BabaYaga

Glad the problem worked itself out. Still, I'd like to comment on 2 and 3. You should probably apply directly to PhD programs, if you know that that is actually what you want--also, admissions will be more sure of that too, if you have some research experience. There's plenty of instances where either a masters is considered (either automatically or by you asking) for applicants who don't make the PhD cut, so you may not be taking too much of a risk if you apply directly to PhD programs. You'll obviously want to check with the school for their policy on that. The 'better' schools will contain research groups that are publishing the kind of work you want to be doing in respected venues, placing alumni at the kind of institutions you want to eventually be employed at, and located in the environment you want to live in. I think a good starting point is to ask professors at your school who are familiar with your subfield to come up with a list of some of the research groups whose work they follow. From there, you can probably start ranking them in order of competitiveness (which could differ from the US News ranking system) and pick a few from the more selective, less selective, etc, schools, and eliminate places based on intangibles or overall reputation.

Yeah, Hopkins appears to be the preferred option if you want to do applied stuff intersecting with medical applications--and I find that attractive as well. Also, I think most would favor Hopkins over UT Austin if they want to do work in NLP and Speech recognition. Outside of that, I get the impression that research into novel AI and ML methods is more diverse if not stronger at UT Austin. Also, many of ML CS faculty at Hopkins seem to be just starting their careers or recently from industry. If I want to keep the academia door open, I wonder if it may not be better to find well-established research groups, of which their could be more of at UT. Regarding rankings: I didn't think about them much until I got acceptances and with so many intangibles to look at, it seems only natural to look for some quantitative way of comparing. I'm not even sure what is considered 1st tier, 2nd tier, and whose metric this uses. Thanks guys, I appreciate the thoughts!

I got an admit to UT Austin (CS, PhD) on the 12th. It's not after the deadline, but close enough for me to think that there could still be hope for you to hear something. If you already have an acceptance and an extension you might as well wait it out til the end.

Hi all, Between these two institutions, what would you choose? My interests are primarily in machine learning. Obviously, a good fit with a good research group should trump other factors, but suppose that's equal. Also, funding is less of an issue, as I'm coming in with an NSF Fellowship. I'm still considering what I want to aim for after graduation--but I'd like a PhD that maintains as many options as possible. Alumni from both universities have no apparent problem finding interesting industry positions, so I'm more concerned with keeping my options open for an academic career. Regarding rankings: US News ranks Hopkins #28 and Texas #8 (and #5 in AI). When, if ever, does such a difference in rankings matter? Texas is very keen on advertising this credential, while Hopkins likes to point out that such rankings are based on reputation and favor larger programs. On the other hand, I think Hopkins is more prestigious in general. Given Hopkins' repute as a medical institution, I gather there are lots of opportunities for meaningful collaboration, especially for people in ML, NLP and related fields. On the other hand, Texas with a much larger department has a lot of well established profs doing more fundamental research in these areas. Thanks for any thoughts!