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I'm a first-year PhD student in Machine Learning, and I've often heard that some students end up having to be a TA because his/her advisor does not have a sufficient funding for them to have a research assistantship. Generally, how important is the availability of funding of a professor as a factor to determine one's advisor? I understand the most important factor is the relevance of the professor to one's research, but then there are multiple choices left, so I'd like to know the answer.

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Pretty important, depending on the field.

In some fields (e.g. humanities), it is standard that professors will have little funding for RAs, while in others (e.g. computer science) it is expected that they can fund all of their students through RAs. It depends on the availability of funding for that field. So one important caveat is that you should compare the professor’s funding situation with the alternatives, namely other professors in the department.

Time you spend TAing is time you don’t spend on research, which is the main purpose of a PhD. A couple of semesters of TAing is good experience, but, if you are constantly TAing due to lack of funding, it could hold you back, relative to other students.

You should also wonder why this professor can’t get funding. It may be a sign of deeper issues. It might signal that the professor’s research is not going well, in which case being their student is a bad idea. In particular, they might be denied tenure (i.e. fired) and their students will be left advisorless.

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  • Thank you very much for your answer. I didn't know that funding was that important. – J. Doe Feb 14 '19 at 4:42
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    Is this view of TAing common in machine learning? When I was a graduate student in math, I was a TA every semester - just like every other graduate student in my department - and don't feel that it affected me negatively. – Misha Lavrov Feb 14 '19 at 5:03
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    @MishaLavrov I suppose there is a huge difference btw pure math PhD and ML PhD in terms of the type of students they currently attract. The former type tends to want to go to academia, whereas the latter to industry, since there is plethora of good industry labs. You like to teach so much that you even taught high school students at math camp (including myself) during summer, which I'm very thankful for. But from my observation, most people in ML, including myself, has the view expressed by Thomas as above. – J. Doe Feb 14 '19 at 5:19
  • @J.Doe Thank you for the explanation (and for the flattery). – Misha Lavrov Feb 14 '19 at 5:28
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    There is one upside to having to teach, though. One finally learns to explain to another human being what one's research is about,... maybe. That can also help in interviews. In general, I would find access to interesting datasets more important than money. – HRSE Feb 14 '19 at 8:16

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