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UT Austin vs Johns Hopkins for Machine Learning PhD


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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!



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Personally, I would choose UTA. There are two really good faculty there in my area (data mining). There actually isn't a good reason I didn't apply there to work with the IDEAL lab. I have had some contact with the professor before as a Master's student. JHU has never been on the map for me, but it might depend on the area. I would take a look at IDEAL and DML labs there. Really good faculty (personally, as well).


I think it's such a personal choice, and there are a lot more important factors to consider than ranking. I dont think the exact ranking matters much, but it is important to be in at least the second tier schools (i.e. top-100 in US). In top conferences you'll be interacting with people mostly from these places. As for myself, there isn't anyone I follow at an MIT or Berkeley, so those rankings are meaningless.

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I'm not sure what you're interested in, but if you're interested in Computer Integrated Surgery at all, Hopkins is the way to go.  This is partially because Johns Hopkins is so strong in medical research and there are opportunities for collaboration with the medical school, but also just because the professor who does it is pretty amazing (Russell Taylor).  


I'm not a Computer Scientist, and can't really comment much about the programs in general. Good luck making a decision!

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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!

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