization, theory, machine learning: UT Austin ECE vs Yale CS

[selfconcordance]

Hi everyone,

I am a student of mathematics and computer science interested in optimization, theoretical computer science, and machine learning. I would really appreciate some of your thoughts on 2 PhD programs I am deciding between -- ECE at UT Austin and CS at Yale. I am an international applicant and ideally wanted to visit both of these (very different) institutes before taking a call, but that just isn't possible now.

Thoughts on UT Austin

1. Strong group in ECE and CS departments.
2. Will I be able to work with faculty in CS? Will the large size of the institute prohibit me from doing so?
3. Austin seems like a cool city, but is somewhat isolated. Will I have to catch a flight to go anywhere?
4. Too many undergrads.
5. May face funding issues.
6. Growing safety concerns.
7. The size of the university probably allows for greater opportunities. I think I'll find like-minded people no matter if I want to start a company, enter industry, or stay in academia.
8. Not from an ECE background, so not sure how that will pan out.
9. Reputed within CS folks.
10. ECE probably won't let me indulge in theoretical CS.

Thoughts on Yale

1. Small(er) school. This is both a pro and a con. Not sure if I'll be able to find enough people with similar interests at Yale. However, the size will allow for more inter-departmental discussion?
2. Growing CS dept. (but fairly average at the moment)
3. Smacked between Boston and NYC.
4. Strong math department.
5. Funding situation should be good.
6. Mostly with an undergrad focus. Do grad students feel neglected?
7. New Haven safety issues.
8. Reputed in general.

Thanks in advance everyone!